GTC 2020: Enabling 800 Projects for GPU-Accelerated Science on Perlmutter at NERSC
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Enabling 800 Projects for GPU-Accelerated Science on Perlmutter at NERSC
Jonathan Madsen, Lawrence Berkeley National Laboratory | Jack Deslippe, NERSC | Muaaz Awan, Lawrence Berkeley National Laboratory
The National Energy Research Scientific Computing Center (NERSC) is the mission HPC center for the U.S. Department of Energy Office of Science and supports the needs of 800+ projects and 7,000+ scientists with advanced HPC and data capabilities. NERSC's newest system, Perlmutter, is an upcoming Cray system with heterogeneous nodes including AMD CPUs and NVIDIA Volta-Next GPUs. It will be the first NERSC flagship system with GPUs. Preparing our diverse user base for the new system is a critical part of making the system successful in enabling science at scale. The NERSC Exascale Science Application Program is responsible for preparing the simulation, data, and machine learning workloads to take advantage of the new architecture. We'll outline our strategy to enable our users to take advantage of the new architecture in a performance-portable way and discuss early outcomes. We'll highlight our use of tools and performance models to evaluate application readiness for Perlmutter and how we effectively frame the conversation about GPU optimization with our wide user base. In addition, we'll highlight a number of activities we are undertaking in order to make Perlmutter a more productive system when it arrives through compiler, library, and tool development. We'll also cover outcomes from a series of case studies that demonstrate our strategy to enable users to take advantage of the new architecture. We'll discuss the programming model used to port codes to GPUs, the strategy used to optimize code bottlenecks, and the GPU vs. CPU speedup achieved so far. The codes will include Tomopy (tomographic reconstruction), Exabiome (genomics de novo assembly), and AMReX (Adaptive Mesh Refinement software framework).