Autotuning of OpenCL Kernels with Global Optimizations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F17%3A00098442" target="_blank" >RIV/00216224:14610/17:00098442 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/citation.cfm?doid=3152821.3152877" target="_blank" >https://dl.acm.org/citation.cfm?doid=3152821.3152877</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3152821.3152877" target="_blank" >10.1145/3152821.3152877</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Autotuning of OpenCL Kernels with Global Optimizations

Popis výsledku v původním jazyce

Autotuning is an important method for automatically exploring code optimizations. It may target low-level code optimizations, such as memory blocking, loop unrolling or memory prefetching, as well as high-level optimizations, such as placement of computation kernels on proper hardware devices, optimizing memory transfers between nodes or between accelerators and main memory. In this paper, we introduce an autotuning method, which extends state-of-the-art low-level tuning of OpenCL or CUDA kernels towards more complex optimizations. More precisely, we introduce a Kernel Tuning Toolkit (KTT), which implements inter-kernel global optimizations, allowing to tune parameters affecting multiple kernels or also the host code. We demonstrate on practical examples, that with global kernel optimizations we are able to explore tuning options that are not possible if kernels are tuned separately. Moreover, our tuning strategies can take into account numerical accuracy across multiple kernel invocations and search for implementations within specific numerical error bounds.

Název v anglickém jazyce

Autotuning of OpenCL Kernels with Global Optimizations

Popis výsledku anglicky

Autotuning is an important method for automatically exploring code optimizations. It may target low-level code optimizations, such as memory blocking, loop unrolling or memory prefetching, as well as high-level optimizations, such as placement of computation kernels on proper hardware devices, optimizing memory transfers between nodes or between accelerators and main memory. In this paper, we introduce an autotuning method, which extends state-of-the-art low-level tuning of OpenCL or CUDA kernels towards more complex optimizations. More precisely, we introduce a Kernel Tuning Toolkit (KTT), which implements inter-kernel global optimizations, allowing to tune parameters affecting multiple kernels or also the host code. We demonstrate on practical examples, that with global kernel optimizations we are able to explore tuning options that are not possible if kernels are tuned separately. Moreover, our tuning strategies can take into account numerical accuracy across multiple kernel invocations and search for implementations within specific numerical error bounds.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_013%2F0001802" target="_blank" >EF16_013/0001802: CERIT Scientific Cloud</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

1st Workshop on Autotuning and Adaptivity Approaches for Energy Efficient HPC Systems (ANDARE'2017)

ISBN

9781450353632

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

ACM

Místo vydání

Portland (USA)

Místo konání akce

Portland, Oregon, USA

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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