Number of Tasks, not Threads, is Key

The concept of task already exists in many parallel programming models. Programmers express parallelism by defining tasks in their applications, and runtime libraries schedule tasks on threads. However, in many task-based parallel programming models, choosing the right number of threads is still key...

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Bibliographic Details
Published in:Proceedings - Euromicro Workshop on Parallel and Distributed Processing pp. 128 - 136
Main Authors: Tousimojarad, Ashkan, Vanderbauwhede, Wim
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.03.2015
Subjects:
Benchmark testing
Benchmarks
Cilk Plus
Conferences
GPRM
Instruction sets
Kernel
Libraries
Manycore
Networks
OpenMP
Optimization
Parallel processing
Parallel programming
Parallelism
Programmers
Programming
Runtime
Schedules
Task
Tasks
TBB
Thread
TILEPro64
Xeon Phi
ISSN:1066-6192
Online Access:Get full text
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Summary:The concept of task already exists in many parallel programming models. Programmers express parallelism by defining tasks in their applications, and runtime libraries schedule tasks on threads. However, in many task-based parallel programming models, choosing the right number of threads is still key to performance. Hence, the onus is on the programmer to decide not only about the number of tasks, but also about the optimal number of threads in order to get good performance. In this paper, we aim to show that desirable performance can be achieved by only focusing on tasks. For this purpose, we compare a purely task-centric parallel programming model called GPRM with three popular approaches (OpenMP, Intel Cilk Plus, and TBB) on two modern many core systems, the Tilera TILEPro64 and Intel Xeon Phi, which have respectively 64 and 60 physical cores integrated into a single chip. We have chosen three benchmarks with different characteristics to show that a task-centric approach such as GPRM can facilitate parallel programming while it outperforms other models in most cases. It does so by controlling only the number of tasks, rather than having to tune the number of threads.
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SourceType-Conference Papers & Proceedings-2
ISSN:1066-6192
DOI:10.1109/PDP.2015.81