IWOMP 2020 – Conference Program

In this tutorial, we’ll undertake an interactive exploration of the common core of OpenMP. We will cover through discussion and demos the 20 features of OpenMP programmers use all the time. This will give you what you need to become an OpenMP programmer. It will also prepare you for the more advanced OpenMP tutorials later in the day.The tutorial will be based on the C programming language, though Fortran programmers should be able to keep up as we avoid the more complex corners of C. The contents of the tutorial focusses on novice OpenMP programmers, but more experienced OpenMP programmers will benefit as well; they’ll just need to download the exercises (I’ll provide a link to a GitHub repository) so they can explore on their own during the tutorial, adding all the complexity they need to stay engaged.

Tim Mattson is a parallel programmer obsessed with every variety of science (Ph.D. Chemistry, UCSC, 1985). He is a senior principal engineer in Intel’s parallel computing lab.Tim has been with Intel since 1993 and has worked with brilliant people on great projects including: (1) the first TFLOP computer (ASCI Red), (2) MPI, OpenMP and OpenCL, (3) two different research processors (Intel’s TFLOP chip and the 48 core SCC), (4) Data management systems (Polystore systems and Array-based storage engines), and (5) the GraphBLAS API for expressing graph algorithms as sparse linear algebra.Tim is passionate about teaching. He’s been teaching OpenMP longer than anyone on the planet with OpenMP tutorials at every SC’XY conference but one since 1998. He has published five books on different aspects of parallel computing, the latest (Published November 2019) titled “The OpenMP Common Core: making OpenMP Simple Again”.

This tutorial will cover several advanced topics of OpenMP:

• NUMA Aware programming
• Vectorization / SIMD
• Advanced Tasking

Dr. Michael Klemm is part of the Datacenter Ecosystem Engineering
organization of the Intel Architecture, Graphics and Software group. His focus is on High Performance and Throughput Computing.  He holds an M.Sc.  in Computer Science and a Doctor of Engineering degree (Dr.-Ing.) in Computer Science from the Friedrich-Alexander-University Erlangen-Nuremberg, Germany.  Michael’s research focus is on compilers and runtime optimizations for distributed systems.  His areas of interest include compiler construction, design of programming languages, parallel programming, and performance analysis and tuning.  In 2016, Michael was appointed Chief Executive Officer of the OpenMP Architecture Review Board.

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Dr. Christian Terboven is a senior scientist and leads the HPC group at RWTH Aachen University. His research interests center around Parallel Programming and related Software Engineering aspects. Dr. Terboven has been involved in the Analysis, Tuning and Parallelization of several large-scale simulation codes for various architectures. He is responsible for several research projects in the area of programming models and approaches to improve the productivity and efficiency of modern HPC systems.

While most HPC developers have MPI experience, many are not familiar with the latest OpenMP features and how to use them in their codes. Modern OpenMP can be used in creative ways to map parallelism to current and emerging parallel architectures. This  tutorial that describes recent and new features of OpenMP for accelerators,  with a focus on those features that have proven important applications.The OpenMP 5.1 specification is scheduled for release at SC’20 with exciting new functionality that improves support for Accelerators.Thus it is important not only for developers to be aware of the current standard, and what is available today, but also what is coming next and what will be available in the exascale time frame (2021).The tutorial is expected to cover the following topics:

• Overview of what is available today in OpenMP 5.0 for GPUs
• An overview of the accelerator programming model
  • Examples on how to map data structures
  • How to exploit the parallelism in the target regions
• How to use OpenMP target and tasking constructs to manage and orchestrate work and communication between the CPUs and accelerators and inter-node communication.
• Some examples of success stories on how applications have used OpenMP to leverage GPUs on HPC systems.
• Other uses: Using OpenMP offload via other frameworks Raja/Kokkos
• A deeper introduction to OpenMP 5.1 and preview of  the latest features in the new spec to manage memory for heterogenous shared memory spaces, unified addresses / memories, deep copy, detach tasks, etc.

Oscar Hernandez received a Phd in Computer Science from the University of Houston. He is a senior staff member of the Computer Science Research (CSR) Group, which supports the Programming Environment and Tools for the Oak Ridge Leadership Computing Facility (OLCF). He represents ORNL in many specifications such as OpenACC, OpenMP, and benchmarking bodies SPEC/HPG. He is currently part of the SOLLVE ECP team and is the application liaison for the project. At ORNL he also works closely with application teams including the CAAR and INCITE efforts and constantly interacts with them to address their programming model and tools needs via HPC software ecosystems. He is currently working on the programming environment for Summit and works very closely with the vendors to track the evolution of the next-generation programming models. He has worked on many projects funded by DOE, DoD, NSF, and Industrial Partners in the Oil & Gas industry.

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Thomas R. W. Scogland received his PhD degree in computer science from Virginia Tech in 2014. He is a computer scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research interests include parallel programming models, heterogeneous computing and resource management at scale. He serves on the OpenMP Language Committee, the WG14-C and WG21-C++ committees, and as co-chair of the Green500.

Abstract.  In this talk we will look at four software packages for dense linear algebra. These packages have been developed over time.  Some of these packages are intended as research vehicles and others as production tools.  The four packages are: BALLISTIC to sustain LAPACK and ScaLAPACK packages.  PLASMA research into multicore implementations.  MAGMA research into GPU implementations. SLATE dense linear algebra for Exascale machines.  We will look at how OpenMP is being used in each.

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Speaker Profile: Jack Dongarra holds an appointment 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;
• in 2013 he received the ACM/IEEE Ken Kennedy Award;
• in 2019 he received the ACM/SIAM Computational Science and Engineering Prize, and in 2020 he received the IEEE Computer Pioneer Award.

He is a Fellow of the AAAS, ACM, IEEE, and SIAM and a foreign member of the Russian Academy of Science, a foreign member of the British Royal Society, and a member of the US National Academy of Engineering.

Abstract. Flang is a new Fortran frontend in the LLVM project. While Flang is a work in progress, when finished it will provide comprehensive support for the latest Fortran and OpenMP standards. Flang uses the new LLVM MLIR technology to generate Fortran and OpenMP codes. This talk will give an overview of status and describe how OpenMP support is being implemented in Flang and MLIR.Flang is a new Fortran frontend in the LLVM project. While Flang is a work in progress, when finished it will provide comprehensive support for the latest Fortran and OpenMP standards. Flang uses the new LLVM MLIR technology to generate Fortran and OpenMP codes. This talk will give an overview of status and describe how OpenMP support is being implemented in Flang and MLIR.

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Speaker Profile. David Truby is a senior software engineer at Arm. His latest project has been adding OpenMP support for the LLVM Flang Fortran compiler. He is a PhD candidate at the University of Warwick, with his research focusing on performance portability frameworks and parallel programming models, and has previously worked with IBM Research on OpenMP target offload support in the Clang compiler.

Abstract. This talk will cover Mythic’s hybrid mixed-signal computing architecture and unique software development tools, including a Deep Neural Network (DNN) graph compiler. In addition, some ideas will be proposed on how OpenMP can be used to program this type of architecture. Mythic’s Intelligence Processing Units (IPUs) combine analog compute-in-memory acceleration with digital processing elements. They are designed for high-performance and power-efficient AI inference acceleration.

The Mythic IPU is a tile-based dataflow architecture. Each tile has an analog compute array, flash memory for weight storage, local SRAM memory, a single-instruction multiple-data (SIMD) unit, and a control processor. The tiles are interconnected with an efficient on-chip router network.  Mythic has built a unique suite of development tools, including a DNN graph compiler, to enable the rapid deployment of AI inference applications on the IPU. The tools perform such actions as mapping DNNs to tiles, setting up dataflow conditions, and analog-aware program transformations.

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Speaker Profile: Eric Stotzer (Ph.D. University of Houston, 2010) is a Fellow and Director of Compiler Research at Mythic.  He is currently working on developing new programming models for mixed-signal AI inference accelerators. Eric was previously with Texas Instruments for 31 years where he was a Distinguished Member Technical Staff. Over the years, he has worked on compilers and tools for micro-controllers and digital signal processors as well as hardware/software co-design efforts for new processor architectures. Eric was the TI representative on the OpenMP Architecture Review Board and co-chair of the OpenMP accelerator subcommittee. Eric is a co-author of Using OpenMP – The Next Step, The MIT Press.

Abstract. In his keynote, Mark will discuss AMD’s commitment to High Performance Computing, an open ecosystem and how OpenMP fits squarely in AMD’s product strategy to enable heterogeneous computing. He will also take a closer look at AMD’s approach to optimize for performance while maintaining compatibility with multiple programming languages, and heterogeneous architectures. He will also touch on the expected impact for Machine Learning and future Exascale supercomputers. .

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Speaker Profile:

Mark Papermaster is Chief Technology Officer and Executive Vice President of Technology and Engineering at AMD and is responsible for corporate technical direction, product development including system-on-chip (SOC) methodology, microprocessor design, I/O and memory and advanced research. He led the re-design of engineering processes at AMD and the development of the award-winning “Zen” high-performance x86 CPU family, high-performance GPUs and the company’s modular design approach, Infinity Fabric. He also oversees Information Technology that delivers AMD’s compute infrastructure and services.

His more than 35 years of engineering experience includes significant leadership roles managing the development of a wide range of products, from microprocessors to mobile devices and high-performance servers. Before joining AMD in October 2011 as Chief Technology Officer and Senior Vice President, Papermaster was the leader of Cisco’s Silicon Engineering Group, the organization responsible for silicon strategy, architecture, and development for the company’s switching and routing businesses. He served as Apple Senior Vice President of Devices Hardware Engineering, where he was responsible for iPod and iPhone hardware development. He also held a number of senior leadership positions at IBM overseeing development of the company’s key microprocessor and server technologies.

Papermaster received his bachelor’s degree from the University of Texas at Austin and master’s degree from the University of Vermont, both in Electrical Engineering. He is a long-term member of the University of Texas Cockrell School of Engineering Advisory Board, Olin College Presidents Council and the Juvenile Diabetes Research Foundation. Most recently, he was appointed to the CTO Forum Advisory Board and IEEE Industry Advisory Board.