Enhancing Autotuning Capability with a History Database-Reference-Cited by-同舟云学术

Enhancing Autotuning Capability with a History Database

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)
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Author:

Cho Younghyun¹,Demmel James W.¹,Li Xiaoye S.²,Liu Yang²,Luo Hengrui²

Affiliation:

1. University of California, Berkeley,Department of Electrical Engineering and Computer Sciences,Berkeley,CA,USA

2. Lawrence Berkeley National Laboratory,Computational Research Division,Berkeley,CA,USA

Funder

National Nuclear Security Administration

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9691770/9691932/09691973.pdf?arnumber=9691973

Reference18 articles.

1. Collective knowledge: Organizing Research Projects as a Database of Reusable Components and Portable Workflows with Common APIs;fursin;ArXiv Preprint,2020

2. Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization

3. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

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