Equalizer: Dynamic Tuning of GPU Resources for Efficient Execution

GPUs use thousands of threads to provide high performance and efficiency. In general, if one thread of a kernel uses one of the resources (compute, bandwidth, data cache) more heavily, there will be significant contention for that resource due to the large number of identical concurrent threads. This contention will eventually saturate the performance of the kernel due to contention for the bottleneck resource, while at the same time leaving other resources underutilized. To overcome this problem, a runtime system that can tune the hardware to match the characteristics of a kernel can effectively mitigate the imbalance between resource requirements of kernels and the hardware resources present on the GPU. We propose Equalizer, a low overhead hardware runtime system, that dynamically monitors the resource requirements of a kernel and manages the amount of onchip concurrency, core frequency and memory frequency to adapt the hardware to best match the needs of the running kernel. Equalizer provides efficiency in two modes. Firstly, it can save energy without significant performance degradation by throttling under-utilized resources. Secondly, it can boost bottleneck resources to reduce contention and provide higher performance without significant energy increase. Across a spectrum of 27 kernels, Equalizer achieves 15% savings in energy mode and 22% speedup in performance mode.