SCHEDULE: NOV 10-16, 2012
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Code Generation for Parallel Execution of a Class of Irregular Loops on Distributed Memory Systems
SESSION: Runtime-Based Analysis and Optimization
EVENT TYPE: Papers
TIME: 4:00PM - 4:30PM
SESSION CHAIR: Siegfried Benkner
AUTHOR(S):Mahesh Ravishankar, John Eisenlohr, Louis-Noel Pouchet, J. Ramanujam, Atanas Rountev, P. Sadayappan
ROOM:355-D
ABSTRACT:
Parallelization and locality optimization of affine loop nests has been successfully addressed for shared-memory machines. However, many large-scale simulation applications must be executed in a distributed environment, and use irregular/sparse computations where the control-flow and array-access patterns are data-dependent.
In this paper, we propose an approach for effective parallel execution of a class of irregular loop computations in a distributed memory environment, using a combination of static and run-time analysis. We discuss algorithms that analyze sequential code to generate an inspector and an executor. The inspector captures the data-dependent behavior of the computation in parallel and without requiring
replication of any of the data structures used in the original computation. The executor performs the computation in parallel. The effectiveness of the framework is demonstrated on several benchmarks and a climate modeling application.
Chair/Author Details:
Siegfried Benkner (Chair) - University of Vienna
Mahesh Ravishankar - Ohio State University
John Eisenlohr - Ohio State University
Louis-Noel Pouchet - Ohio State University
J. Ramanujam - Louisiana State University
Atanas Rountev - Ohio State University
P. Sadayappan - Ohio State University
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Code Generation for Parallel Execution of a Class of Irregular Loops on Distributed Memory Systems
SESSION: Runtime-Based Analysis and Optimization
EVENT TYPE:
TIME: 4:00PM - 4:30PM
SESSION CHAIR: Siegfried Benkner
AUTHOR(S):Mahesh Ravishankar, John Eisenlohr, Louis-Noel Pouchet, J. Ramanujam, Atanas Rountev, P. Sadayappan
ROOM:355-D
ABSTRACT:
Parallelization and locality optimization of affine loop nests has been successfully addressed for shared-memory machines. However, many large-scale simulation applications must be executed in a distributed environment, and use irregular/sparse computations where the control-flow and array-access patterns are data-dependent.
In this paper, we propose an approach for effective parallel execution of a class of irregular loop computations in a distributed memory environment, using a combination of static and run-time analysis. We discuss algorithms that analyze sequential code to generate an inspector and an executor. The inspector captures the data-dependent behavior of the computation in parallel and without requiring
replication of any of the data structures used in the original computation. The executor performs the computation in parallel. The effectiveness of the framework is demonstrated on several benchmarks and a climate modeling application.
Chair/Author Details:
Siegfried Benkner (Chair) - University of Vienna
Mahesh Ravishankar - Ohio State University
John Eisenlohr - Ohio State University
Louis-Noel Pouchet - Ohio State University
J. Ramanujam - Louisiana State University
Atanas Rountev - Ohio State University
P. Sadayappan - Ohio State University
Click here to download .ics calendar file