David Abramson has been involved in computer architecture and high performance computing research since 1979. Previous to joining the University of Queensland in 2013, he has held appointments at Monash University, Griffith University, CSIRO, and RMIT. At CSIRO, he was the program leader of the Division of Information Technology High Performance Computing Program and was also an adjunct associate professor at RMIT in Melbourne. He served as a program manager and chief investigator in the Co-operative Research Centre for Intelligent Decisions Systems and the Co-operative Research Centre for Enterprise Distributed Systems. At Monash, he served as the Science Director of the Monash e-Research Centre and Director of the Monash e-Education Centre. He is currently the director of the UQ Centre for Research Computing and an adjunct professor in the School of IT and EE. He is a fellow of the ACM, the IEEE, the ATSE, and the ACS.

Dr. Agarwal is a Senior Scientist and the Data Science and Technology Department (http://dst.lbl.gov), Head at Lawrence Berkeley National Laboratory (LBNL). Dr. Agarwal’s current research focuses on developing computational tools to enable scientists to more effectively organize and use their data to address challenges. She has worked on projects involving watershed understanding, tropical forests, soil carbon, carbon capture, cosmology, particle accelerators, and satellite data. Dr. Agarwal earned her BS in Mechanical Engineering from Purdue University. Her MS and PhD are from University of California, Santa Barbara in Computer Engineering.

Sadaf R. Alam is Chief Technology Officer at the Swiss National Supercomputing Centre (CSCS) in Lugano, Switzerland. Dr. Alam studied computer science at the University of Edinburgh, UK, where she received her Ph.D. in 2004. Until March 2009 she was a computer scientist at the Oak Ridge National Laboratory, USA. In her role as the CTO, she ensures end-to-end integrity of HPC systems and storage solutions and leads strategic projects at the centre.

Srinivas Aluru is a professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He co-directs the Georgia Tech Interdisciplinary Research Institute in Data Engineering and Science (IDEaS), and co-leads the NSF South Big Data Regional Innovation Hub which serves 16 Southern States in the U.S. and Washington D.C. Earlier, he held faculty positions at Iowa State University, IIT Bombay, New Mexico State University, and Syracuse University. Aluru conducts research in high performance computing, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. He is currently the Chair of ACM SIGBIO. He is a recipient of the NSF Career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, and the Outstanding Senior Faculty Research award and the Dean’s award for faculty excellence at Georgia Tech. He received the IEEE Computer Society meritorious service award, and is a Fellow of the AAAS and IEEE.

Dr. Abdelhalim Amer holds an Assistant Computer Scientist appointment at Argonne National Laboratory. His research falls under the parallel and distributed computing landscape. More specifically, he focuses on parallel runtime systems and programming models targeting applications in massively parallel environments. This includes tackling scalability challenges in shared-memory as well as distributed-memory parallel environments. Dr. Amer is a co-PI of two projects and authored several papers in this research area and presented numerous talks in conferences and institutions.

Georgios Andreadis is a B.Sc. student at the Delft University of Technology, the Netherlands. His Honours-track work focuses on datacenter scheduling. He is also the tech lead of the OpenDC team. Contact him at G.Andreadis@atlarge-research.com.

Toshikazu Aoyama is an assistant manager of AI Platform Division at NEC Corporation. His responsibility at NEC is to develop system software and to develop AI/Bigdata analytics business strategy for SX-Aurora TSUBASA.

Pavan Balaji is a Computer Scientist and Group Lead at the Argonne National Laboratory, an Institute Fellow of the Northwestern-Argonne Institute of Science and Engineering at Northwestern University, and a Research Fellow of the Computation Institute at the University of Chicago. He leads the Programming Models and Runtime Systems group at Argonne. His research interests include parallel programming models and runtime systems for communication and I/O on extreme-scale supercomputing systems, modern system architecture, cloud computing systems, data-intensive computing, and big-data sciences. Pavan is the Technical Program Chair for Supercomputing 2019.

Debbie Bard leads the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. Debbie’s work at NERSC focuses on data-intensive supercomputing, including machine learning at scale.

Mehmet Esat Belviranli, Ph.D., is a Postdoctoral Research Associate at Oak Ridge National Laboratory (ORNL). At ORNL, he is a member of the Future Technologies Group in the Computer Science and Mathematics Division. He received his Ph.D. degree in computer science from University of California, Riverside. He served as a program committee member and external reviewer for various conferences, journals, and research proposals. His research is mainly focused on high performance computing and systems. His studies have been published in MICRO, PPoPP, SC, ICS, PACT and TACO on various topics such as heterogeneous architectures, runtime systems, task-based execution, performance modeling, NVMs, deep memory hierarchies, source-to-source translation, systems research for deep learning, and parallel programming paradigms.

Abhinav Bhatele is a computer scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research interests include performance optimizations through analysis and visualization, task mapping and load balancing, network design and simulation, parallel runtimes and interoperation, and HPC data analytics. Abhinav received a B.Tech. degree in Computer Science and Engineering from I.I.T. Kanpur, India in May 2005 and M.S. and Ph.D. degrees in Computer Science from the University of Illinois at Urbana-Champaign in 2007 and 2010 respectively. Abhinav was a recipient of the ACM/IEEE-CS George Michael Memorial HPC Fellowship in 2009 and the IEEE TCSC Young Achievers in Scalable Computing award in 2014. He has received best paper awards at Euro-Par 2009, IPDPS 2013 and IPDPS 2016.

Matthias Blumrich is a Senior Research Scientist at NVIDIA, working in the Network Research Group. He is primarily interested in all aspects of large-scale, high-performance network architecture and design, ranging from components to entire systems. Prior to joining NVIDIA, he was a member of the core architecture and design team for the three generations of Blue Gene supercomputers at IBM Research. Dr. Blumrich holds a B.E.E. degree from Stony Brook University, and M.A. and Ph.D. degrees in Computer Science from Princeton University.

Aydın Buluç is a Staff Scientist at the Computational Research Division of Berkeley Lab, working on high-performance graph analysis, libraries, and their applications in genomics and bioinformatics. His current research interests also include parallel sparse matrix computations and communication-avoiding algorithms. Previously, he was a Luis W. Alvarez Fellow. He earned his doctorate in computer science from the University of California at Santa Barbara in 2010. He is a recipient of the DOE Early Career Award in 2013 and the IEEE TCSC Award for Excellence for Early Career Researchers in 2015.

Suren Byna is a Staff Scientist in the Scientific Data Management (SDM) Group in the Computational Research Division (CRD) at Lawrence Berkeley National Lab (LBNL). His research interests are in scalable scientific data management. More specifically, he works on optimizing parallel I/O and on developing systems for managing scientific data. He is the PI of the ECP funded ExaHDF5 project, and ASCR funded object-centric data management systems (Proactive Data Containers - PDC) and experimental and observational data management (EOD-HDF5) projects.

Phil Carns is a principal software development specialist in the Mathematics and Computer Science Division of Argonne National Laboratory. He is also an adjunct associate professor of electrical and computer engineering at Clemson University and a fellow of the Northwestern-Argonne Institute for Science and Engineering. He received his Ph.D. in computer engineering from Clemson University in 2005. Phil's research interests include characterization of I/O patterns, simulation of large-scale storage systems, and design of high-performance system software.

Sourav Chakraborty is a PhD student at the department of Computer Science and Engineering at The Ohio State University. Their research interests include High performance networks, Fault tolerance, Scalable distributed sytems, RDMA, and other aspects of High performance computing in general. He has been working in the Network Based Computing Laboratory (NOWLAB) with Prof. D K Panda since 2013. He has published in multiple international conferences and contributed to various open source software including MVAPICH2 and SLURM. Sourav earned his Bachelor's from Jadavpur University in 2011. Before joining OSU, he worked for Yahoo! as a software developer. He has also worked at the Lawrence Livermore National Laboratory as a research intern.

Bingwei Chen is a PhD student from the department of computer science and technology in Tsinghua University. His research interests include algorithm design for real world’s applications such as seismic modeling, full waveform inversion and climate modeling on heterogeneous computing platforms. The related papers have won honors such as the Gordon Bell Prize of SC17.

Wenguang Chen is a professor in Department of Computer Science and Technology, Tsinghua University. His research interest is in parallel and distributed systems and programming systems. He received the Bachelor’s and Ph.D. degrees in computer science from Tsinghua University in 1995 and 2000 respectively. Before joining Tsinghua in 2003, he was the CTO of Opportunity International Inc. He was appointed as the associate head of Department of Computer Science and Technology from 2007 to 2014.

Xiaofei Chen is a professor working in Department of Earth and Space Sciences, Southern University of Science and Technology. His main research interests are theoretical and computational geophysics and its applications in earthquake prevention and disaster mitigation and resource exploration.

Kazem Cheshmi is a PhD candidate at the University of Toronto. His research focus is domain-specific compiler design and parallel computing approaches for numerical methods. Kazem is a recipient of Adobe fellowship 2018, the grand final award of the 2017 ACM SRC competitions and CGO-SRC 2017. He received his M.A.Sc degree from the University of Tehran in 2013 in computer engineering. Before joining the University of Toronto, Kazem was a researcher at Rutgers University, US, Concordia University, Canada and Rostock University, Germany.

Clara Cromey is a Software Engineer at Raytheon Missile Systems. She holds a M.S. in Computer Science and a B.A. in Mathematics, both from the University of Arizona. While there, she researched network contention and job scheduling in HPC machines under Dr. David Lowenthal.

Maarten de Hoop is the Simons Chair in Computational and Applied Mathematics and Earth Science at Rice University, USA. He is also a visiting faculty member at MIT and the Graduate University of Chinese Academy of Sciences and has been on the faculty of Purdue University and Colorado School of Mines. He received his Ph.D. in technical sciences from Delft University of Technology in the Netherlands in 1992. His research interests are in inverse problems, microlocal analysis and computation, and applications in exploration and global seismology and geodynamics. Over the last 20 years, de Hoop has received significant research support from the energy industry. At Purdue, de Hoop founded the Geo-Mathematical Imaging Group, an industry-university consortium project. He received the J. Clarence Karcher Award from the Society of Exploration Geophysicists and has been a fellow of the Institute of Physics since 2001.

As Chief Technology Officer (CTO) for Livermore Computing (LC) at Lawrence Livermore National Laboratory (LLNL), Bronis R. de Supinski formulates LLNL's large-scale computing strategy and oversees its implementation. His frequently interacts with supercomputing leaders and oversees many collaborations with industry and academia. Previously, Bronis led several research projects in LLNL's Center for Applied Scientific Computing. He earned his Ph.D. in Computer Science from the University of Virginia in 1998 and he joined LLNL in July 1998. In addition to his work with LLNL, Bronis is also a Professor of Exascale Computing at Queen's University of Belfast and an Adjunct Associate Professor in the Department of Computer Science and Engineering at Texas A&M University. Throughout his career, Bronis has won several awards, including the prestigious Gordon Bell Prize in 2005 and 2006, as well as an R&D 100 for his leadership in the development of a novel scalable debugging tool.

Los Alamos National Laboratory

Gökalp Demirci is a PhD student at the University of Chicago, advised by Janos Simon. He completed BS and MS degrees in Computer Engineering at Boğaziçi University in Turkey. His research interests are in theoretical computer science and his thesis focuses on approximation algorithms for combinatorial optimization problems such as various scheduling and clustering problems.

Larry Dennison is Director of Network Research at NVIDIA. Prior to NVIDIA, he worked on software systems such as high-performance distributed applications, database scaling for the cloud and software-defined networking. He co-founded Avici Systems, where he also architected and led the development of the ASIC chipset for the Avici Terabit Router which utilized a 3-D toroidal network. At BBN, Dr. Dennison was the principal investigator for MicroPathfinder, a wearable computer that connected to other wearables over a very low power RF network. Dr. Dennison holds Ph.D., M.S., and B.S. degrees from the Massachusetts Institute of Technology.

Larry Dennison is Director of Network Research at NVIDIA. Prior to NVIDIA, he worked on software systems such as high-performance distributed applications, database scaling for the cloud and software-defined networking. He co-founded Avici Systems, where he also architected and led the development of the ASIC chipset for the Avici Terabit Router which utilized a 3-D toroidal network. At BBN, Dr. Dennison was the principal investigator for MicroPathfinder, a wearable computer that connected to other wearables over a very low power RF network. Dr. Dennison holds Ph.D., M.S., and B.S. degrees from the Massachusetts Institute of Technology

Luiz DeRose is a senior principal engineer and the programming environments director at Cray Inc, where he is responsible for the programming environment strategy for all Cray systems. Before joining Cray in 2004, he was a research staff member and the tools group leader at the Advanced Computing Technology Center at IBM Research. He has a PhD in Computer Science from the University of Illinois at Urbana-Champaign. His research has been primarily on topics of compilers and tools for high performance computing. With more than 25 years of high performance computing experience and a deep knowledge of its programming environments, he has been responsible for the design and development of many performance tools that have had significant technological impact in academia and industry, such as FALCON, SIGMA, the HPM Toolkit, the CrayPat Performance Collector, the Cray Apprentice2 Performance Analyzer, and the Cray Comparative Debugger.

Jack Deslippe is the application performance group lead at NERSC. Jack and his group are partnering with DOE application teams to evaluate and improve the performance of applications on the Cori Knights-Landing based system at NERSC as well exploring and influencing performance portability strategies. He received a Ph.D. from UC Berkeley in physics in 2011, with research centered on computational materials physics and nano-science, including the development and scaling of electronic-structure codes. Jack has been at NERSC since 2011, acting as a consultant and developer for materials science applications, the MyNERSC architect and currently leads the NERSC Exascale Science Applications Program (NESAP).

Minh Ngoc Dinh is a research fellow at the Research Computing Centre, the University of Queensland, Australia. He was awarded his Doctor of Philosophy Degree in Computer Science from Monash University, Australia, in 2013. His PhD thesis emphasizes the use of debug-time assertions for debugging large-scale parallel programs. He has also been involved in research projects including high throughput Grid-based environment for real-time biomedical imaging and scalable debugging tool for high performance computing platforms.

Jens Domke is a postdoctoral researcher at the Global Scientific Information and Computing Center, which hosts the TSUBAME3 supercomputer for the Tokyo Institute of Technology, Japan. He received his doctoral degree from the Technische Universität Dresden, Germany, in 2017 for his work on HPC routing algorithms and interconnects. Jens started his career in HPC in 2008, after he and a team of five students of the TU Dresden and Indiana University, won the Student Cluster Competition at SC '08. Since then, he published several peer-reviewed journal and conference articles. Jens contributed the DFSSSP and Nue routing algorithms to the subnet manager of InfiniBand. His research focus is on interconnects, topologies, and routing algorithms for HPC systems. Furthermore, he is interested in SDN networks, scheduling algorithms for parallel architectures, and performance evaluation and optimization of parallel applications.

Jack Dongarra holds appointments at the University of Tennessee, Oak Ridge National Laboratory, and the University of Manchester. He specializes in numerical algorithms in linear algebra, parallel computing, use of advanced-computer architectures, programming methodology, and tools for parallel computers. He was awarded the IEEE Sid Fernbach Award in 2004; in 2008 he was the recipient of the first IEEE Medal of Excellence in Scalable Computing; in 2010 he was the first recipient of the SIAM Special Interest Group on Supercomputing's award for Career Achievement; in 2011 he was the recipient of the IEEE Charles Babbage Award; and in 2013 he received the ACM/IEEE Ken Kennedy Award. He is a Fellow of the AAAS, ACM, IEEE, and SIAM and a foreign member of the Russian Academy of Science and a member of the US National Academy of Engineering.

Xiaohui Duan is currently a Ph.D. candidate in the School of Software Engineering, Shandong University, Jinan, China. He won a gold medal in the 2011 ACM-ICPC Asia Fuzhou Regional Contest. He received his Bachelor's degree in Computer Science and Technology in 2015. His research interests include high-performance computing, heterogeneous architectures, and parallel algorithm design. The work of redesigning CAM-SE based on Sunway TaihuLight make him a Gordon Bell prize finalist in 2017.

Hans Eberle is a Senior Research Scientist in the Network Research Group at NVIDIA Research. Previously, he held positions at Oracle Labs, Sun Labs, ETH Zurich, and at the DEC Systems Research Center. He holds a Doctorate in Technical Sciences, a Diploma in Electrical Engineering, both from ETH Zurich, and an MBA in Sustainable Management from the Presidio Graduate School in San Francisco.

Mark Endrei is a PhD Candidate at the University of Queensland, Australia. He also has more than 20 years of experience in IT industry, working with large corporations both nationally and internationally. He has a Bachelor of Engineering Degree (H1) in Computer Systems Engineering from RMIT University.

Massimiliano Fatica is the director in the High Performance Computing and Benchmarks team at Nvidia. He received a Degree in Aeronautical Engineering and a PhD in Theoretical and Applied Mechanics, both from the Sapienza University of Rome, Italy. His research interests are in computational fluid dynamics and parallel and high performance computing. He has been a finalist in the ACM Gordon Bell prize multiple times and received an honorable mention in 2011.

S M Ferdous is a PhD student in Computer Science at Purdue University. He is working with Prof. Alex Pothen. His research interest includes parallel graph algorithms, high performance computing and computational biology. He has completed his Bachelor and Masters in Computer Science and Engineering from Bangladesh University of Engineering and Technology.

Haohuan Fu is the deputy director of the National Supercomputing Center in Wuxi, leading the R&D division. He is also an associate professor in the Ministry of Education Key Laboratory for Earth System Modeling, and Department of Earth System Science in Tsinghua University, where he leads the research group of High Performance Geo-Computing (HPGC). Fu has a PhD in computing from Imperial College London. Since joining Tsinghua in 2011, Dr. Fu has been working towards the goal of providing both the most efficient simulation platforms and the most intelligent data management and analysis platforms for geoscience applications. His research has, for example, led to efficient designs of atmospheric dynamic solvers for both Tianhe-1A, Tianhe-2, Sunway TaihuLight supercomputers, and the reconfigurable computing platforms. His works have won the Gordon Bell Prize 2016 and 2017.

Lin Gan is the assisstant researcher in the Department of Computer Science at Tsinghua University, and assistant director of the National Supercomputing Center in Wuxi. His research interests include high-performance-computing solutions to geo-science applications based on hybrid platforms such as CPUs, FPGAs, GPUs, and the Sunway TaihuLight system. Gan received a PhD in computer science from Tsinghua University. He has received the 2016 ACM Gordon Bell Prize, 2017 ACM GordonBell Prize Finalist, Tsinghua-Inspur Computational Geosciences Youth Talent Award, and the FPL2015 Most Significant Paper Award, etc.

Ping Gao received the Bachelor's degree from Qingdao University, China. She is currently a Ph.D. candidate in the School of Software Engineering at Shandong University. Her research interests include high-performance computing in molecular dynamics and bioinformatics, performance optimization.

Evangelos Georganas is a Research Scientist at Intel’s Parallel Computing Lab in Santa Clara, California since December 2016. Before joining Intel, he was an interim Postdoctoral Fellow in the Computational Research Division at the Lawrence Berkeley National Laboratory. He obtained his Ph.D. from UC Berkeley in Computer Science where he was advised by Prof. Katherine Yelick. He was affiliated with Berkeley Benchmarking and Optimization group and the ClaSS Group at Lawrence Berkeley National Laboratory. His research interests include High Performance Computing, Scientific Applications, Deep Learning and Computational Biology. His Ph.D. thesis was on scalable parallel algorithms for genome and metagenome analysis and he has also worked on communication-avoiding algorithms. Before joining UC Berkeley, he received his diploma in Electrical and Computer Engineering from National Technical University of Athens.

Devarshi Ghoshal is a Research Scientist at Lawrence Berkeley National Laboratory. He received his Ph.D. in Computer Science from Indiana University, Bloomington in 2014. His current research interests include high performance computing, large scale data management in distributed systems, I/O performance benchmarking and performance optimizations in scientific workflows.

Elsa Gonsiorowski currently works at the Livermore Computing, Lawrence Livermore National Laboratory. Elsa does research in Computer Architecture and Software Engineering. Their most recent publication is 'Using quality of service lanes to control the impact of raid traffic within a burst buffer.'

Gary is the Division Leader of the High Performance Computing (HPC) Division at Los Alamos National Laboratory, where he is responsible for managing the personnel and processes required to stand up and operate major supercomputing systems, networks, and storage systems for the Laboratory for both the DOE/NNSA Advanced Simulation and Computing (ASC) program and LANL institutional HPC. He conducts and sponsors R&D and is also the LANL lead in coordinating DOE/NNSA alliances with universities in the HPC I/O and filesystems area. He is one of the principal leaders of a small group of HPC I/O experts that guide the government in its I/O related computer science R&D investments through the HECIWG, and is the Director of the Los Alamos/UCSC Institute for Scientific Scalable Data Management and the Los Alamos/CMU Institute for Reliable High Performance Information Technology. He is also the LANL PI for PDSI, a SciDAC2 Institute award-winning project.

Hui Guan is a Ph.D. candidate in the Department of Electrical and Computer Engineering at North Carolina State University. Her current research focuses on speeding up performance tuning on modern computing systems, especially from the perspectives of algorithm configuration and code optimization. Her research involves various important algorithms in Machine Learning, Data Mining, and Deep Learning.

Luanzheng Guo is a fourth-year Ph.D. student in Computer Science at UC Merced where he is advised by Professor Dong Li. Currently, his research interests lie in resilience problems on large-scale parallel systems.

Chin Guok joined ESnet in 1997 as a network engineer, focusing primarily on network statistics. He was a core engineer in the testing and production deployment of MPLS and QoS (Scavenger Service) within ESnet. He is the technical lead of the ESnet On-demand Secure Circuits and Advanced Reservation System (OSCARS) project which enables end-users to provision guaranteed bandwidth virtual circuits within ESnet. He also serves as a co-chair of the Open Grid Forum On-Demand Infrastructure Service Provisioning Working Group. His research interests include high-performance networking and network protocols; dynamic network resource provisioning; network tuning issues; hybrid network traffic engineering. Guok received a M.S. in Computer Science, from the University of Arizona in 1997 and a B.S. Computer Science, University of Pacific in 1991.

Azzam Haidar is a senior software engineer at NVIDIA. He received his Ph.D. in 2008 from CERFACS, France. Before joining NVIDIA, he was a Research Scientist at the Innovative Computing Laboratory at the University of Tennessee, Knoxville. His research interests focus on the development and implementation of parallel linear algebra routines for scalable distributed multi-core and GPU architectures, for large-scale dense and sparse problems, as well as new algorithms for singular value (SVD) and eigenvalue problems as well as approaches that combine direct and iterative algorithms to solve large linear systems.

Dr. Mahantesh Halappanavar joined Pacific Northwest National Laboratory in December 2009, and is the Team Lead in the Data Science group. His work focuses on parallel graph algorithms and spans several applications including contingency analysis of electric power grids, statistical textual analysis, numerical linear algebra, information security and fault tolerance. He is the PI of the Exagraph Center in the Exascale Computing Project of the DOE and the NNSA.

Dr. Siva Kumar Sastry Hari is a Senior Research Scientist in the Computer Architecture Research Group at NVIDIA. His research interests are in the fields of Computer Architecture, GPUs, Compilers, and Reliable systems. He obtained his Ph.D. and M.S. from the Computer Science Department at University of Illinois at Urbana-Champaign where he received the David J. Kuck Outstanding Ph.D. Thesis Award in 2014. He obtained his bachelor's degree from the Computer Science and Engineering Department at Indian Institute of Technology at Madras.

Conghui He got his PhD degree from the department of computer science and technology in Tsinghua University. His research interests include design methodologies for optimizing computationally intensive applications on heterogeneous computing platforms. The related papers have won honors such as the Gordon Bell Prize of SC17.

Alexander Heinecke studied Computer Science and Finance and Information Management at Technische Universität München, Germany. In 2010 and 2012, he completed internships in the High Performance and Throughput Computing team at Intel, Munich, Germany and at Intel Labs Santa Clara, CA, USA, working on the Intel MIC architecture. In 2013 he finished his Ph.D. studies at Technische Universität München, Germany. He joined Intel's Parallel Computing Lab in Santa Clara, CA, USA in 2014 as Research Scientist. His core research field is the use and co-design of multi- and many-core architectures in emerging scientific computing applications, such as high-order methods for solving partial differential equations or hierarchical discretizations/solvers of high-dimensional problems and for machine / deep learning applications. In 2014, he and his co-authors were selected as Gordon Bell finalists for running multi-physics earthquake simulations at multi-petaflop performance on more than 1.5 millions of cores.

Stephen Herbein is a computer scientist at Lawrence Livermore National Laboratory. He earned his PhD in Computer Science from the University of Delaware. His research interests include next-generation batch job scheduling, parallel IO, and data analytics. In collaboration with researchers at University of Delaware, he has researched and developed both IO-aware and hierarchical next-generation schedulers. He is a member of both the IEEE and the ACM.

Jeff Hittinger is the Division Leader of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research interests include computational plasma physics, computational fluid dynamics, higher-order finite-difference and finite-volume methods, parallel computing, and calculation verification. Jeff received his Ph.D. in Aerospace Engineering and Scientific Computing (2000), his M.S. in Mathematics (1997), and his M.S.E. in Aerospace Engineering (1994). Jeff received his B.S. in Mechanical Engineering from Lehigh University (1993).

Torsten is an Associate Professor of Computer Science at ETH Zurich, Switzerland. Before joining ETH, he lead the performance modeling and simulation efforts of parallel petascale applications for the NSF-funded Blue Waters project at NCSA/UIUC. He is also a key member of the Message Passing Interface (MPI) Forum where he chairs the "Collective Operations and Topologies" working group. Torsten won best paper awards at the ACM/IEEE Supercomputing Conference 2010 (SC10), EuroMPI 2013, ACM/IEEE Supercomputing Conference 2013 (SC13), and other conferences, and has authored chapters of the MPI-2.2 and MPI-3.0 standards. For his work, Torsten received the IEEE TCSC Young Achievers in Scalable Computing Award in 2013, and the Latsis prize of ETH Zurich in 2015. Torsten was elected into the first steering committee of ACM's SIGHPC in 2013. His research interests revolve around the central topic of "Performance-centric Software Development" and include scalable networks, parallel programming techniques, and performance modeling.

Henry Hoffmann is an Associate Professor in the Department of Computer Science at the University of Chicago. He was granted early tenure in 2018. At Chicago he leads the Self-aware computing group (or SEEC project) and conducts research on adaptive techniques for power, energy, accuracy, and performance management in computing systems. He received the DOE Early Career Award in 2015. He completed a PhD in Electrical Engineering and Computer Science at MIT, where his research on self-aware computing was named one of ten "World Changing Ideas" by Scientific American in December 2011. As a Masters student he worked on MIT's Raw processor, one of the first multicores. Along with other members of the Raw team, he spent several years at Tilera Corporation, a startup which commercialized the Raw architecture and created one of the first manycores (Tilera was sold in 2014).

Michael Houston is a Distinguished Engineer at Nvidia. He leads the Deep Learning SW team and works on optimizing deep learning frameworks and algorithms for Nvidia platforms. Mike received his BS in Computer Science with honors from the University of California, San Diego (UCSD) in 2001. During his time at UCSD, he worked at the San Diego Supercomputer Center in the Visualization Group. He received his PhD from Stanford University where he worked in the Stanford Graphics Lab under the advisement of Pat Hanrahan and received an Intel PhD Fellowship. Mike spent several years at ATI and then as an AMD Fellow in the Advanced Technology Development Group working on hardware and software for heterogeneous computing before coming to Nvidia.

Dr. Howie Huang is a Professor of Computer Engineering at the George Washington University. Motivated by the needs of big data and cybersecurity applications, he works at the intersection of algorithm, computer architecture and systems, with recent research focus on developing high-performance computing and machine learning techniques tailored for large-scale graph datasets. His work on graph traversal has ranked highly on both Graph500 and Green Graph500 benchmarks, which measure the performance and energy efficiency of the most powerful data-intensive supercomputers in the world. Dr. Huang is a recipient of the National Science Foundation CAREER Award, NVIDIA Academic Partnership Award, Comcast Technology Research and Development Fund Award, and IBM Real Time Innovation Faculty Award.

Renfei joined HKUST as a Ph.D. student in 2018. He received B.Eng. degree from the College of Computer Science, Zhejiang University. He also received an honored degree in Chu Kochen Honors College, Zhejiang University. His research interests include Distributed Systems, Cloud Computing and Big Data Visualization.

Prof.dr.ir. Alexandru Iosup is a tenured Full Professor and University Research Chair at the Vrije Universiteit Amsterdam, the Netherlands, where he leads the Massivizing Computer Systems group. He is also Associate Professor with the Distributed Systems group at TU Delft, the Netherlands, where he received his Ph.D. in 2009. His work has received numerous awards, including the Netherlands ICT-Researcher of the Year (2016), Netherlands Teacher of the Year (2015), and several SPEC SPECtacular community-awards (the last in 2017). He is a member of the Young Academy of the Royal Academy of Arts and Sciences of the Netherlands. He is elected Chair of the SPEC Research Cloud Group. In his spare time, he contributes to training legal refugees in the Netherlands. You can contact Alexandru by email [ A.Iosup@vu.nl ], visiting (check http://atlarge.science), or via Twitter [ @AIosup ].

Yoko Isobe a manager of AI Platform Division at NEC Corporation, and a visiting researcher of Cyber Science Center at Tohoku University, Japan. Her responsibility at NEC is the performance evaluation of the SX vector supercomputer and other system.

Shintaro Iwasaki is a Ph.D. candidate at the University of Tokyo in Japan. He received his B.S. and M.S. degrees from the University of Tokyo in 2015 and 2017, respectively. His current research interests include parallel languages, compilers, runtime systems, and scheduling techniques.

Dan's career as a computational systems biologist has included leadership roles in academic, corporate and national lab settings. His lab focuses on the development and subsequent application of mathematical, statistical and computational methods to biological datasets in order to yield new insights into complex biological systems. His lab's approaches include the use of Network Theory, Wavelet Theory, data analytics, and explainable-AI in a supercomputing context. These mathematical and statistical methods are applied to various population and (meta)multiomics data sets in an attempt to better understand the functional relationships as well as biosynthesis, signaling, transcriptional, translational, degradation and kinetic regulatory networks at play in biological organisms and communities. His group at ORNL studies many systems - from viruses to microbes to plants to humans. His lab is actively involved in the development of new exascale applications for biology.

Nikhil Jain is a Computer Scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. He works on topics related to parallel computing including networks, performance optimization, scalable application development, and interoperation of languages. Nikhil received a Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2016, and B.Tech. and M.Tech degrees in Computer Science and Engineering from I.I.T. Kanpur, India in May 2009. He was awarded the Sidney Fernbach postdoctoral fellowship for two years in 2016, the IBM PhD fellowship in 2014, and the Andrew and Shana Laursen fellowship in 2011.

Surabhi Jain has worked at Intel Corporation as a Software Development Engineer since 2015, where her team is responsible for enabling next generation MPI for future Exascale machines. Her work mostly currently focuses on optimizing shared memory-based intra-node collectives in MPICH. She graduated from The Ohio State University with a Master’s degree in Computer Science and Engineering, focusing on Parallel and Distributed Computing. She attained a Bachelor’s degree in Computer Engineering from the Indian Institute of Information Technology (IIITD&M) Kancheepuram, Chennai, India.

Yuede Ji is a Ph.D. student from the Department of Electrical and Computer Engineering at The George Washington University working with Prof. Howie Huang. Prior to joining GWU, he received his B.E., M.S. from College of Computer Science and Technology, Jilin University. His current research lies in high performance computing and cyber security. For high performance computing, he mainly focuses on the acceleration of graph computation with multi-processors and GPUs. For cyber security, he mainly focuses on the vulnerability detection from both the source code and the binary with the insight of graph analytics.

Nan (Ted) Jiang is a senior research scientist at NVIDIA's network research group. He received his PhD degree in electrical engineering from Stanford University in 2012. His research interests include a wide range of interconnection networks topics from adaptive routing algorithms, congestion control mechanisms, allocator designs, router designs, topology analysis, and simulation. His network expertise spans from network-on-chip for SoCs to large scale system area networks for high performance computing and data centers.

Chao Jin’s research interests include energy-efficient parallel computing, large-scale distributed systems, and big data. He earned his PhD in Computer Science and Technology from Tsinghua University, China. He currently works in the Research Computing Centre in the University of Queensland.

Wayne is a Computational Scientist at Oak Ridge National Laboratory. He specializes in developing mathematical algorithms and software to solve science problems more effectively on HPC systems. He received an R&D100 Award for the Falcon reservoir simulator project and was Gordon Bell finalist for the uDeviceX project and also for CoMet, the first reported application to achieve an ExaOp. His activities include application requirements gathering, application performance modeling and prediction, performance optimization, application readiness for future leadership systems and acceptance testing of leadership-class HPC systems. He earned his Ph.D. in Mathematics at the University of Texas at Austin.

David Kaeli is presently a College of Engineering Distinguished Professor of ECE at the Northeastern University. Prior to joining Northeastern, he spent 12 years at IBM, the last 7 working at IBM T.J. Watson Research Center. He has published over 350 peer reviewed papers. Dr. Kaeli was named an IEEE Fellow in 2010, and a Distinguished Scientist of the ACM in 2014. He serves an Associate Editor for ACM TACO, IEEE TPDS and the Elsevier JPDC.

Charu Kalra is a PhD student in the Computer Engineering Department at Northeastern University. She is part of the Northeastern University Computer Architecture Research (NUCAR) group under the direction of Prof. David Kaeli. Her research areas include GPU compilers, software reliability, machine learning, workload characterization, and performance evaluation of GPU systems. In 2014, Charu was featured on NVIDIA's 'Women Who CUDA' list. She has also pursued internships at AMD, and AMD Research in the past.

Shoaib Kamil's is a Senior Research Scientist at Adobe Research, where his work spans the areas of high performance programming and computing, domain specific languages and compilers, and program synthesis. Most recently, he was a research scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology, working with Profs. Saman Amarasinghe and Armando Solar-Lezama. Prior to MIT, he obtained his PhD from the University of California at Berkeley as part of the Parallel Computing Laboratory, funded by Intel and Microsoft. He has also worked at Lawrence Berkeley National Laboratory on large-scale codes running on supercomputers for climate simulation, among other applications.

Kate Keahey is one of the pioneers of infrastructure cloud computing. She created the Nimbus project, recognized as the first open source Infrastructure-as-a-Service implementation, and continues to work on research aligning cloud computing concepts with the needs of scientific datacenters and applications. To facilitate such research for the community at large, Kate leads the Chameleon project, providing a deeply reconfigurable, large-scale, and open experimental platform for Computer Science research. To foster the recognition of contributions to science made by software projects, Kate co-founded and serves as co-Editor-in-Chief of the SoftwareX journal, a new format designed to publish software contributions. Kate is a Scientist at Argonne National Laboratory and a Senior Fellow at the Computation Institute at the University of Chicago.

Dr. Stephen W. Keckler is the Vice President of Architecture Research at NVIDIA and Adjunct Professor of Computer Science at the University of Texas at Austin, where he served as a full-time faculty member (full professor with tenure) from 1998 to 2012. At NVIDIA, Dr. Keckler focuses on parallel, energy-efficient architectures that span mobile through supercomputing platforms. He is a Fellow of the ACM, a Fellow of the IEEE, an Alfred P. Sloan Research Fellow, and a recipient of the NSF CAREER award, the ACM Grace Murray Hopper award, the President's Associates Teaching Excellence Award at UT-Austin, and the Edith and Peter O’Donnell award for Engineering. He earned a B.S. in Electrical Engineering from Stanford University and an M.S. and a Ph.D. in Computer Science from the Massachusetts Institute of Technology.

Dr. Arif Khan is a Scientist in the Data Sciences group at the Pacific Northwest National Laboratory since August 2017. His research interests include graph algorithm, high performance computing, approximation algorithm along with their applications in bioinformatics, social networks and machine learning. His goal is to explore how approximation algorithms can solve big graph problems using leadership class supercomputers. Arif graduated in 2017 with a Ph.D. in Computer Science from the Purdue University, West Lafayette, Indiana. He has received the John Rice fellowship at Purdue, and also won a prize in the ACM student research competition on HPC at Supercomputing 2013.

Scott A. Klasky is the group leader for Scientific Data in the Computer Science and Mathematics Research Division at the Oak Ridge National Laboratory. He holds a Ph.D. in Physics from the University of Texas at Austin (1994), and has previously worked at the University of Texas at Austin, Syracuse University, and the Princeton Plasma Physics Laboratory. Dr. Klasky is a world expert in scientific computing and scientific data management, co-authoring over 150 papers. He is also the leader of the ADIOS project.

Christopher Knight is an assistant computational scientist in the Argonne Leadership and Computing Facility since 2013 after his post-doctorate in Argonne. He received his doctorate in Chemistry from Ohio State University in 2009. He works closely with researchers to help them accomplish their scientific goals on both new and mature projects using leadership computational resources. To address the unique challenges of efficiently using leadership-scale resources at ACLF, he assists researchers with porting, profiling, and debugging their codes, discuss strategies and provide general guidance on code parallelization, I/O, load- balancing, workflow design, and data management. His research interests include development and use of scalable algorithms for molecular simulations based on classical and first principles methods.

Hiroaki Kobayashi is currently Professor and Chair of Computer and Mathematical Sciences Department, Graduate School of Information Sciences, Tohoku University. In 1995, 1997-1998 and 2001-2002, he was Visiting Associate Professor of Stanford University (EE department and Computer Systems Lab.) . In 2008-2016, he was Director of Cyberscience Center (Supercomputer Center) of Tohoku University. In 2012-2016, he was a member of Education and Research Council of Tohoku University. His research interests include high-performance computer architectures, supercomputer systems, and their applications. He received the B.E. Degree in Communication Engineering, and the M.E. and D.E. Degrees in Information Engineering from Tohoku University. He is a senior member of IEEE CS, and a member of ACM, IEICE and IPSJ. He received 2017 Minister of Education Award in the Field of Computer Science and received 2018 Minister of Education Award in the field of Science and Engineering.

Kazuhiko Komatsu is an Associate Professor at Cyberscience Center, Tohoku University. His research interests include high performance computing. He received the B.E. Degree in Mechanical Engineering and the M.S. and Ph.D. Degrees in Information Sciences from Tohoku University in 2002, 2004, and 2008, respectively.

Nathaniel Kremer-Herman received the B.A. in Computer Science at Hanover College in Indiana. He is pursuing a Ph.D. in Computer Science & Engineering at the University of Notre Dame. His research interests include scientific applications of distributed computing systems, philosophy of science, and computer-based education.

Dr. Kalyan Kumaran is a Senior Computer Scientist at Argonne Leadership Computing Facility. Prior to Argonne, he held research positions at Silicon Graphics and VMware.

Thorsten is working with the application readiness team to deliver optimized codes for Cori. He also acts as Liaison for defining and demonstrating application portability between the three major US HPC locations, i.e. NERSC, ALCF and OLCF. He is further collaborating with Data Analytic Services (DAS) in order to provide scalable deep learning solutions on the NERSC HPC infrastructure. Before joining NERSC, Thorsten was working as a Postdoc in the Nuclear Science Division at LBNL. He developed and optimized codes for computing multi-baryon correlations in Lattice QCD. He received his PhD from the University of Wuppertal, Germany, in 2011, where he performed calculations for electroweak matrix elements, hadron and quark masses in Lattice QCD.

Ignacio Laguna is a Computer Scientist at the Center for Applied Scientific Computing at the Lawrence Livermore National Laboratory. He earned the M.Sc. and Ph.D. degrees in Computer Engineering from Purdue University, West Lafayette, Indiana, in 2008 and 2012, respectively. His research work focuses on the development of techniques and tools to improve the reliability of high-performance computing applications and systems, including debugging and correctness tools, fault-tolerance and resilience techniques, and application's behavioral analysis. Ignacio was the recipient of the ACM/IEEE-CS George Michael Memorial Fellowship in 2011 and is an IEEE Senior Member.

Michael O. Lam is an Assistant Professor in the Computer Science Department at James Madison University in Harrisonburg, Virginia. He received his Ph.D. and M.S. degrees in Computer Science from the University of Maryland, College Park, and conducts research at Lawrence Livermore National Laboratory as a visiting faculty scholar. His research interests include program analysis, high-performance computing, and systems tools. He is the primary developer for the CRAFT floating-point analysis framework and the SHVAL shadow value instrumentation tool.

Through his position with the Shodor Education Foundation, Inc., Scott Lathrop is the Blue Waters Technical Program Manager for Education. Lathrop has been involved in high performance computing and communications activities since 1986. Lathrop is currently coordinating the community engagement activities for the Blue Waters project. Lathrop has been involved in the SC Conference series since 1989, served as a member of the SC Steering Committee for six years. He served as the Conference Chair for the SC11 and XSEDE14 Conferences. He helped form the International HPC Training Consortium and has been active in the planning and participation in HPC Training Workshops at the SC and ISC Conferences.

Franck Le is a research staff member at IBM T. J. Watson Research Center. His current research interests lie at the intersection of Internet of Things, Artificial Intelligence, and Distributed Systems & Networks. He received a Ph.D. from Carnegie Mellon University in 2010, and a Diplome d'Ingenieur from the Ecole Nationale Superieure des Telecommunications de Bretagne in 2000.

Seyong Lee is a Computer Scientist in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. His research interests include parallel programming and performance optimisation in heterogeneous computing environments, program analysis, and optimizing compilers. He received his PhD in Electrical and Computer Engineering from Purdue University, USA. He is a member of the OpenACC Technical Committee and the NVIDIA PathForward Working Group of the Exascale Computing Project PathForward Program. He served as a program committee/guest editor/external reviewer for various conferences, journals, and research proposals. His paper on SC10 won the best student paper award, and his paper on PPoPP09 was selected as the most cited paper among all papers published in PPoPP between 2009 and 2014. He received the IEEE Computer Society TCHPC Award for Excellence for Early Career Researchers in High Performance Computing at SC16.

Sandia National Laboratories

Dong Li is an assistant professor at Computer Science and Engineering, University of California, Merced since 2015. Previously (2011-2014), he was a research scientist at the Oak Ridge National Laboratory (ORNL). Before that, he earned his PhD in computer science from Virginia Tech. Dong's research focuses on HPC, and maintains a strong relevance to computer systems. The core theme of his research is to study how to enable scalable and efficient execution of scientific and enterprise applications on increasingly complex large-scale parallel systems. His work creates innovation in runtime, architecture, performance modeling, and programming models; His work often coordinates software and hardware to solve challenges on execution efficiency of large-scale parallel systems. Dong received a CAREER Award from NSF (2016), a Berkeley Lab University Faculty Fellowship (2016), and an ORNL/CSMD Distinguished Contributor Award (2013). His paper in SC’14 was nominated as the best student paper.

Jiajia Li is a 5th-year Ph.D. candidate in Computational Science & Engineering at Georgia Institute of Technology and will join High-Performance Computing group of Pacific Northwest National Laboratory (PNNL). She is working as a graduate research assistant in HPC Garage with Professor Richard Vuduc. Before, she was a research intern of IBM Thomas J. Watson Research Center and Intel Parallel Computing Lab in the summers of 2015 and 2016 respectively. In the past, she received a Ph.D. degree from Institute of Computing Technology at Chinese Academy of Sciences. Her current research emphasizes on optimizing tensor algorithms including tensor decompositions and fundamental tensor operations especially for sparse data by utilizing various parallel architectures.

Liandeng Li is a PhD candidate in the Department of Computer Science and Technology at Tsinghua University and National Supercomputer Centre in Wuxi, China. His research interests include design methodologies for optimising computationally intensive applications on heterogeneous computing platforms.

Ruipeng Li received the B.S degree in Computer Science from Beijing University of Posts and Telecommunications, Beijing, China, in 2006, the M.S degree in Computer Science from Arkansas State University, AR, USA, in 2008, and the Ph.D degree in Computer Science from University of Minnesota, MN, USA, in 2015. From 2015 to 2018, he was a postdoctoral researcher at the Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, where he is currently a computer scientist. His research interests include sparse matrix computations, parallel computing, iterative methods for solving linear systems and eigenvalue problems, and multilevel preconditioning techniques.

Xiangyu Li received his B.S. degree in Computer Science at Beijing University of Posts and Telecommunications. He is now pursuing a Ph.D. degree in Electrical and Computer Engineering at Northeastern University, Boston, under Prof. David Kaeli's supervision. His research areas are in general purpose GPU computing, distributed computing systems, machine learning, and its acceleration on these platforms.

Yuxuan Li is the PhD in the Department of Computer Science at Tsinghua University. His research interests include high-performance-computing solutions to geo-science applications based on hybrid platforms such as CPUs, GPUs, and the Sunway TaihuLight system. Li graduated from computer science from Tsinghua University. He has won the first place in both ASC Student Supercomputer Challenge 2017 and ISC Student Cluster Competition 2017.

I joined Oak Ridge National Laboratory in Feb 2012 as a postdoctoral research associate, transited into a staff member in Sept 2013. My current research focuses on data analysis methods and systems. I obtained PhD in computer science and engineering from Penn State in 2012, under the guidance of Dr. Chita R. Das. I obtained my Master's degree in 2000 and bachelor's degree in 1998, both from Seoul National University, Korea. Between PhD and Master's degree, I worked as a software engineer at Samsung electronics from Feb 2000 to Aug 2005.

Feng (Francis) Liu is a PhD candidate in computer science. His study area includes resource management in HPC and cloud environment. He works in the DCSG (Distributed Computing Systems Group) of the CS department of University of Minnesota from 2012 to 2018, during which time, he participated in the DOE-funded AIMES project from 2012 to 2015, and had an internship with Argonne National Lab in 2016 summer. Before starting his PhD career, he worked in the telecommunication industry as a software engineer. He received his Master's degree from PKU and Bachelor's degree from BIT in Beijing, China.

Dr. Hang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering and the Director for High Performance Data Analytics (HPDA) Lab at University of Massachusetts Lowell. His research centers around high-performance graph computing and machine learning. Notably, his Graphics Processing Units (GPU)-accelerated graph traversal system has claimed the No. 1 most energy efficient traversal place in Green Graph 500 in June 2014. Prior to joining UMass Lowell, Dr. Liu earned his Ph.D. degree from the George Washington University where he is the recipient of Phillip/Temofel Sprawcew Endowment Scholarship and Best Dissertation Award from the Department of Electrical and Computer Engineering.

Weiguo Liu received his Bachelor and Master degree from the Xi'an JiaoTong University, China in 1998 and 2002, and the Ph.D. degree from the Nanyang Technological University (NTU), Singapore, in 2006. He is currently a professor and the director of the High-performance Computing and Big Data Processing Lab at Shandong University and published more than 50 articles. Prof. Liu was nominated as "Taishan Scholar" in Shandong province, and received numerous awards (e.g. ACM Gordon Bell Prize award in SC 2017, Fraunhofer IGD Best Paper award, and CCF HPC China Best Paper award). He is also the committee of the high performance computing of China Computer Federation. His research interests include high-performance computing, bioinformatics, and data mining. His research group has designed tools and algorithms for applications in data processing and computational science using parallel computing technologies such as CUDA-enabled GPUs, CPU/GPU/Xeon Phi clusters, and supercomputers.

Xin Liu, Ph.D. in Electrical Engineering, research associate in University of California San Francisco from 2011 to 2013, joined NVIDIA in 2015. In NVIDIA, she works on deep neural network research for autonomous driving and general vision applications, including object detection, scene understanding, and model compression.

Yang (Jace) Liu is a research assistant in the Department of Computer Science and Engineering at Tongji University, China. His research interests include software defined networking, large-scale data analytics systems and high-performance computing. He received a bachelor degree in engineering from the Department of Computer Science and Engineering at Tongji University in 2017.

Scott Lloyd is a computer scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. He received a Ph.D. in Computer Science from Brigham Young University and has broad experience in industry. Scott co-founded a parallel-processing company (which became Linux Networx), worked with diagnostic imaging at GE Medical Systems, and investigated processor-in-memory architecture at Micron Technology. His research interests include high-performance computing applied to the physical and life sciences, computer architecture, and reconfigurable computing.

Glenn K. Lockwood is an engineer at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory who specializes in I/O performance analysis, extreme-scale storage architectures, and emerging I/O technologies and APIs. His research interests revolve around understanding I/O performance by correlating performance analysis across all levels of the I/O subsystem, from node-local page cache to back-end storage devices.

David Lowenthal is Professor of Computer Science at the University of Arizona, where he has been a faculty member since January 2009. His research is in all aspects of parallel and distributed computing, with a particular focus on performance analysis, performance modeling, and power management. Prior to Arizona, he was on the faculty in Computer Science at the University of Georgia. He holds a B.S. degree in Computer Science and Math from the University of California, Davis and M.S. and Ph.D degrees in Computer Science from the University of Arizona.

Nathan Luehr is a Senior Developer Technology Engineer working to accelerate deep learning frameworks at NVIDIA.

John MacAuley is a chief software architect at ESnet.

Ankur Mahesh is an undergraduate student at UC Berkeley studying computer science. He is interested in the application of machine learning and probabilistic graphical models to scientific problems.

Abdulrahman Mahmoud is a PhD candidate at the University of Illinois at Urbana-Champaign in the Department of Computer Science. His primary research focuses on computer architecture and hardware reliability for systems. Prior to his graduate studies, Abdulrahman received his bachelor's degree from Princeton University, where he was awarded the John Ogden Bigelow Jr. Prize in Electrical Engineering.

Preeti Malakar is an Assistant Professor at the Indian Institute of Technology Kanpur. Prior to this, she was a postdoc and then an Assistant Computer Scientist at the Argonne National Laboratory, after receiving her doctorate in Computer Science from Indian Institute of Science Bangalore. She works on large-scale simulation-time analysis, high-performance I/O and topology-aware data movement. Her paper on novel techniques to improve throughput of simulations with multiple regions of interest, was a finalist for the Best Student Paper in Supercomputing 2012. She was awarded the Google India Women in Engineering Award (now Google Anita Borg Scholarship) in 2011 and the TCS Research Fellowship during her PhD.

Ivana Marincic is a Computer Science PhD student at the University of Chicago, advised by Henry Hoffmann. She is actively collaborating with Argonne National Laboratory, where she works with Venkat Vishwanath from Argonne Leadership Computing Facility on power optimizations for supercomputing systems. She obtained her MS in Computer Science at the University of Chicago in 2018, which focused on power profiling and power-aware in-situ analysis of scientific computing simulations at scale.

Pak Markthub received his Bachelor degree in Computer Engineering from Kasetsart University, Thailand in 2011, and the degree of Master of Science in Mathematical and Computing Science from Tokyo Institute of Technology, Japan in 2015. Currently, he is a PhD student under the MEXT scholarship program in the same department he studied during his Master's degree. At that school, he is a member of the Satoshi Matsuoka Research Laboratory, which specializes in high performance computing (HPC) research. He had interned at and is collaborating with Future Technologies Group at Oak Ridge National Laboratory, USA, to investigate runtime systems for managing data movement between large-capacity non-volatile memory and high-performance-throughput oriented processors (such as GPUs). His current research interests are system and accelerator virtualization, efficient data movement in deep memory hierarchy, and system software concerning accelerators.

Fabian Mastenbroek is a B.Sc. student at the Delft University of Technology, the Netherlands. His Honors-track work focuses on datacenter scheduling. He is also a lead contributor to the OpenDC project. Contact him at F.Mastenbroek@atlarge-research.com.

Michael Matheson is a Senior Scientist in the Advanced Data and Workflows Group in the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory. His current research interests include high performance computing, scientific visualization, and scalable analytics. He has worked on projects involving engineering optimization, distributed rendering, and machine learning. He currently is a member of the pbdR project which is a set of highly scalable R packages for distributed and parallel computing and profiling in data science. His employment background has included the aerospace industry and supercomputing computer companies. He is a Mechanical Engineer from the University of Utah specializing in Computational Fluid Dynamics.

Amrita Mathuriya is a Sr. HPC Application Engineer at Intel Corporation working on optimization of deep learning and HPC applications for Intel architectures. She provided expertise in parallel computing and performance enhancement for software development and path-finding projects, to enable optical proximity correction (OPC) for Intel's lithography technology at 14 nm and beyond. Currently, she is working on enabling end-to-end optimized solution for deep learning applications. She has applied HPC in various application domains including Computational Geometry, Electromagnetics, Computational Biology, Quantum Physics and Artificial Intelligence. She obtained B.Tech degree in CS from Indian institute of Technology, Roorkee in India and MS degree in Computational Science and Engineering from Georgia Tech, USA.

Satoshi Matsuoka from April 2018 has become the director of Riken CCS, the top-tier HPC center that represents HPC in Japan, currently hosting the K Computer and developing the next generation Post-K machine, along with multitudes of ongoing cutting edge HPC research being conducted. He was the leader of the TSUBAME series of supercomputers, at Tokyo Institute of Technology, where he still holds a Professor position, to continue his research activities in HPC as well as scalable Big Data and AI.

John McCalpin is a Research Scientist in the High Performance Computing Group at TACC. Before joining TACC, he was a senior individual contributor in performance analysis and system architecture in the computer industry. His industrial experience includes performance analysis and server system architecture at both SGI and IBM, and he served as technology lead for the Torrenza program at AMD (supporting third-party accelerated computing technologies). Prior to his industrial career, John spent six years on the faculty of the University of Delaware, engaged in research and teaching on numerical simulation of the large-scale circulation of the oceans. Best known for the STREAM Benchmark, McCalpin's research interests focus on understanding the complex interactions among the many components of modern memory hierarchies, characterizing important applications in HPC, and developing a system architecture that includes communication and synchronization as first class features.

Maryam Mehri Dehnavi is an Assistant Professor in the Computer Science Department at the University of Toronto. Previously she was an Assitant Professor at Rutgers University. Her research focuses on high-performance computing and domain-specific compiler design. She was a postdoctoral researcher at MIT in 2013-2015 and a visiting scholar at UC Berkeley. She received her Ph.D. in Electrical and Computer Engineering from McGill University in 2013. Maryam is the recipient of the NSF CRII, NSERC CGS, NSERC PDF, and the FQRNT awards. Her research has received the grand final prize of the 2017 ACM Student Research Competitions.

Peter Mendygral is a computational astrophysicist and HPC performance engineer for Cray Programming Environments. He specializes in communication libraries and application performance on Cray systems. Peter received a PhD in astrophysics from the University of Minnesota in 2011 and joined Cray after completing his degree.

Xiangxu Meng received the BSc and MEng degrees from the Department of Computer Science, Shandong University, Jinan, P.R. China in 1982 and 1985, respectively, and the PhD degree from the Institute of Computing Technology, Chinese Academy of Sciences, in 1998. He is a professor in the School of Software, Shandong University. His current research interests include human-computer interaction, virtual reality, computer graphics, CAD/CAM/CIMS, visualization, and high-performance computing.

Harshitha is a postdoctoral research staff in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. Her research involves mixed precision floating point analysis, studying the impact of silent data corruptions on HPC applications, load balancing algorithms and HPC run-time systems. Harshitha received her Ph.D. (2016) and M.S. (2012) in Computer Science from University of Illinois at Urbana-Champaign. She was awarded ACM/IEEE-CS George Michael Fellowship in 2014, Anita Borg Scholarship in 2014 and Siebel Scholarship in 2012.

Michelle has been a professor in the Department of Computer Science at the University of Arizona since August 2015. Prof. Strout's main research area is high performance computing and her research interests include compilers and run-time systems, scientific computing, and software engineering. She earned her Ph.D. at the University of California, San Diego in 2003 with Jeanne Ferrante and Larry Carter as co-advisors. In 2008, Michelle received a CAREER Award from the National Science Foundation for her research in parallelization techniques for irregular applications, such as molecular dynamics simulations. In 2010, she received a DOE Early Career award to fund her research in separating the specification of scientific computing applications from the specification of implementation details such as how to parallelize such computations.

Sanchit Misra received the PhD degree in computer engineering from Northwestern University in 2011. He is currently a research scientist at Intel’s Parallel Computing Labs, Bengaluru, India. His research interests include high-performance computational biology, machine learning, parallel algorithms, and application driven architecture research. He is currently working on architecture specific parallel algorithms for key computational biology and machine learning kernels for the latest multi- and many-core architectures and using that for application-driven architecture research.

Kathryn Mohror is the Group Leader for the Data Analysis Group in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). Kathryn’s research on high-end computing systems is currently focused on scalable fault tolerant computing and I/O for extreme scale systems. Her other research interests include scalable performance analysis and tuning, and parallel programming paradigms. Kathryn received her Ph.D. in Computer Science in 2010, an M.S. in Computer Science in 2004, and a B.S. in Chemistry in 1999 from Portland State University (PSU) in Portland, OR.

Shintaro Momose is a principal engineer at NEC Deutschland GmbH, and a visiting associate professor of Cyber Science Center at Tohoku University, Japan. His responsibilities at NEC Deutschland are the technical marketing, concept/architecture design, and technical promotion of the SX vector supercomputer. He received the B.E. Degree in Mechanical Engineering, and the M.S. and the Ph.D. Degrees in Information Sciences from Tohoku University in 1999, 2003, and 2005 respectively.

Mayur Mudigonda got his undergraduate degree from Anna University in Tamil Nadu, India. He later got his Masters degree in Computer Science and a specialization in Cognitive Science from Michigan State University. Presently, he is a PhD candidate at UC Berkeley at the Redwood Center for Theoretical Neuroscience. Mayur's research interests are largely focused on robotics, vision, theoretical neuroscience, and machine learning with its applications to science.

Todd Munson received a B.S. in Computer Science from the University of Nebraska in 1995, and an M.S. in 1996 and Ph.D. in 2000 in Computer Science from the University of Wisconsin at Madison. He is a Computational Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory, a Senior Fellow in the Computation Institute at the University of Chicago and Argonne National Laboratory. The primary focus of his research is algorithms and applications of numerical optimization and variational inequalities. He has been widely recognized for his contributions. Among other honors he was awarded both a Presidential Early Career Award for Scientists and Engineers from the White House and an Early Career Scientist and Engineer Award from the US Department of Energy in 2006 and the Beale-Orchard-Hayes Prize from the Mathematical Programming Society in 2003.

Akihiro Musa is an executive specialist of 1st Government and Public Solutions Division at NEC Corporation, and a visiting Professor of Cyber Science Center at Tohoku University, Japan. He received Ph.D. Degrees in information sciences from Tohoku University in 2009.

Santosh Nagarakatte is an Associate Professor of Computer Science at Rutgers University. He obtained his PhD from the University of Pennsylvania in 2012. His research interests are in Hardware-Software Interfaces spanning Programming Languages, Compilers, Software Engineering, and Computer Architecture. His papers have been selected as IEEE MICRO TOP Picks papers of computer architecture conferences in 2010 and 2013. He has received the NSF CAREER Award in 2015, ACM SIGPLAN PLDI 2015 Distinguished Paper Award, and ACM SIGSOFT ICSE 2016 Distinguished Paper Award for his research on LLVM compiler verification.

Harvey Newman (Sc. D, MIT 1974) is the Marvin L. Goldberger Professor of Physics at Caltech, and a faculty member since 1982. In 1973- 4 he co-led the team that discovered fourth quark flavor known as “charm”. He co-led the MARK J Collaboration that discovered the gluon, the carrier of the strong force in 1979. Since 1994 he has been a member of CMS that discovered the Higgs boson at the LHC in 2012. Newman has had a leading role in originating, developing and operating state of the art international networks and collaborative systems serving the high energy and nuclear physics communities since 1982. He served on the IETF and the Technical Advisory Group that led to the NSFNet in 1985-6, originated the worldwide LHC Computing Model in 1996, and has led the science and network engineering teams defining the state of the art in long-distance data transfers since 2002.

Daniel Osei-Kuffuor is a computational scientist in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. Daniel's research interests include using numerical analysis, numerical linear algebra, and high-performance computing to advance numerical solver capabilities for physics applications. He earned his PhD in Scientific Computing from the University of Minnesota in 2011.

Tony Pan is a senior research scientist in the Interdisciplinary Research Institute for Data Engineering and Science (IDEaS) at Georgia Institute of Technology. He received the Sc.B. degree in Biophysics from Brown University, the M.S. degree in Computer Science from Rensselaer Polytechnic Institute, and the Ph.D. degree in Computational Science and Engineering from Georgia Institute of Technology. Previously, he held research positions at General Electric, The Ohio State University, and Emory University. His research interests include high performance computing, distributed information systems, bioinformatics, and biomedical and imaging informatics.

Michael Papka is the division director of the Argonne Leadership Computing Facility (ALCF) and the deputy associate laboratory director for Computing, Environment, and Life Sciences (CELS). As director of the ALCF, Dr. Papka is responsible for a DOE national scientific user facility that houses one of the world’s fastest supercomputers and enables researchers to address some of the toughest challenges in science and engineering. In his role as deputy in the CELS directorate, he supports programmatic efforts that either strongly contribute to or greatly benefit from high performance computing. Dr. Papka is also a senior fellow of the University of Chicago/Argonne National Laboratory Computation Institute, where he conducts interdisciplinary studies involving multiscale simulation data, and investigates techniques for managing, processing, and analyzing data in the computational pipeline in order to find crucial information leading to scientific breakthroughs. He is also an associate professor of computer science at Northern Illinois University.

Manish Parashar is Distinguished Professor of Computer Science at Rutgers University. He is also the founding Director of the Rutgers Discovery Informatics Institute (RDI2). He is currently on an IPA appointment at the National Science Foundation. His research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering. Manish is the founding chair of the IEEE Technical Consortium on High Performance Computing (TCHPC), Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems. He has received a number of awards for his research and leadership, and is Fellow of AAAS, Fellow of IEEE/IEEE Computer Society and ACM Distinguished Scientist. For more information please visit http://parashar.rutgers.edu/.

Dr. Robert M. Patton is a computational analytics scientist at Oak Ridge National Laboratory. His research is focused on nature-­‐inspired computational techniques for large‐scale data analytics. He is a member of IEEE’s CI Society and ACM’s SIGEVO.

Everett Phillips is a Senior Applied Engineer at NVIDIA with the HPC Software and Benchmarks group.

Randall Pittman is a Masters student in the Department of Computer Science at North Carolina State University. His research is primarily Deep Learning optimization, with a focus on the systems that enable efficient compute device utilization.

Samuel Pollard is a Ph.D student at the University of Oregon with a focus on HPC. He first received a B.S. in mathematics (2014) and an M.S. in computer science (2016) from Western Washington University. He has research experience in job scheduling at Lawrence Livermore National Lab and symbolic execution at Sandia National Lab. His research interests include performance analysis, program transformation, parallel programming models, and numerical analysis.

Alex Pothen is a Professor in the department of Computer Science at Purdue. Alex's research interests are in combinatorial scientific computing (CSC), parallel computing, and bioinformatics algorithms. He is a Fellow of the Society for Industrial and Applied Mathematics. He was the Director of the CSCAPES Institute, a pioneering research center in CSC (2006-2012). He is an editor of the Journal of the ACM, and has served on the editorial boards of SIAM Review, SIAM Books, SIAM Journal on Scientific Computing, and other journals and book series. Alex has advised more than twenty PhD students and post-doctoral scholars, and his research is supported by the DOE, Exasacale Computing Project, NSF, and Intel Corporation.

Heidi Poxon is a senior principal engineer in the Programming Environments group at Cray Inc., where she leads the application modeling and performance tools activities for all Cray systems. She has over 25 years of experience in high performance computing, where she has designed, developed, and ported distributed and shared memory parallel programming model software to a variety of proprietary operating systems, led the direction, design, and implementation of message passing software at Cray Research, and was instrumental in the success of the distributed computing environment at SGI that allowed applications to scale on their leading edge system. Using her experience with proprietary networks, she also led the interconnect and distributed computing direction for an HPC Linux-based cluster company. Most recently, she was the principal designer of Cray’s application parallelization tool, Reveal.

Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization. He is also interested in applied statistics, machine learning, computer graphics and computer vision. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

Fritz is a PhD student in Computer Engineering at Northeastern University. He is part of the NUCAR (Northeastern University Computer Architecture) group, under the direction of Prof. David Kaeli. Fritz received his Bachelor of Science in Computer Engineering from Northeastern University in May 2012. His research focuses on program reliability, more specifically on the analysis of the effects of soft errors on GPU programs. He has also worked on architectural simulation of embedded processors and on the implementation of a MIPS emulator within the CPU-GPU simulation framework: Multi2Sim.

Lavanya Ramakrishnan is a staff scientist at Lawrence Berkeley National Lab. Her research interests are in software tools for computational and data-intensive science. Ramakrishnan has previously worked as a research staff member at Renaissance Computing Institute and MCNC in North Carolina. She has masters and doctoral degrees in Computer Science from Indiana University and a bachelor degree in computer engineering from VJTI, University of Mumbai. She joined LBL as an Alvarez Postdoctoral Fellow in 2009.

Jie is a PhD student in School of Computer Science and Engineering at the University of California, Merced. Her research focuses on data consistency in large-scale parallel systems. Jie received her bachelor degree in computer science from Beijing Institute of Technology, China, in 2017. She severed as a student volunteer in SC'17. She did an internship in Lawrence Livermore National Lab in summer 2018.

Tahsin Reza is a PhD student in Computer Engineering at University of British Columbia. His research interests are parallel and distributed systems for HPC applications. His doctoral research is in the area of distributed graph processing, specifically problems concerning pattern mining in large metadata graph. He earned MSc and BSc degrees from Carleton University in Ottawa and Memorial University of Newfoundland, respectively. His professional experiences include research internships at Lawrence Livermore National Laboratory and a software developer position at BlackBerry Ltd. in Waterloo.

Michael Ringenburg is a Principal Engineer in the Artificial Intelligence and Advanced Productivity group at Cray, where he works closely with engineers and data scientists to enable complex machine learning and analytics workflows on Cray systems. Prior to joining the analytics and artificial intelligence team, Michael worked on compilers, debuggers, and runtime systems at Cray. Michael also teaches occasional classes in the University of Washington Computer Science Professional Masters Program. He holds a PhD from the University of Washington in Computer Science, where he focused on programming language support for novel architectures.

Pierre Riteau is a software architect and developer. As a member of staff of the Computation Institute at University of Chicago, he acted as lead DevOps engineer of the Chameleon testbed from 2015 to 2018, leading the development of the software and the system operations of the testbed. He holds a PhD in Computer Science from the University of Rennes 1 in France.

Joshua Romero is a Developer Technology Engineer at NVIDIA. His expertise is in GPU computing with a focus on high performance scientific computing applications.

Norm Rubin has many years of experience delivering commercial compilers for processors, and is a recognized expert in the field. He was the architect and lead implementer for the widely used AMD graphics compute GPU compiler. Norm was part of the AMD architecture team that designed GCN (Graphics core next). For the last several years, he has been at NVIDIA Research where he is working in algorithms and future programming models. Lately, Norm has been building specialized dsl compilers for machine learning. Norm is a visiting scholar at Northeastern University. Dr. Rubin holds a PhD from the Courant Institute of NYU. Besides his work in compilers and architecture, he is well known for his work in GPU systems, compiler related parts of the tool chain, binary translators and dynamic optimizers.

Yousef Saad is a College of Science and Engineering (CSE) distinguished professor with the Dept of Computer Science and Engineering at the University of Minnesota. He received the "Doctorat d'Etat" from the University of Grenoble (France) in 1983. He was head of the Dept of Computer Science and Engineering from January 1997 to June 2000 and became a CSE distinguished professor in 2005. From 1981 to 1990, he held positions at Berkeley, Yale, UIUC, and RIACS. His current research interests include numerical linear algebra, sparse matrix computations, iterative methods, parallel computing, numerical methods for electronic structure, and linear algebra methods in data mining. He is the developer or co-developer of several software packages for solving sparse linear systems of equations and eigenvalue problems including SPARSKIT, pARMS, ITSOL, and EVSL. He is also a SIAM fellow (class of 2010) and a fellow of the AAAS (2011).

Vivek Sarkar is a Professor in the School of Computer Science, and the Stephen Fleming Chair for Telecommunications in the College of Computing at Georgia Tech. Previously, Sarkar was Professor of Computer Science and the E.D. Butcher Chair in Engineering at Rice University, He leads the Habanero Extreme Scale Software Research Laboratory which develops programming system foundations for current and future high-performance computing systems and influences standards such as OpenMP, Java concurrency, and C++ tasking libraries. Earlier, Sarkar was Senior Manager of Programming Technologies at IBM Research, where he led the X10 programming language, the Jikes Research Virtual Machine, and the ASTI optimizer projects. Sarkar was a member of the IBM Academy of Technology during 1995-2007, and was inducted as an ACM Fellow in 2008. He has served on the US Department of Energy’s Advanced Scientific Computing Advisory Committee (ASCAC) since 2009, and CRA’s Board of Directors since 2015.

Masayuki Sato received the B.E. degree from Tohoku University, in 2007, and the M.S. and PhD degrees in information sciences from Tohoku University, in 2009 and 2012, respectively. He is currently an assistant professor of the Graduate School of Information Sciences, Tohoku University. His research interests include high performance and low-power computer architecture.

Markus Schordan joined the Center for Applied Scientific Computing (CASC) in April 2013 as a senior computer scientist. His research interests include program analysis and formal software verification, reversible computation, and compiler construction. He is co-author of the software verification tool CodeThorn, the tool Backstroke for reversible computation, and the ROSE compiler infrastructure. In 2009 he received an R&D 100 AWARD, in 2011 a Best Paper Award at the 11th IEEE Source-Code and Manipulation Conference (SCAM 2011), and in 2012 and 2013 he received the Method Combination Award in the RERS software verification challenge. In 2014, 2015, and 2017 he won with his team in various tracks of the RERS Challenge. He received a Diploma Degree in computer science from TU Vienna in 1997, and a Ph.D. degree from University Klagernfurt in 2001 (with distinction). He is author of 35+ peer-reviewed publications and has been PC member in 25+ conferences.

Martin Schulz is a Full Professor at the Technische Universität München (TUM), which he joined in 2017. Prior to that, he held positions at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL) and Cornell University. He earned his Doctorate in Computer Science in 2001 from TUM and a Master of Science in Computer Science from UIUC. Martin has published over 200 peer-reviewed papers and currently serves as the chair of the MPI Forum, the standardization body for the Message Passing Interface. His research interests include parallel and distributed architectures and applications; performance monitoring, modeling and analysis; memory system optimization; parallel programming paradigms; tool support for parallel programming; power-aware parallel computing; and fault tolerance at the application and system level. Martin was a recipient of the IEEE/ACM Gordon Bell Award in 2006 and an R&D 100 award in 2011.

John Shalf is Department Head for Computer Science at Lawrence Berkeley National Laboratory (LBNL). Shalf is a coauthor of over 60 publications in the field of parallel computing software and HPC technology, including three best papers and the widely cited report “The Landscape of Parallel Computing Research: A View from Berkeley” (with David Patterson and others). Prior to joining Berkeley Laboratory, John worked at the National Center for Supercomputing Applications, and was a visiting Scientist at the Max Planck / Albert Einstein Institute in Potsdam where he co-authored the Cactus Computational Toolkit.

Lei Shao is a Software Application Engineer at Intel Corporation working on enabling end-to-end optimized solution for deep learning applications. She has prior experience in Machine Learning/Predictive Modeling for wireless products. Lei holds 20+ US patents. Lei got her PhD degree in Electrical Engineering from University of Washington, USA.

Xipeng Shen is a Professor in Computer Science at North Carolina State University in USA. He received the Early Career Research Award from the US Department of Energy in 2011, and CAREER Award from US NSF in 2010. His primary research work lies in the field of programming systems and high performance computing, featuring an emphasis on inter-disciplinary problems and approaches with a recent focus on high performance machine learning and high-level program optimizations. Prior to joining NC State in 2014 as a Chancellor's Faculty Excellence Program cluster hire in Data-Driven Science, Shen was the Adina Allen Term Distinguished Associate Professor in the Computer Science Department at The College of William and Mary. He received his Ph.D. in Computer Science from University of Rochester in 2006.

Jia Shi is a Ph.D. candidate at the Geo-Mathematical Imaging Group (GMIG) in the Department of Earth, Environmental and Planetary Sciences, Rice University, USA. He received his B.S. in geophysics and B.A. in Economics from Peking University, Beijing, China, in 2012. His research primarily focuses on seismic inverse problems, normal mode seismology, and high-performance computing.

Staci Smith is a fourth-year Ph.D. student under Dr. David Lowenthal at the University of Arizona. Her research interests are in performance analysis, performance modeling, and runtime systems for HPC machines. She currently focuses on communication and network performance, with interest in using routing, software-defined networking, and job scheduling techniques to decrease network congestion. She received her B.S in Mathematics and Computer Science from the University of Arizona in 2014, where she was selected as Outstanding Senior by both departments. She was a computer science Galileo Circle Scholar in 2013 and again in 2017, and she is currently the 2018-2019 ARCS Foundation Crawford Endowment Scholar.

Dr. Michael Sullivan is a research scientist studying computer system architecture at NVIDIA in Austin, TX. His main research interest is the design of efficient and dependable large-scale computer systems. Specifically, he has studied system-level reliability modeling with cross-layer coordination, strong memory system protection, low-cost pipeline protection, and efficient and reliable application-specific acceleration. He obtained his Ph.D. from University of Texas at Austin and his bachelor’s degree from George Mason University.

Jimeng Sun is an Associate Professor of College of Computing at Georgia Tech. Prior to Georgia Tech, he was a researcher at IBM TJ Watson Research Center. His research focuses on health analytics and data mining, especially in designing tensor factorizations, deep learning methods, and large-scale predictive modeling systems. Dr. Sun has been collaborating with many healthcare organizations. He published over 120 papers and filed over 20 patents (5 granted). He has received SDM/IBM early career research award 2017, ICDM best research paper award in 2008, SDM best research paper award in 2007, and KDD Dissertation runner-up award in 2008. Dr. Sun received B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, M.Sc and PhD in Computer Science from Carnegie Mellon University in 2006 and 2007.

Li Tan received her BSc and MSc in Computer Science from Beijing Technology and Business University, China in 2001 and 2004. She received her PhD in Computer Science from Beijing Institute of Technology, China in 2009. She is now a Professor of Computer Science Department at the Beijing Technology and Business University. Her research interests include machine learning algorithms, wireless sensor network and mobile sensor network.

Kenjiro Taura received the BS, MS, and DSc degrees from the University of Tokyo, in 1992, 1994, and 1997, respectively. He is a professor in the Department of Information and Communication Engineering, the University of Tokyo. His major research interests include centered on parallel/distributed computing and programming languages. His expertise includes efficient dynamic load balancing, parallel and distributed garbage collection, and parallel/distributed workflow systems. He is a member of the ACM and the IEEE.

Douglas Thain is a Professor and the Associate Chair in the Department of Computer Science and Engineering at the University of Notre Dame. He received the B.S. in Physics from the University of innesota - Twin Cities and the M.S. and Ph.D. in Computer Sciences from the University of Wisconsin - Madison, where he contributed to the Condor distributed computing system. At Notre Dame, he works closely with researchers in multiple fields of science and engineering to attack scientific problems using large scale computing. His research team creates and publishes open source software that is used around the world to harness large scale computing systems such as clusters, clouds, and grids.

Jayaraman J. Thiagarajan received the Ph.D. degree in electrical engineering from Arizona State University, Tempe, AZ, USA, in 2013. He is currently a Computer Scientist with the Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA. He has co-authored two books. His current research interests include machine learning, computer vision, natural language processing, signal processing, topological data analysis, the investigation of using machine learning for science, clinical data analysis, high-performance computing, computed tomography, and building tools for model interpretability. Dr. Thiagarajan was a recipient of the multiple Best Paper Nominations at premier IEEE conferences. He has served as a reviewer for several IEEE, ACM, Elsevier, and Springer journals and conferences.

John Thomson is a Lecturer (Assistant Professor) in the School of Computer Science, University of St Andrews. John's Research interests center around empirical approaches to systems design, including optimising compilers, video compression, HPC, embedded systems, applied machine learning, parallelisation techniques and runtime systems.

Benjamin Tovar is a research software engineer at the University of Notre Dame. In his current role, he is the lead maintainer of CCTools, a suite of tools to quickly enable scientist the use distributed, highthroughput computing. Prior to his position at Notre Dame, he was a Post-doctoral fellow in the area of control engineering in robotics at Northwestern University, and he received a Ph.D. in Computer Science from the University of Illinois UrbanaChampaign, where he studied algorithmic modeling for robotics.

Sean Treichler is a Principal Research Scientist and has contributed to the architectures of multiple generations of NVIDIA GPU products. Having recently returned from the doctoral program in computer science at Stanford University, he is exploring the co-evolution of processor architectures and the programming models that drive them, with an emphasis on enabling programmers to quickly close the gap between theoretical and achieved performance for applications of interest in the scientific computing and machine learning communities.

For the past 25 years, Dr. Timothy Tsai has been involved with research into the reliability of many aspects of computing systems, including general computing systems, supercomputers, telecommunications systems, automotive systems, and storage subsystems. His current work focuses on the reliable use of NVIDIA processors, software, and systems, particularly for HPC and automotive uses. He obtained his Ph.D. from Electrical and Computer Engineering department at University of Illinois at Urbana-Champaign and B.S. from Electrical and Computer Engineering department at Brigham Young University.

Dr. Antonino Tumeo received the M.S degree in Informatic Engineering, in 2005, and the Ph.D degree in Computer Engineering, in 2009, from Politecnico di Milano in Italy. Since January 2014, he is a Senior Research Scientist the PNNL's High Performance Computing group. He Joined PNNL in 2009 as a post doctoral research associate, and became a research scientist in February 2011. Previously, he was a post doctoral researcher at Politecnico di Milano. His research interests are modeling and simulation of high performance architectures, hardware-software codesign, FPGA prototyping and GPGPU computing. He is a Senior Member of the ACM and of the IEEE.

Didem Unat joined Koç University in September 2014 as a full time faculty. Previously she was at the Lawrence Berkeley National Laboratory and worked at the Exascale Combustion Co-design center. She is the recipient of the prestigious Luis Alvarez Fellowship in 2012 at the Berkeley Lab. Her research interest lies primarily in the area of high performance computing, parallel programming models, compiler analysis and performance modeling. She is currently working on designing and evaluating programming models for state-of-the-art computer architectures. She received her Ph.D under Prof. Scott B. Baden's research group at University of California-San Diego. In her thesis, she developed the Mint programming model and its source-to-source compiler to facilitate GPGPU programming. She holds a B.S in computer engineering from Boğaziçi University.

Laurens Versluis is a Ph.D. student at Vrije Universiteit Amsterdam, the Netherlands, where he studies modern distributed systems. His work on massivizing computer systems focuses on resource management and scheduling with non-functional requirements, for applications in cloud computing. Currently he is one of the tech leads of the MagnaData project. Contact him at L.F.D.Versluis@vu.nl.

Jeffrey Vetter, Ph.D., is a Distinguished R&D Staff Member at Oak Ridge National Laboratory (ORNL). At ORNL, Vetter is the founding group leader of the Future Technologies Group in the Computer Science and Mathematics Division. Vetter also holds a joint appointment at the University of Tennessee-Knoxville. Vetter earned his Ph.D. in Computer Science from the Georgia Institute of Technology. Vetter is a Fellow of the IEEE, and a Distinguished Scientist Member of the ACM. In 2010, Vetter, as part of an interdisciplinary team from Georgia Tech, NYU, and ORNL, was awarded the ACM Gordon Bell Prize. His work has won awards at major conferences including Best Paper Awards at the International Parallel and Distributed Processing Symposium (IPDPS) EuroPar, Best Student Paper Finalist at SC14, and Best Presentation at EASC 2015. In 2015, Vetter served as the SC15 Technical Program Chair. See his website for more information: http://ft.ornl.gov/~vetter/.

Venkatram Vishwanath is a computer scientist at Argonne National Laboratory. He is the Data Science group lead at the Argonne leadership computing facility (ALCF). His current focus is on algorithms, system software, and workflows to facilitate data-centric applications on supercomputing systems. His interests include scientific applications, supercomputing architectures, parallel algorithms and runtimes, scalable analytics and collaborative workspaces. He has received best papers awards at venues including HPDC and LDAV, and a Gordon Bell finalist. Vishwanath received his Ph.D. in computer science from the University of Illinois at Chicago in 2009.

Richard (Rich) Vuduc is an Associate Professor at the Georgia Institute of Technology (“Georgia Tech”), in the School of Computational Science and Engineering, a department devoted to the study of computer-based modeling and simulation of natural and engineered systems. His research lab, The HPC Garage (@hpcgarage), is interested in high-performance computing, with an emphasis on algorithms, performance analysis, and performance engineering. He is a recipient of a DARPA Computer Science Study Group grant; an NSF CAREER award; a collaborative Gordon Bell Prize in 2010; Lockheed-Martin Aeronautics Company Dean’s Award for Teaching Excellence (2013); and Best Paper Awards at the SIAM Conference on Data Mining (SDM, 2012) and the IEEE Parallel and Distributed Processing Symposium (IPDPS, 2015), among others. He received his Ph.D. in Computer Science from the University of California, Berkeley, and was a postdoctoral scholar in the Center for Advanced Scientific Computing the Lawrence Livermore National Laboratory.

I am Wubing Wan, I graduated from Xiamen University. The subject of postgraduate research is statistical physics. Now I am an employee of the National Supercomputer Center of Wuxi. My main job is to optimize the code performance.

Chenyu Wang is a MSc student in School of Computer Science, University of St Andrews. She received a BSc in Computer Science from Beijing Technology and Business University in 2018 and finished her final year research project in National Supercomputer Centre in Wuxi, China and Tsinghua University. Her research interests include large-scale scientific computing, and remote sensing image analysis.

Wei Wang is currently an Assistant Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He is also affiliated with HKUST Big Data Institute. Wei received Ph.D. from the University of Toronto in 2015, and M.Eng. and B.Eng from Shanghai Jiao Tong University, all in the Department of Electrical and Computer Engineering. His research interests cover the broad area of distributed systems, with special emphasis on big data and machine learning, cloud computing, and computer networks in general. He is a recipient of the 2015 Chinese Government Award for Outstanding Students Abroad and the Best Paper Finalist Award at the USENIX ICAC 2013.

Xin Wang is a Ph.D. candidate in the Department of Computer Science and Engineering at Tongji University. His research interests include software defined networking, interdomain routing and distributed computing. He received a bachelor degree in engineering from the Department of Computer Science and Engineering at Tongji University in 2013.

Yanwen Wei, a PhD candidate, studies in Tsinghua Univeisity, Department of Earth System Science. She has research experiences in seismic wave propagation simulation, full waveform inversion and application of machine learning in geophysics.

Jon B. Weissman is a Professor of Computer Science at the University of Minnesota where he leads the Distributed Computing Systems Group. His research interests are in distributed systems, cloud/edge computing, high performance computing, and storage systems.

Nicholas J. Wright is the group lead for the advanced technologies group at NERSC/LBNL. His work is focused upon evaluating future technologies for potential application in scientific computing and performance measurement and optimization. He earned both his undergraduate and doctoral degrees in chemistry at the University of Durham in England.

Kai is currently a PhD student in EECS at the University of California, Merced. Before coming to UC Merced, he got his Masters degree in Computer Science and Engineering from Michigan State University in 2016. His research broadly falls into general areas of High-Performance Computing (Large-Scale Parallel Systems). Specifically, he focuses on the following areas: (i) Resource Management in Heterogeneous Computing (Non-volatile memory); (ii) Parallel programming models and runtime; (iii) Performance optimization and modeling; (iv) Resilience and consistency. He served as a student volunteer in SC'16. He served as an external reviewer for SC'18, IPDPS’17, Cluster'17, NAS'17, and HPCC'17. He did an internship at Lawrence Livermore National Laboratory in 2018 and another internship at Los Alamos National Laboratory in 2017.

Yuanzhe Xi is an assistant professor in the Department of Mathematics and Computer Science at Emory University. He received his M.S. degree in Computational Finance and Ph.D. degree in Applied Mathematics from Purdue University in 2014. From 2014 to 2018, he served as a postdoctoral research associate position in the Department of Computer Science and Engineering at the University of Minnesota. His current research interests include fast multiscale algorithms for scientific computing and data analytics, numerical optimization, randomized algorithms, high-performance computing.

Qiao Xiang is a postdoctoral fellow in the Department of Computer Science at Yale University. His research interests include software defined networking, resource discovery and orchestration in collaborative data sciences, interdomain routing, and wireless cyber-physical systems. From 2014 to 2015, he was a postdoctoral fellow in the School of Computer Science at McGill University. He received his master and Ph.D. degrees in computer science at Wayne State University in 2012 and 2014, respectively, and a bachelor degree in information security and a bachelor degree in economics from Nankai University in 2007.

Wei XUE is an associate professor in the Department of Computer Science and Technology at Tsinghua University. His research interests include scientific computing and uncertainty quantification. Wei has a PhD in electrical engineering from Tsinghua University and has been awarded the 2016 ACM Gordon Bell Prize, Computational Earth Science Young Researcher Award (2013). He is a senior member of CCF and a member of IEEE and ACM.

Guangwen Yang is a professor in the Department of Computer Science and Technology and the director of the National Supercomputing Center in Wuxi. His research interests include parallel algorithms, cloud computing, and the earth system model. Yang has a PhD in computer architecture from Harbin Institute of Technology.

Dr. Y. Richard Yang is a Professor of Computer Science and Electrical Engineering at Yale University. Dr. Yang's research is supported by both US government funding agencies and leading industrial corporations, and spans areas including computer networks, mobile computing, wireless networking, and network security. His work has been implemented/adopted in products/systems of major companies (e.g., AT&T, Alcatel-Lucent, Cisco, Google, Microsoft, Youku), and featured in mainstream media including Economist, Forbes, Guardian, Chronicle of Higher Education, Information Week, MIT Technology Review, Science Daily, USA Today, Washington Post, and Wired, among others. His awards include a CAREER Award from the National Science Foundation and a Google Faculty Research Award. Dr. Yang's received his B.E. degree in Computer Science and Technology from Tsinghua University (1993), and his M.S. and Ph.D. degrees in Computer Science from the University of Texas at Austin (1998 and 2001).

Kathy Yelick is the Associate Laboratory Director for Computing Sciences at Lawrence Berkeley National Laboratory (Berkeley Lab). The Computing Sciences area at Berkeley Lab comprises the National Energy Research Scientific Computing Division (NERSC), the Scientific Networking Division (home to the Energy Sciences Network, ESnet) and the Computational Research Division. Dr. Yelick has been a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley since 1991 and has held a joint research appointment at Berkeley Lab since 1996. She was leader of the Future Technologies Group from 2005 through 2007 and the NERSC Director from 2008 through 2012. She earned her Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and is an internationally recognized expert in high performance computing. Her research interests include parallel programming languages, automatic performance tuning, performance analysis, parallel algorithms, and optimizing compilers.

Dr. Steven Young is a research scientist at Oak Ridge National Laboratory working in the Computational Data Analytics Group. He earned a Ph.D. in Computer Engineering from The University of Tennessee where he studied machine learning in the Machine Intelligence Lab. He also holds a B.S. in Electrical Engineering from The University of Tennessee. His current research involves applying machine learning to large scale datasets with a focus on deep learning methods.

Teng Yu is a PhD candidate in School of Computer Science, University of St Andrews. He received a MRes in Advanced Computing from Imperial College London in 2016, a BSc in Computer Science from University College Cork joint with Beijing Technology and Business University in 2015. His research interests include operating systems scheduling, high performance computing, heterogeneous systems and applied machine learning.

Yinghao is currently a Ph.D candidate in the ECE Department of Hong Kong University of Science of Technology (HKUST). Before joining HKUST, Yinghao received his B.Sc. degree from the Department of Electrical Engineering at Fudan University, Shanghai in July 2015. His research interest focuses focus on cache management in data-parallel systems.

Jingxuan (Jensen) Zhang is a Ph.D. candidate in the Department of Computer Science and Engineering at Tongji University. His research focuses on network resource discovery, abstraction and programming consistency for large-scale data analytics systems. He is also an active member of the IETF ALTO working group and the OpenDaylight open source community. He received a bachelor degree in engineering from the Department of Computer Science and Engineering at Tongji University in 2015.

Meng Zhang is a master candidate in Shandong University, School of Computer Science, advised by Dr. Weiguo Liu. And she also recevied her bachelor degree from the same university in 2017. Her research focuses on large scale parallel computation and optimization.

Tingjian Zhang is a master candidate in the Department of Computer Science and Technology in Shandong University. His research interests include high performance computing and machine learning. To our best knowledge, he is currently the youngest Gordon Bell prize winner in the world by taking part in the earthquake simulation project based on the Sunway TaihuLight supercomputer.

Mr. Wei Zhang is a current Ph.D student in the Department of Computer Science at Texas Tech University. His research interests include distributed data management, graph data management, storage systems, high performance computing. He started his Ph.D study from 2016 and now has published one paper about distributed streaming graph partitioning at CCGrid 2018 and another paper about distributed index construction at PACT 2018. Since 2017, Mr. Wei Zhang joined the collaboration with Scientific Data Management Group at the Computational Research Division of Lawrence Berkeley National Laboratory. He is now working on a project related to metadata search over scientific datasets.

Wenqiang Zhang is a Ph.D. candidate majored Geophysics in School of Earth and Space Sciences, University of Science and Technology of China. He is working on numerical simulation of destructive earthquakes, seismic hazard estimation, and is particularly interested in the physics of earthquake source including spontaneous rupture process and earthquake cycling.

Wusheng Zhang received his ME and PhD from Tianjin University and Tsinghua University, in 2007 and 2010. He focus on research of parallel and distributed computing. Currently is charge of system and application management of WuXi National Super Computing Center.

Zhenguo Zhang is an assistant professor working in Department of Earth and Space Sciences, Southern University of Science and Technology. His researches includes modeling seismic wave propagation in complex media, rupture dynamics and earthquake disasters.

Dr. Wenlai Zhao is a PostDoctoral researcher in Tsinghua University and is leading the distributed machine learning research group in the National Supercomputing Center in Wuxi (NSCCWX). He got his Ph.D. degree from Department of Computer Science and Technology in Tsinghua University. His research interest is heterogeneous parallel computing and distributed machine learning.