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2023

2022

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2020

<table border=1 align=center width=80%> Abstract: 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. </table>

[bibtex-key = 10.5555/3433701.3433783]

2019

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2013

<table border=1 align=center width=80%> Abstract: In this paper, we present some solutions to handle to problems commonly encountered when dealing with fine grain parallelization on multi-core architecture: expressing algorithm using a task grain size suitable for the hardware and minimizing the time penalty due to Non Uniform Memory Accesses. To evaluate the benefit of our work we present some experiments on the fine grain parallelization of an iterative solver for spare linear system with some comparisons with the Intel TBB approach. </table>

[bibtex-key = rossignon:hal-00858350]

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