|Title||Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance|
|Publication Type||Conference Paper|
|Year of Publication||2020|
|Authors||Slaughter, E., W. Wu, Y. Fu, L. Brandenburg, N. Garcia, W. Kautz, E. Marx, K. S. Morris, Q. Cao, G. Bosilca, S. Mirchandaney, W. Lee, S. Treichler, P. McCormick, and A. Aiken|
|Conference Name||International Conference for High Performance Computing Networking, Storage, and Analysis (SC20)|
We present Task Bench, a parameterized benchmark designed to explore the performance of distributed programming systems under a variety of application scenarios. Task Bench dramatically lowers the barrier to benchmarking and comparing multiple programming systems by making the implementation for a given system orthogonal to the benchmarks themselves: every benchmark constructed with Task Bench runs on every Task Bench implementation. Furthermore, Task Bench's parameterization enables a wide variety of benchmark scenarios that distill the key characteristics of larger applications.
To assess the effectiveness and overheads of the tested systems, we introduce a novel metric, minimum effective task granularity (METG). We conduct a comprehensive study with 15 programming systems on up to 256 Haswell nodes of the Cori supercomputer. Running at scale, 100μs-long tasks are the finest granularity that any system runs efficiently with current technologies. We also study each system's scalability, ability to hide communication and mitigate load imbalance.
Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance
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