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Ookami is a computer technology testbed supported by the United States National Science Foundation. It provides researchers with access to the A64FX processor developed by Fujitsu in collaboration with RIK{\Xi}N for the Japanese path to exascale computing, as deployed in Fugaku, the fastest computer in the world. By focusing on crucial architectural details, the ARM-based, multi-core, 512-bit SIMD-vector processor with ultrahigh-bandwidth memory promises to retain familiar and successful programming models while achieving very high performance for a wide range of applications. We review relevant technology and system details, and the main body of the paper focuses on initial experiences with the hardware and software ecosystem for micro-benchmarks, mini-apps, and full applications, and starts to answer questions about where such technologies fit into the NSF ecosystem., Comment: 14 pages, 7 figures, PEARC '21: Practice and Experience in Advanced Research Computing, July 18--22, 2021, Boston, MA, USA

Alternative programming models and runtimes are increasing in popularity and maturity. This allows porting and comparing, on competitive grounds, emerging parallel approaches against the traditional MPI+X paradigm. In this work, an implementation of distributed task-based stencil computation is compared with a traditional MPI+X implementation of the same application. The Legion task-based parallel programming system is used as an alternative to MPI, but the underlying OpenMP approach is kept at the subdomain level. Overall results are promising toward making this alternative method competitive to the traditional MPI approach. In future work, extensions to other applications will be explored, as well as the use of GPUs.

The development of the A64FX processor by Fujitsu has created a massive innovation in High-Performance Computing and the birth of Fugaku: the current world’s fastest supercomputer. A variety of tools are used to analyze the run-times and performances of several applications, and in particular, how these applications scale on the A64FX processor. We examine the performance and behavior of applications through OpenMP scaling and how their performance differs across different compilers both on the new Ookami cluster at Stony Brook University as well as the Fugaku supercomputer at RIKEN in Japan.

Simulations based on stencil computations (widely used in geosciences) have been dominated by the MPI+OpenMP programming model paradigm. Little effort has been devoted to experimenting with task-based parallelism in this context. We address this by introducing OpenMP task parallelism into the kernel of an industrial seismic modeling code, Minimod. We observe that even for these highly regular stencil computations, taskified kernels are competitive with traditional OpenMP-augmented loops, and in some experiments tasks even outperform loop parallelism.