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Video: Preparing to program Aurora at Exascale – Early experiences and future directions

Hal Finkel from Argonne

In this video from IWOCL / SYCLcon 2020, Hal Finkel from Argonne National Laboratory presents: Preparing to program Aurora at Exascale – Early experiences and future directions.

Argonne National Laboratory’s Leadership Computing Facility will be home to Aurora, our first exascale supercomputer. Aurora promises to take scientific computing to a whole new level, and scientists and engineers from many different fields will take advantage of Aurora’s unprecedented computational capabilities to push the boundaries of human knowledge. In addition, Aurora’s support for advanced machine-learning and big-data computations will enable scientific workflows incorporating these techniques along with traditional HPC algorithms. Programming the state-of-the-art hardware in Aurora will be accomplished using state-of-the-art programming models. Some of these models, such as OpenMP, are long-established in the HPC ecosystem. Other models, such as Intel’s oneAPI, based on SYCL, are relatively-new models constructed with the benefit of significant experience. Many applications will not use these models directly, but rather, will use C++ abstraction libraries such as Kokkos or RAJA. Python will also be a common entry point to high-performance capabilities. As we look toward the future, features in the C++ standard itself will become increasingly relevant for accessing the extreme parallelism of exascale platforms.

This presentation will summarize the experiences of our team as we prepare for Aurora, exploring how to port applications to Aurora’s architecture and programming models, and distilling the challenges and best practices we’ve developed to date. oneAPI/SYCL and OpenMP are both critical models in these efforts, and while the ecosystem for Aurora has yet to mature, we’ve already had a great deal of success. Importantly, we are not passive recipients of programming models developed by others. Our team works not only with vendor-provided compilers and tools, but also develops improved open-source LLVM-based technologies that feed both open-source and vendor-provided capabilities. In addition, we actively participate in the standardization of OpenMP, SYCL, and C++. To conclude, I’ll share our thoughts on how these models can best develop in the future to support exascale-class systems.

Hal Finkel graduated from Yale University in 2011 with a Ph.D. in theoretical physics focusing on numerical simulation of early-universe cosmology. He’s now the Lead for Compiler Technology and Programming Languages at the ALCF. Hal has contributed to the LLVM compiler infrastructure project for many years and is currently the code owner of the PowerPC backend and the pointer-aliasing-analysis subsystem, among others. As part of DOE’s Exascale Computing Project (ECP), Hal is a PathForward technical lead, Co-PI for the PROTEAS-TUNE, Flang, Kokkos, and Proxy Apps projects, and a member of several other ECP-funded projects. Hal represents Argonne on the C++ Standards Committee and serves as vice-chair of the committee. He was the lead developer on the bgclang project, which provided LLVM/Clang on IBM Blue Gene/Q supercomputers. Hal also helps develop the Hardware/Hybrid Accelerated Cosmology Code (HACC), a two-time IEEE/ACM Gordon Bell Prize finalist. He has designed and implemented a tree-based force evaluation scheme and the I/O subsystem and contributed to many other HACC components.

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