{"id":347771,"date":"2017-01-05T22:17:26","date_gmt":"2017-01-06T06:17:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=347771"},"modified":"2018-10-16T19:59:17","modified_gmt":"2018-10-17T02:59:17","slug":"verification-producer-consumer-synchronization-gpu-programs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/verification-producer-consumer-synchronization-gpu-programs\/","title":{"rendered":"Verification of producer-consumer synchronization in GPU programs."},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Previous efforts to formally verify code written for GPUs have focused solely on kernels written within the traditional data-parallel GPU programming model. No previous work has considered the higher performance, but more complex, warp-specialized kernels based on producer-consumer named barriers available on current hardware. In this work we present the first formal operational semantics for named barriers and define what it means for a warp-specialized kernel to be correct. We give algorithms for verifying the correctness of warp-specialized kernels and prove that they are both sound and complete for the most common class of warp-specialized programs. We also present WEFT, a verification tool for checking warp-specialized code. Using WEFT, we discover several non-trivial bugs in production warp-specialized kernels.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Previous efforts to formally verify code written for GPUs have focused solely on kernels written within the traditional data-parallel GPU programming model. No previous work has considered the higher performance, but more complex, warp-specialized kernels based on producer-consumer named barriers available on current hardware. 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