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Divmaps Blog Detecting and removing divergences from CUDA kernels

The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data SIMD execution model. SIMD machines give application developers a tremendous computational power; however, the model also bring restrictions. In particular, processing elements PEs execute in lock-step, and might lose performance due to divergences in the programs control flow. In
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Divmaps Blog Detecting and removing divergences from CUDA kernels

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The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data SIMD execution model. SIMD machines give application developers a tremendous computational power; however, the model also bring restrictions. In particular, processing elements PEs execute in lock-step, and might lose performance due to divergences in the programs control flow. In

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The site had the following in the homepage, "Detecting and removing divergences from CUDA kernels." I noticed that the web site stated " To identify chains of similarities between two possibly divergent program paths, and weave these paths together as much as possible." They also stated " Our techniques on the Ocelot open-source CUDA compiler. And have tested it on many industrial-strength GPU benchmarks, including Rodinia. Our implementation is freely available. For a more concise tutorial, see the slides."

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