NDLI: Efficient measurement of data flow enabling communication-aware parallelisation

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Efficient measurement of data flow enabling communication-aware parallelisation

Content Provider	ACM Digital Library
Author	Stroobandt, Dirk Bertels, Peter Heirman, Wim
Abstract	As multicore chips scale to higher processor counts, communication between cores becomes more and more important. Indeed, when a single application is split up among multiple cores, which are connected through a relatively slow network, the amount of communication that is required will have an essential effect on performance. Therefore, if the application can be partitioned in such a way that communication between threads is minimised, or that placement on non-uniform networks can be performed with regards to communication, a significant performance boost can be obtained. But to do this effectively, communication streams inside the application must be known. In this paper, we introduce a profiling tool for Java that can measure data flows between methods. It constructs a communication graph, which combines a traditional call graph with data flow information. The overhead of profiling is brought down by a factor of 15 through the use of reservoir sampling. We prove that this can be done with a limited decrease in accuracy. This way, we can quickly estimate communication flows, which forms the critical information that allows an efficient communication-aware parallelisation to be made.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781605584072
DOI	10.1145/1463768.1463776
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-11-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data-flow Profiling
Content Type	Text
Resource Type	Article

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