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Graphics processors, or GPUs, have recently been widely used as acceleratorsin the shared environments such as clusters and clouds. In such sharedenvironments, many kernels are submitted to GPUs from different users, andthroughput is an important metric for performance and total ownership cost.Despite the recently improved runtime support for concurrent GPU kernelexecutions, the GPU can be severely underutilized, resulting in suboptimalthroughput. In this paper, we propose Kernelet, a runtime system with dynamicslicing and scheduling techniques to improve the throughput of concurrentkernel executions on the GPU. With slicing, Kernelet divides a GPU kernel intomultiple sub-kernels (namely slices). Each slice has tunable occupancy to allowco-scheduling with other slices and to fully utilize the GPU resources. Wedevelop a novel and effective Markov chain based performance model to guide thescheduling decision. Our experimental results demonstrate up to 31.1% and 23.4%performance improvement on NVIDIA Tesla C2050 and GTX680 GPUs, respectively.

Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling