Speculative Execution of Parallel Programs with Precise Exception Semantics on GPUs

General purpose computing on GPUs (GPGPU) can enable significant performance and energy improvements for certain classes of applications. However, current GPGPU programming models, such as CUDA and OpenCL, are only accessible by systems experts through lowlevel C/C++ APIs. In contrast, large numbers of programmers use highlevel languages, such as Java, due to their productivity advantages of type safety, managed runtimes and precise exception semantics. Current approaches to enabling GPGPU computing in Java and other managed languages involve low-level interfaces to native code that compromise the semantic guarantees of managed languages, and are not readily accessible to mainstream programmers. In this paper, we propose compile-time and runtime technique for accelerating Java programs with automatic generation of OpenCL while preserving precise exception semantics. Our approach includes (1) automatic generation of OpenCL kernels and JNI glue code from a Java-based parallel-loop construct (forall), (2) speculative execution of OpenCL kernels on GPUs, and (3) automatic generation of optimized and parallel exception-checking code for execution on the CPU. A key insight in supporting our speculative execution is that the GPU’s device memory is separate from the CPU’s main memory, so that, in the case of a misspeculation (exception), any side effects in a GPU kernel can be ignored by simply not communicating results back to the CPU. We demonstrate the efficiency of our approach using eight Java benchmarks on two GPU-equipped platforms. Experimental results show that our approach can significantly accelerate certain classes of Java programs on GPUs while maintaining precise exception semantics.