GSI: a GPU stall inspector to characterize the sources of memory stalls for tightly coupled GPUs

Alsop, Johnathan R.

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https://hdl.handle.net/2142/90587 Copy

Description

Title

GSI: a GPU stall inspector to characterize the sources of memory stalls for tightly coupled GPUs

Author(s)

Alsop, Johnathan R.

Issue Date

2016-04-20

Director of Research (if dissertation) or Advisor (if thesis)

Adve, Sarita V.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• GPUs
• performance analysis
• memory systems
• heterogeneous computing

Abstract

In recent years the power wall has prevented the continued scaling of single core performance. This has led to the rise of dark silicon and motivated a move toward parallelism and specialization. As a result, energy-efficient high-throughput GPU cores are increasingly favored for accelerating data-parallel applications. However, the best way to efficiently communicate and synchronize across heterogeneous cores remains an important open research question. Many methods have been proposed to improve the efficiency of heterogeneous memory systems, but current methods for evaluating the performance effects of these innovations are limited in their ability to attribute differences in execution time to sources of latency in the memory system. Performance characterization of tightly coupled CPU-GPU systems is complicated by the high levels of parallelism present in GPU codes. Existing simulation tools provide only coarse-grained metrics which can obscure the underlying memory system interactions that cause performance differences. In this thesis we introduce GPU Stall Inspector (GSI), a method for identifying and visualizing the causes of GPU stalls with a focus on a tightly coupled CPU-GPU memory subsystem. We demonstrate the utility of our approach by evaluating the sources of stalls in several recent architectural innovations for tightly coupled, heterogeneous CPU-GPU systems.

Graduation Semester

2016-05

Type of Resource

text

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http://hdl.handle.net/2142/90587

Copyright and License Information

Copyright 2016 Johnathan Alsop

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