Locality‐Conscious Nested‐Loops Parallelization |
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Authors: | Saeed Parsa Mohammad Hamze |
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Affiliation: | Saeed Parsa (phone: +98 912 100118, parsa@iust.ac.ir), and Mohammad Hamzei (hamzei@iust.ac.ir) are with the School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. |
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Abstract: | To speed up data‐intensive programs, two complementary techniques, namely nested loops parallelization and data locality optimization, should be considered. Effective parallelization techniques distribute the computation and necessary data across different processors, whereas data locality places data on the same processor. Therefore, locality and parallelization may demand different loop transformations. As such, an integrated approach that combines these two can generate much better results than each individual approach. This paper proposes a unified approach that integrates these two techniques to obtain an appropriate loop transformation. Applying this transformation results in coarse grain parallelism through exploiting the largest possible groups of outer permutable loops in addition to data locality through dependence satisfaction at inner loops. These groups can be further tiled to improve data locality through exploiting data reuse in multiple dimensions. |
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Keywords: | Automatic nested loops parallelization data locality loop tiling |
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