Integrating Multi-GPU Execution in an OpenACC Compiler

GPUs have become promising computing devices in current and future computer systems due to its high performance, high energy efficiency, and low price. However, lack of high level GPU programming models hinders the wide spread of GPU applications. To resolve this issue, OpenACC is developed as the first industry standard of a directive-based GPU programming model and several implementations are now available. Although early evaluations of the OpenACC systems showed significant performance improvement with modest programming efforts, they also revealed the limitations of the systems. One of the biggest limitations is that the current OpenACC compilers do not automate the utilization of multiple GPUs. In this paper, we present an OpenACC compiler with the capability to execute single GPU OpenACC programs on multiple GPUs. By orchestrating the compiler and the runtime system, the proposed system can efficiently manage the necessary data movements among multiple GPUs memories. To enable advanced communication optimizations in the proposed system, we propose a small set of directives as extensions of OpenACC API. The directives allow programmers to express the patterns of memory accesses in the parallel loops to be offloaded. Inserting a few directives into an OpenACC program can reduce a large amount of unnecessary data movements and thus helps the proposed system drawing great performance from multi-GPU systems. We implemented and evaluated the prototype system on top of CUDA with three data parallel applications. The proposed system achieves up to 6.75x of the performance compared to OpenMP in the 1CPU with 2GPU machine, and up to 2.95x of the performance compared to OpenMP in the 2CPU with 3GPU machine. In addition, in two of the three applications, the multi-GPU OpenACC compiler outperforms the single GPU system where hand-written CUDA programs run.