University of Illinois Urbana-Champaign

Analysis of communication and computation overlap in accelerated programs

Yellapragada, Sushma

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YELLAPRAGADA-THESIS-2022.pdf

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https://hdl.handle.net/2142/115786

Description

Title
Analysis of communication and computation overlap in accelerated programs
Author(s)
Yellapragada, Sushma
Issue Date
2022-04-27
Director of Research (if dissertation) or Advisor (if thesis)
Snir, Marc
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Graphics Processing Units (GPU(s))
  • Compute Unified Device Architecture (CUDA)
Abstract
Accelerated computing has revolutionized a broad range of industries by applying Graphics Processing Units (GPU(s)) to optimize workloads for efficient performance. Any application that is designed to run on such specialized devices has a typical flow that begins and ends with data movement between Central Processing Unit (CPU) and GPU. Previous research has shown that this data movement is indeed a major bottleneck with even most optimized applications hitting only 30% of the peak performance [1]. While most recent efforts have been to manually tune communication to improve performance, in this work, we present three different general optimization techniques to introduce overlap between communication and computation. The optimized model thus developed on a stencil application is evaluated against the baseline model using various performance metrics including time and throughput. Sets of experiments performed on two different GPUs reported that a certain combination of these optimizations result in a maximum speedup of 6.5 and a 160% increase in bandwidth utilization when compared to the baseline model in Compute Unified Device Architecture (CUDA).
Graduation Semester
2022-05
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/115786
Copyright and License Information
Copyright 2022 Sushma Yellapragada

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