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Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

Peng Di, Ding Ye, Yu Su, Yulei Sui, Jingling Xue
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
International Conference on Parallel Processing (ICPP’12), 2012
@article{di2012automatic,

   title={Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs},

   author={Di, Peng and Ye, Ding and Su, Yu and Sui, Yulei and Xue, Jingling},

   year={2012}

}

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Automatically parallelizing loop nests into CUDA kernels must exploit the full potential of GPUs to obtain high performance. One state-of-the-art approach makes use of the polyhedral model to extract parallelism from a loop nest by applying a sequence of affine transformations to the loop nest. However, how to automate this process to exploit both intraand inter-SM parallelism for GPUs remains a challenging problem. Presently, compilers may generate code significantly slower than hand-optimized code for certain applications. This paper describes a compiler framework for tiling and parallelizing loop nests with uniform dependences into CUDA code. We aim to improve two levels of wavefront parallelism. We find tiling hyperplanes by embedding parallelismenhancing constraints in the polyhedral model to maximize intra-tile, i.e., intra-SM parallelism. This improves the load balance among the SPs in an SM executing a wavefront of loop iterations within a tile. We eliminate parallelism-hindering false dependences to maximize inter-tile, i.e., inter-SM parallelism. This improves the load balance among the SMs executing a wavefront of tiles. Our approach has been implemented in PLUTO and validated using eight benchmarks on two different NVIDIA GPUs (C1060 and C2050). Compared to PLUTO, our approach achieves 2 – 5.5X speedups across the benchmarks. Compared to highly hand-optimized 1-D Jacobi (3 points), 2-D Jacobi (5 points), 3-D Jacobi (7 points) and 3-D Jacobi (27 points), our speedups, 1.17X, 1.41X, 0.97X and 0.87X with an average of 1.10X on C1060 and 1.24X, 1.20X, 0.86X and 0.95X with an average of 1.06X on C2050, are competitive.
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