Online Memory Access Pattern Analysis on an Application Profiling Tool

As memory subsystems have become complex in the state of the art system architectures, application program codes required to be optimized targeting to their deeper memory hierarchy for rewarding their performance. To support such optimizations, we are developing a memory access pattern analysis tool...

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Bibliographic Details
Published in2014 Second International Symposium on Computing and Networking pp. 602 - 604
Main Authors Matsubara, Yuki, Sato, Yukinori
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Algorithm design and analysis
Benchmark testing
Heuristic algorithms
Memory access pattern
Memory management
Online profiling
Optimization
Pattern analysis
Runtime
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ISSN2379-1888
DOI10.1109/CANDAR.2014.86

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Summary:As memory subsystems have become complex in the state of the art system architectures, application program codes required to be optimized targeting to their deeper memory hierarchy for rewarding their performance. To support such optimizations, we are developing a memory access pattern analysis tool. In this paper, we present the methodology how we detect memory access patterns on-the-fly on an execution-driven application analysis tool called Exana. First, we implement an offline trace file based method using a Python script code and verify its functionalities. Then, in order to improve its analysis speed, the code is ported to C++ language programs and integrated in the Exana. We evaluate the time and memory usage for the analysis of each implementation. From the results, we confirmed our online implementation can process faster than the offline trace file based method.
ISSN:2379-1888
DOI:10.1109/CANDAR.2014.86