%0 Book Section %1 Unkule2012-gp %A Unkule, Swapneela %A Shaltz, Christopher %A Qasem, Apan %B Compiler Construction %D 2012 %I Springer Berlin Heidelberg %K Expose %P 21--40 %T Automatic Restructuring of GPU Kernels for Exploiting Inter-thread Data Locality %X Hundreds of cores per chip and support for fine-grain multithreading have made GPUs a central player in today's HPC world. For many applications, however, achieving a high fraction of peak on current GPUs, still requires significant programmer effort. A key consideration for optimizing GPU code is determining a suitable amount of work to be performed by each thread. Thread granularity not only has a direct impact on occupancy but can also influence data locality at the register and shared-memory levels. This paper describes a software framework to analyze dependencies in parallel GPU threads and perform source-level restructuring to obtain GPU kernels with varying thread granularity. The framework supports specification of coarsening factors through source-code annotation and also implements a heuristic based on estimated register pressure that automatically recommends coarsening factors for improved memory performance. We present preliminary experimental results on a select set of CUDA kernels. The results show that the proposed strategy is generally able to select profitable coarsening factors. More importantly, the results demonstrate a clear need for automatic control of thread granularity at the software level for achieving higher performance.

@incollection{Unkule2012-gp, abstract = {Hundreds of cores per chip and support for fine-grain multithreading have made GPUs a central player in today's HPC world. For many applications, however, achieving a high fraction of peak on current GPUs, still requires significant programmer effort. A key consideration for optimizing GPU code is determining a suitable amount of work to be performed by each thread. Thread granularity not only has a direct impact on occupancy but can also influence data locality at the register and shared-memory levels. This paper describes a software framework to analyze dependencies in parallel GPU threads and perform source-level restructuring to obtain GPU kernels with varying thread granularity. The framework supports specification of coarsening factors through source-code annotation and also implements a heuristic based on estimated register pressure that automatically recommends coarsening factors for improved memory performance. We present preliminary experimental results on a select set of CUDA kernels. The results show that the proposed strategy is generally able to select profitable coarsening factors. More importantly, the results demonstrate a clear need for automatic control of thread granularity at the software level for achieving higher performance.}, added-at = {2015-06-08T14:28:31.000+0200}, author = {Unkule, Swapneela and Shaltz, Christopher and Qasem, Apan}, biburl = {https://www.bibsonomy.org/bibtex/27bb8eb311c9352a74921f80da17ae762/christophv}, booktitle = {Compiler Construction}, interhash = {068b436f834ec7b9694b4f14b8a76380}, intrahash = {7bb8eb311c9352a74921f80da17ae762}, keywords = {Expose}, pages = {21--40}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2016-01-04T14:22:08.000+0100}, title = {Automatic Restructuring of {GPU} Kernels for Exploiting Inter-thread Data Locality}, year = 2012 }