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Title: Identifying and Exploiting Spatial Regularity in Data Memory References

Abstract

The growing processor/memory performance gap causes the performance of many codes to be limited by memory accesses. If known to exist in an application, strided memory accesses forming streams can be targeted by optimizations such as prefetching, relocation, remapping, and vector loads. Undetected, they can be a significant source of memory stalls in loops. Existing stream-detection mechanisms either require special hardware, which may not gather statistics for subsequent analysis, or are limited to compile-time detection of array accesses in loops. Formally, little treatment has been accorded to the subject; the concept of locality fails to capture the existence of streams in a program's memory accesses. The contributions of this paper are as follows. First, we define spatial regularity as a means to discuss the presence and effects of streams. Second, we develop measures to quantify spatial regularity, and we design and implement an on-line, parallel algorithm to detect streams - and hence regularity - in running applications. Third, we use examples from real codes and common benchmarks to illustrate how derived stream statistics can be used to guide the application of profile-driven optimizations. Overall, we demonstrate the benefits of our novel regularity metric as a low-cost instrument to detect potentialmore » for code optimizations affecting memory performance.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
15004432
Report Number(s):
UCRL-JC-154597
TRN: US201015%%625
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Conference
Resource Relation:
Conference: Supercomputing 2003, Phoenix, AZ, Nov 15 - Nov 21, 2003
Country of Publication:
United States
Language:
English
Subject:
99; ALGORITHMS; BENCHMARKS; DESIGN; DETECTION; METRICS; PERFORMANCE; STATISTICS; VECTORS

Citation Formats

Mohan, T, de Supinski, B R, McKee, S A, Mueller, F, Yoo, A, and Schulz, M. Identifying and Exploiting Spatial Regularity in Data Memory References. United States: N. p., 2003. Web. doi:10.1145/1048935.1050199.
Mohan, T, de Supinski, B R, McKee, S A, Mueller, F, Yoo, A, & Schulz, M. Identifying and Exploiting Spatial Regularity in Data Memory References. United States. doi:10.1145/1048935.1050199.
Mohan, T, de Supinski, B R, McKee, S A, Mueller, F, Yoo, A, and Schulz, M. Thu . "Identifying and Exploiting Spatial Regularity in Data Memory References". United States. doi:10.1145/1048935.1050199. https://www.osti.gov/servlets/purl/15004432.
@article{osti_15004432,
title = {Identifying and Exploiting Spatial Regularity in Data Memory References},
author = {Mohan, T and de Supinski, B R and McKee, S A and Mueller, F and Yoo, A and Schulz, M},
abstractNote = {The growing processor/memory performance gap causes the performance of many codes to be limited by memory accesses. If known to exist in an application, strided memory accesses forming streams can be targeted by optimizations such as prefetching, relocation, remapping, and vector loads. Undetected, they can be a significant source of memory stalls in loops. Existing stream-detection mechanisms either require special hardware, which may not gather statistics for subsequent analysis, or are limited to compile-time detection of array accesses in loops. Formally, little treatment has been accorded to the subject; the concept of locality fails to capture the existence of streams in a program's memory accesses. The contributions of this paper are as follows. First, we define spatial regularity as a means to discuss the presence and effects of streams. Second, we develop measures to quantify spatial regularity, and we design and implement an on-line, parallel algorithm to detect streams - and hence regularity - in running applications. Third, we use examples from real codes and common benchmarks to illustrate how derived stream statistics can be used to guide the application of profile-driven optimizations. Overall, we demonstrate the benefits of our novel regularity metric as a low-cost instrument to detect potential for code optimizations affecting memory performance.},
doi = {10.1145/1048935.1050199},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2003},
month = {7}
}

Conference:
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