Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance

Abstract

We present Task Bench, a parameterized benchmark designed to explore the performance of parallel and distributed programming systems under a variety of application scenarios. Task Bench lowers the barrier to benchmarking 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.We conduct a comprehensive study with implementations of Task Bench in 15 programming systems on up to 256 Haswell nodes of the Cori supercomputer. We introduce a novel metric, minimum effective task granularity to study the baseline runtime overhead of each system. We show that when running at scale, 100 µs is the smallest granularity that even the most efficient systems can reliably support with current technologies. We also study each system's scalability, ability to hide communication and mitigate load imbalance.

Runtime vs Scheduler: Analyzing Dask's Overheads

“…The author of DASK has performed a series of DASK scaling microbenchmarks [17]. In [18], the authors benchmark several distributed task systems on a set of fundamental task graph shapes to compare their relative overhead and scaling properties. They find out that DASK stops scaling relatively quickly if the task granularity is smaller than ca.…”