When adapting GPU-specific OpenCL kernels to run on multi-core/many-core cpus, coarsening the thread granularity is necessary and thus extensively used. However, locality concerns exposed in GPU-specific OpenCL code a...
详细信息
ISBN:
(数字)9783319098739
ISBN:
(纸本)9783319098739;9783319098722
When adapting GPU-specific OpenCL kernels to run on multi-core/many-core cpus, coarsening the thread granularity is necessary and thus extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the cpu performance. When executing GPU-specific kernels on cpus, local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns by using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for cpus by removing all the unwanted local-memory arrays together with the obsolete barrier statements. Experiments show that the automated transformation can satisfactorily improve OpenCL kernel performances on Sandy Bridge cpu and Intel's many-Integrated-core coprocessor.
OpenCL is an open heterogeneous programming framework. Although OpenCL programs are func- tionally portable, they do not provide performance portability, so code transformation often plays an irreplaceable role. When ...
详细信息
OpenCL is an open heterogeneous programming framework. Although OpenCL programs are func- tionally portable, they do not provide performance portability, so code transformation often plays an irreplaceable role. When adapting GPU-specific OpenCL kernels to run on multi-core/many-core cpus, coarsening the thread granularity is necessary and thus has been extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the cpu performance. Typi- cally, the use of OpenCL＇s local memory on multi-core/many-core cpus may lead to an opposite performance effect, because local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for cpus by （1） removing all the unwanted local-memory arrays together with the obsolete barrier statements and （2） optimizing the coalesced kernel code with vectorization and locality re-exploitation. Moreover, we have developed an automated tool chain that makes this transformation of GPU-specific OpenCL kernels into a cpu-friendly form, which is accompanied with a scheduler that forms a new OpenCL runtime. Experiments show that the automated transformation can improve OpenCL kernel performance on a multi-corecpu by an average factor of 3.24. Satisfactory performance improvements axe also achieved on Intel＇s many-integrated-core coprocessor. The resultant performance on both architectures is better than or comparable with the corresponding OpenMP performance.
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