scholarly journals Towards Task-Parallel Reductions in OpenMP

2015 ◽  
pp. 189-201 ◽  
Author(s):  
Jan Ciesko ◽  
Sergi Mateo ◽  
Xavier Teruel ◽  
Xavier Martorell ◽  
Eduard Ayguadé ◽  
...  
Keyword(s):  
Task Parallel

JPF-HJ: A Tool for Task Parallel Program Analysis

2019 ◽  
Vol 44 (4) ◽  
pp. 19-19 ◽  
Author(s):  
Joshua Hooker ◽  
Peter Aldous ◽  
Eric Mercer ◽  
Benjamin Ogles ◽  
Kyle Storey ◽  
...  
Keyword(s):  
Program Analysis ◽  
Parallel Program ◽  
Task Parallel

scholarly journals Scheduling Task-parallel Applications in Dynamically Asymmetric Environments

2020 ◽  
Author(s):  
Jing Chen ◽  
Pirah Noor Soomro ◽  
Mustafa Abduljabbar ◽  
Madhavan Manivannan ◽  
Miquel Pericas
Keyword(s):  
Parallel Applications ◽  
Task Parallel

scholarly journals Characterizing and Mitigating Work Time Inflation in Task Parallel Programs

2013 ◽  
Vol 21 (3-4) ◽  
pp. 123-136 ◽  
Author(s):  
Stephen L. Olivier ◽  
Bronis R. de Supinski ◽  
Martin Schulz ◽  
Jan F. Prins
Keyword(s):  
Poor Performance ◽  
Data Access ◽  
Parallel Applications ◽  
Task Parallelism ◽  
Improve Performance ◽  
Additional Time ◽  
Work Time ◽  
Task Parallel ◽  
Time Required ◽  
Data Access Latency

Task parallelism raises the level of abstraction in shared memory parallel programming to simplify the development of complex applications. However, task parallel applications can exhibit poor performance due to thread idleness, scheduling overheads, andwork time inflation– additional time spent by threads in a multithreaded computation beyond the time required to perform the same work in a sequential computation. We identify the contributions of each factor to lost efficiency in various task parallel OpenMP applications and diagnose the causes of work time inflation in those applications. Increased data access latency can cause significant work time inflation in NUMA systems. Our locality framework for task parallel OpenMP programs mitigates this cause of work time inflation. Our extensions to the Qthreads library demonstrate that locality-aware scheduling can improve performance up to 3X compared to the Intel OpenMP task scheduler.

scholarly journals Extreme-scale scripting: Opportunities for large task-parallel applications on petascale computers

2009 ◽  
Vol 180 ◽  
pp. 012046 ◽  
Author(s):  
Michael Wilde ◽  
Ioan Raicu ◽  
Allan Espinosa ◽  
Zhao Zhang ◽  
Ben Clifford ◽  
...  
Keyword(s):  
Parallel Applications ◽  
Task Parallel ◽  
Extreme Scale
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