Designing Scalable Synthetic Compact Applications for Benchmarking High Productivity Computing Systems


One of the main objectives of the DARPA High Productivity Computing Systems (HPCS) program is to reassess the way we define and measure performance, programmability, portability, robustness and ultimately productivity in the High Performance Computing (HPC) domain. This article describes the Scalable Synthetic Compact Applications (SSCA) benchmark suite, a community product delivered under support of the DARPA HPCS program. The SSCA benchmark suite consists of six benchmarks. The first three SSCA benchmarks are specified and described in this article. The last three are to be developed and will relate to simulation. SSCA #1 Bioinformatics Optimal Pattern Matching stresses integer and character operations (no floating point required) and is compute-limited; SSCA #2 Graph Analysis stresses memory access, uses integer operations, is compute-intensive, and is hard to parallelize on most modern systems; and SSCA #3 Synthetic Aperture Radar Application is computationally taxing, seeks a high rate at which answers are generated, and contains a significant file I/O component. These SSCA benchmarks are envisioned to emerge as complements to current scalable micro-benchmarks and complex real applications to measure high-end productivity and system performance. They are also described in sufficient detail to drive novel HPC programming paradigms, as well as architecture development and testing.

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