Pagoda

Author:

Yeh Tsung Tai1,Sabne Amit2,Sakdhnagool Putt3,Eigenmann Rudolf4,Rogers Timothy G.5

Affiliation:

1. Purdue University, West Lafayette, IN, USA

2. Microsoft

3. National Electronics and Computer Technology Center

4. University of Delaware

5. Purdue University

Abstract

Massively multithreaded GPUs achieve high throughput by running thousands of threads in parallel. To fully utilize the their hardware, contemporary workloads spawn work to the GPU in bulk by launching large tasks, where each task is a kernel that contains thousands of threads that occupy the entire GPU. GPUs face severe underutilization and their performance benefits vanish if the tasks are narrow, i.e., they contain less than 512 threads. Latency-sensitive applications in network, signal, and image processing that generate a large number of tasks with relatively small inputs are examples of such limited parallelism. This article presents Pagoda, a runtime system that virtualizes GPU resources, using an OS-like daemon kernel called MasterKernel. Tasks are spawned from the CPU onto Pagoda as they become available, and are scheduled by the MasterKernel at the warp granularity. This level of control enables the GPU to keep scheduling and executing tasks as long as free warps are found, dramatically reducing underutilization. Experimental results on real hardware demonstrate that Pagoda achieves a geometric mean speedup of 5.52X over PThreads running on a 20-core CPU, 1.76X over CUDA-HyperQ, and 1.44X over GeMTC, the state-of-the-art runtime GPU task scheduling system.

Publisher

Association for Computing Machinery (ACM)

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Modelling and Simulation,Software

Cited by 4 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. GhOST: a GPU Out-of-Order Scheduling Technique for Stall Reduction;2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA);2024-06-29

2. ROSGM: A Real-Time GPU Management Framework with Plug-In Policies for ROS 2;2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS);2023-05

3. Dynamic GPU Scheduling with Multi-resource Awareness and Live Migration Support;IEEE Transactions on Cloud Computing;2023

4. A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT;IEEE Internet of Things Journal;2022-01-15

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