A reconfigurable fabric for accelerating large-scale datacenter services

Author:

Putnam Andrew1,Caulfield Adrian M.1,Chung Eric S.1,Chiou Derek2,Constantinides Kypros3,Demme John4,Esmaeilzadeh Hadi5,Fowers Jeremy1,Gopal Gopi Prashanth1,Gray Jan1,Haselman Michael1,Hauck Scott6,Heil Stephen1,Hormati Amir7,Kim Joo-Young1,Lanka Sitaram1,Larus James8,Peterson Eric1,Pope Simon1,Smith Aaron1,Thong Jason1,Xiao Phillip Yi1,Burger Doug1

Affiliation:

1. Microsoft

2. Microsoft and University of Texas at Austin

3. Microsoft and Amazon Web Services

4. Microsoft and Columbia University

5. Microsoft and Georgia Institute of Technology

6. Microsoft and University of Washington

7. Google, Inc.

8. Microsoft and École Polytechnique Fédérale de Lausanne (EPFL)

Abstract

Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we have designed and built a composable, reconfigurablefabric to accelerate portions of large-scale software services. Each instantiation of the fabric consists of a 6x8 2-D torus of high-end Stratix V FPGAs embedded into a half-rack of 48 machines. One FPGA is placed into each server, accessible through PCIe, and wired directly to other FPGAs with pairs of 10 Gb SAS cables In this paper, we describe a medium-scale deployment of this fabric on a bed of 1,632 servers, and measure its efficacy in accelerating the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system when ranking candidate documents. Under high load, the largescale reconfigurable fabric improves the ranking throughput of each server by a factor of 95% for a fixed latency distribution--- or, while maintaining equivalent throughput, reduces the tail latency by 29%

Publisher

Association for Computing Machinery (ACM)

Reference29 articles.

1. Leap scratchpads

2. Maxwell - a 64 FPGA Supercomputer;Baxter R.;Engineering Letters,2008

Cited by 78 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Introduction to the Compute Express Link (CXL) Interconnect;ACM Computing Surveys;2024-07-08

2. SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration;Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays;2024-04

3. Memory Scraping Attack on Xilinx FPGAs: Private Data Extraction from Terminated Processes;2024 Design, Automation & Test in Europe Conference & Exhibition (DATE);2024-03-25

4. Post-configuration Activation of Hardware Trojans in FPGAs;Journal of Hardware and Systems Security;2024-03-13

5. Data Motion Acceleration: Chaining Cross-Domain Multi Accelerators;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3