SRC13. Mapping Applications on Irregular Allocations
Author
Event Type
ACM Student Research Competition
Poster
TimeTuesday, November 15th10am - 5:15pm
LocationExhibit Hall E, Booth #104
DescriptionMapping applications on clusters becomes more difficult as the number of nodes become larger. Supercomputers assign allocations with irregular shapes to users to maximize the utilization of resources, and it is much more difficult to map applications on these irregular allocations.
We extended Rubik, a python based framework to map applications on irregular allocations with a few lines of python code. Rubik was originally designed for regular allocations, so we added features to handle allocations with irregular structure and unavailable nodes and two mapping algorithms such as row-ordering and recursive splitting.
We evaluate our work with two widely used HPC applications on Blue Waters: MILC and Qbox. We reduced execution time by 32.5% in MILC, and by 36.3% in Qbox, and communication time by 60% in MILC and 56% in Qbox.
We extended Rubik, a python based framework to map applications on irregular allocations with a few lines of python code. Rubik was originally designed for regular allocations, so we added features to handle allocations with irregular structure and unavailable nodes and two mapping algorithms such as row-ordering and recursive splitting.
We evaluate our work with two widely used HPC applications on Blue Waters: MILC and Qbox. We reduced execution time by 32.5% in MILC, and by 36.3% in Qbox, and communication time by 60% in MILC and 56% in Qbox.
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