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Isoefficiency in Practice: Configuring and Understanding the Performance of Task-based Applications

Shudler, Sergei ; Calotoiu, Alexandru ; Hoefler, Torsten ; Wolf, Felix (2017)
Isoefficiency in Practice: Configuring and Understanding the Performance of Task-based Applications.
22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Austin, USA (04.-08.02.2017)
doi: 10.1145/3018743.3018770
Conference or Workshop Item, Bibliographie

Abstract

Task-based programming offers an elegant way to express units of computation and the dependencies among them, making it easier to distribute the computational load evenly across multiple cores. However, this separation of problem decomposition and parallelism requires a sufficiently large input problem to achieve satisfactory efficiency on a given number of cores. Unfortunately, finding a good match between input size and core count usually requires significant experimentation, which is expensive and sometimes even impractical. In this paper, we propose an automated empirical method for finding the isoefficiency function of a task-based program, binding efficiency, core count, and the input size in one analytical expression. This allows the latter two to be adjusted according to given (realistic) efficiency objectives. Moreover, we not only find (i) the actual isoefficiency function but also (ii) the function one would yield if the program execution was free of resource contention and (iii) an upper bound that could only be reached if the program was able to maintain its average parallelism throughout its execution. The difference between the three helps to explain low efficiency, and in particular, it helps to differentiate between resource contention and structural conflicts related to task dependencies or scheduling. The insights gained can be used to co-design programs and shared system resources.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Shudler, Sergei ; Calotoiu, Alexandru ; Hoefler, Torsten ; Wolf, Felix
Type of entry: Bibliographie
Title: Isoefficiency in Practice: Configuring and Understanding the Performance of Task-based Applications
Language: English
Date: 26 January 2017
Place of Publication: New York
Publisher: ACM
Book Title: PPoPP'17: Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Event Title: 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Event Location: Austin, USA
Event Dates: 04.-08.02.2017
DOI: 10.1145/3018743.3018770
Abstract:

Task-based programming offers an elegant way to express units of computation and the dependencies among them, making it easier to distribute the computational load evenly across multiple cores. However, this separation of problem decomposition and parallelism requires a sufficiently large input problem to achieve satisfactory efficiency on a given number of cores. Unfortunately, finding a good match between input size and core count usually requires significant experimentation, which is expensive and sometimes even impractical. In this paper, we propose an automated empirical method for finding the isoefficiency function of a task-based program, binding efficiency, core count, and the input size in one analytical expression. This allows the latter two to be adjusted according to given (realistic) efficiency objectives. Moreover, we not only find (i) the actual isoefficiency function but also (ii) the function one would yield if the program execution was free of resource contention and (iii) an upper bound that could only be reached if the program was able to maintain its average parallelism throughout its execution. The difference between the three helps to explain low efficiency, and in particular, it helps to differentiate between resource contention and structural conflicts related to task dependencies or scheduling. The insights gained can be used to co-design programs and shared system resources.

Uncontrolled Keywords: BMBF|01IH13001, DFG|SPPEXA 1648, DoE|DE-SC0015524
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Parallel Programming
Zentrale Einrichtungen
Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ)
Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ) > Hochleistungsrechner
Date Deposited: 20 Apr 2018 12:23
Last Modified: 28 May 2024 13:46
PPN: 518695220
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