CAPA: Collaborative Research: ARION: Taming Heterogeneity with DSLs, Approximation, and Synthesis
CAPA: Collaborative Research: ARION: Taming Heterogeneity with DSLs, Approximation, and Synthesis
批准号:
2217878
负责人:
Jonathan Ragan-Kelley
金额:
$30.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-10-01 至 2023-11-30
中文摘要
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英文摘要
Specialization and the arrival of new technologies are key forces motivating heterogeneous systems. Heterogeneity is already in use widely, with public clouds offering instances that are heterogeneous in both compute capabilities and storage. This project identifies the following forces that will make systems heterogeneous beyond just compute and storage, complicating programming and compilation beyond the challenges that we face today. This project develops Arion, a system for compiling programs onto heterogeneous platforms based on several unifying ideas. The Arion system will be evaluated on practically relevant workloads ranging from computer vision and virtual reality, to graph computations, machine learning and stream processing. The investigators will work with partners in industry to transfer research results to products, and the tools and software developed by this project will be released as open source.The research in this project relies on four unifying ideas. The first thrust explores schedules and type systems separate a program's specification from its implementation strategy, enabling performance portability because one can select, without changing the program, its parallelism, locality, and hardware mapping. The second thrust uses domain-specific languages to describe not only programs but also artifacts used during compilation, such as schedules, resource-, and memory consistency models. This allows automatic synthesis of these artifacts. The third thrust uses resource models to bring scheduling and synthesis to large programs because the target program need not be scheduled or synthesized all at once. Instead, the compiler makes high-level decisions by estimating performance using a model before committing to low-level decisions. Finally, the investigators will use formal methods to lift programs into, and verify and synthesize programs in our DSLs, providing a high degree of automation. The verifiers and synthesizers are automatically generated from descriptions of DSLs.
期刊论文(7)
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DOI:
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发表时间:
2019-09
期刊:
影响因子:
--
作者:
[Kartik Chandra;Audrey Xie;Jonathan Ragan-Kelley;E. Meijer]
通讯作者:
Kartik Chandra;Audrey Xie;Jonathan Ragan-Kelley;E. Meijer
DOI:
10.1145/3519939.3523446
发表时间:
2022-06
期刊:
Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation
影响因子:
--
作者:
[Yuka Ikarashi;G. Bernstein;Alex Reinking;Hasan Genç;Jonathan Ragan-Kelley]
通讯作者:
Yuka Ikarashi;G. Bernstein;Alex Reinking;Hasan Genç;Jonathan Ragan-Kelley
DOI:
10.1145/3508461
发表时间:
2022-03
期刊:
ACM Transactions on Graphics (TOG)
影响因子:
--
作者:
[Karima Ma;Michaël Gharbi;Andrew Adams;Shoaib Kamil;Tzu-Mao Li;Connelly Barnes;Jonathan Ragan-Kelley]
通讯作者:
Karima Ma;Michaël Gharbi;Andrew Adams;Shoaib Kamil;Tzu-Mao Li;Connelly Barnes;Jonathan Ragan-Kelley
DOI:
10.1145/3485486
发表时间:
2020-12
期刊:
Proceedings of the ACM on Programming Languages
影响因子:
--
作者:
[Luke Anderson;Andrew Adams;Karima Ma;Tzu-Mao Li;Tian Jin;Jonathan Ragan-Kelley]
通讯作者:
Luke Anderson;Andrew Adams;Karima Ma;Tzu-Mao Li;Tian Jin;Jonathan Ragan-Kelley
DOI:
10.1145/3528233.3530715
发表时间:
2022-04
期刊:
ACM SIGGRAPH 2022 Conference Proceedings
影响因子:
--
作者:
[Kartik Chandra;Tzu-Mao Li;J. Tenenbaum;Jonathan Ragan-Kelley]
通讯作者:
Kartik Chandra;Tzu-Mao Li;J. Tenenbaum;Jonathan Ragan-Kelley
共 7 条
OAC Core: OAC Core Projects: GPU Geometric Data Processing
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批准号:2403239
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项目类别:Standard Grant
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资助金额:$60.0万
-
财政年份:2024
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负责人:Jonathan Ragan-Kelley
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依托单位:
CAREER: The Exocompiler: Decoupling Algorithms from the Organization of Computation and Data
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批准号:2328543
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项目类别:Continuing Grant
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资助金额:$52.54万
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财政年份:2023
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负责人:Jonathan Ragan-Kelley
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依托单位:
CAREER: The Exocompiler: Decoupling Algorithms from the Organization of Computation and Data
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批准号:1846502
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项目类别:Continuing Grant
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资助金额:$52.54万
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财政年份:2019
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负责人:Jonathan Ragan-Kelley
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依托单位:
CAPA: Collaborative Research: ARION: Taming Heterogeneity with DSLs, Approximation, and Synthesis
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批准号:1723445
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项目类别:Continuing Grant
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资助金额:$30.0万
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财政年份:2017
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负责人:Jonathan Ragan-Kelley
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依托单位:
海外基金