NDLI: TaskInsight: Understanding Task Schedules Effects on Memory and Performance

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TaskInsight: Understanding Task Schedules Effects on Memory and Performance

Content Provider	ACM Digital Library
Author	Black-Schaffer, David Hugo, Andra Ceballos, Germán Grass, Thomas
Abstract	Recent scheduling heuristics for task-based applications have managed to improve their by taking into account memory-related properties such as data locality and cache sharing. However, there is still a general lack of tools that can provide insights into why, and where, different schedulers improve memory behavior, and how this is related to the applications' performance. To address this, we present TaskInsight, a technique to characterize the memory behavior of different task schedulers through the analysis of data reuse between tasks. TaskInsight provides high-level, quantitative information that can be correlated with tasks' performance variation over time to understand data reuse through the caches due to scheduling choices. TaskInsight is useful to diagnose and identify which scheduling decisions affected performance, when were they taken, and why the performance changed, both in single and multi-threaded executions. We demonstrate how TaskInsight can diagnose examples where poor scheduling caused over 10% difference in performance for tasks of the same type, due to changes in the tasks' data reuse through the private and shared caches, in single and multi-threaded executions of the same application. This flexible insight is key for optimization in many contexts, including data locality, throughput, memory footprint or even energy efficiency.
Starting Page	11
Ending Page	20
Page Count	10
File Format	PDF
ISBN	9781450348836
DOI	10.1145/3026937.3026943
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data locality Data reuse Task-based scheduling
Content Type	Text
Resource Type	Article

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