Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations

A trend that has materialized, and has given rise to much attention, is of the increasingly heterogeneous computing platforms. Presently, it has become very common for a desktop or a notebook computer to come equipped with both a multi-core CPU and a GPU. Capitalizing on the maximum computational power of such architectures (i.e., by simultaneously exploiting both the multi-core CPU and the GPU) starting from a high-level API is a critical challenge. We believe that it would be highly desirable to support a simple way for programmers to realize the full potential of today's heterogeneous machines.

This paper describes a compiler and runtime framework that can map a class of applications, namely those characterized by generalized reductions, to a system with a multi-core CPU and GPU. Starting with simple C functions with added annotations, we automatically generate the middleware API code for the multi-core, as well as CUDA code to exploit the GPU simultaneously. The runtime system provides efficient schemes for dynamically partitioning the work between CPU cores and the GPU. Our experimental results from two applications, e.g., k-means clustering and Principal Component Analysis (PCA), show that, through effectively harnessing the heterogeneous architecture, we can achieve significantly higher performance compared to using only the GPU or the multi-core CPU. In k-means, the heterogeneous version with 8 CPU cores and a GPU achieved a speedup of about 32.09x relative to 1-thread CPU. When compared to the faster of CPU-only and GPU-only executions, we were able to achieve a performance gain of about 60%. In PCA, the heterogeneous version attained a speedup of 10.4x relative to the 1-thread CPU version. When compared to the faster of CPU-only and GPU-only versions, we achieved a performance gain of about 63.8%.