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CNS Core:Small: Toward Automatic Virtualization of Accelerators

CNS Core：小：迈向加速器自动虚拟化

基本信息

批准号：
2006943
负责人：
Christopher Rossbach
金额：
$ 50万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006943&HistoricalAwards=false
关键词：
CNS Core Small Toward Automatic

项目摘要

The cloud has transformed computing and society over the past decade by providing fast on-demand access to scalable computing resources. At the same time, the end of Moore’s law has driven the emergence of a panoply of hardware accelerators such as GPUs, TPUs, and FPGAs that are critical to game-changing workloads such as machine learning. Unfortunately, cloud migration and hardware specialization are on a collision course: cloud applications run on virtual infrastructure, but practical virtualization techniques for accelerators remains an open and challenging problem. Solutions offered by cloud providers sacrifice the consolidation benefits of virtualization and waste energy. With data centers projected to consume up to 20% of the world's total power by 2025, improving performance and efficiency of cloud infrastructure is becoming critical to the health of our planet.The proposed research will address key obstacles that limit efficiency and wide adoption of hardware acceleration in the cloud, focusing on two questions. The first is how to build practical, efficient virtualization support for emerging hardware-accelerators. The second is how to make accelerators of the future more efficiently virtualizable. The PI will develop language, OS, and runtime techniques to automate the construction of virtual accelerator support. Applying these techniques to cloud accelerators is a significant research challenge. The PI will explore techniques to dramatically improve virtualization support provided directly by accelerators and will develop language-level virtualization support for reconfigurable accelerators such as field programmable gate arrays. By bringing efficient virtualization support to cloud accelerators and cloud services that use accelerators, the proposed research will bring power-efficient cloud computing to new problems and domains, and reduce energy waste.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的十年里，云通过提供对可扩展计算资源的快速按需访问，改变了计算和社会。与此同时，摩尔定律的终结推动了GPU、TPU和FPGA等一系列硬件加速器的出现，这些加速器对机器学习等改变游戏规则的工作负载至关重要。不幸的是，云迁移和硬件专业化正在发生冲突：云应用程序在虚拟基础设施上运行，但加速器的实用虚拟化技术仍然是一个开放且具有挑战性的问题。云提供商提供的解决方案牺牲了虚拟化的整合优势，并浪费了能源。预计到2025年，数据中心的耗电量将占全球总耗电量的20%，提高云基础设施的性能和效率对地球的健康至关重要。拟议的研究将解决限制云计算效率和硬件加速广泛采用的关键障碍，重点关注两个问题。首先是如何为新兴的硬件加速器构建实用、高效的虚拟化支持。第二个是如何让未来的加速器更有效地虚拟化。 PI将开发语言、操作系统和运行时技术，以自动构建虚拟加速器支持。将这些技术应用于云加速器是一项重大的研究挑战。PI将探索技术，以显着提高加速器直接提供的虚拟化支持，并将开发语言级的虚拟化支持可重新配置的加速器，如现场可编程门阵列。通过为云加速器和使用加速器的云服务提供高效的虚拟化支持，将高能效的云计算应用于新的问题和领域，并减少能源浪费。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Design, implementation, and application of GPU-based Java bytecode interpreters

基于GPU的Java字节码解释器的设计、实现与应用

DOI：
10.1145/3360603
发表时间：
2019
期刊：
Proceedings of the ACM on Programming Languages
影响因子：
0
作者：
Celik, Ahmet;Nie, Pengyu;Rossbach, Christopher J.;Gligoric, Milos
通讯作者：
Gligoric, Milos

Disaggregated GPU Acceleration for Serverless Applications

DOI：
10.1145/3606557.3606560
发表时间：
2023-06
期刊：
ACM SIGOPS Operating Systems Review
影响因子：
0
作者：
Henrique Fingler;Zhiting Zhu;Esther Yoon;Zhipeng Jia;Emmett Witchel;C. Rossbach
通讯作者：
Henrique Fingler;Zhiting Zhu;Esther Yoon;Zhipeng Jia;Emmett Witchel;C. Rossbach

Parla: a Python orchestration system for heterogeneous architectures

Parla：用于异构架构的 Python 编排系统

DOI：
发表时间：
2022
期刊：
Storage and Analysis
影响因子：
0
作者：
Lee, Hochan;Ruys, William;Henriksen, Ian;Peters, Arthur;Yan, Yineng;Stephens, Sean;You, Bozhi;Fingler, Henrique;Burtscher, Martin;Gligoric, Milos
通讯作者：
Gligoric, Milos

DGSF: Disaggregated GPUs for Serverless Functions

DGSF：用于无服务器功能的分解 GPU

DOI：
10.1109/ipdps53621.2022.00077
发表时间：
2022
期刊：
IEEE International Parallel and Distributed Processing Symposium
影响因子：
0
作者：
Fingler, Henrique;Zhu, Zhiting;Yoon, Esther;Jia, Zhipeng;Witchel, Emmett
通讯作者：
Witchel, Emmett

Compiler-driven FPGA virtualization with SYNERGY

使用 SYNERGY 进行编译器驱动的 FPGA 虚拟化

DOI：
10.1145/3445814.3446755
发表时间：
2021
期刊：
Architectural Support for Programming Languages and Operating Systems
影响因子：
0
作者：
Landgraf, Joshua;Yang, Tiffany;Lin, Will;Rossbach, Christopher J.;Schkufza, Eric
通讯作者：
Schkufza, Eric