Griffon – GPU Programming APIs for Scientific and General Purpose Computing (Extended Version)
Pisit Makpaisit, Worawan Marurngsith · 2012
Applications can accelerate up to hundreds of times faster by offloading some computation from CPU to execute at graphical processing units (GPUs). This technique is so called the general-purpose computation on graphic processing units (GPGPUs). Recent research on accelerating various applications by GPGPUs using a programming model from NVIDIA, called Compute Unified Device Architecture (CUDA), have shown significant improvement on performance results. However, writing an efficient CUDA program requires in-depth understanding of GPU architecture in order to develop a suitable data-parallel strategy, and to express it in a low-level style of code. Thus, CUDA programming is still considered complex and error-prone. This paper proposes a new set of application program interfaces (APIs), called Griffon, and its compiler framework for automatic translation of C programs to CUDA-based programs. Griffon APIs allow programmers to exploit the performance of multicore machines using OpenMP and offloads computations to GPUs using Griffon directives. The compiler framework uses a new graph algorithm for efficiently exploiting data locality. Experimental results on a 16-core NVIDIA Geforce 8400M GS using six workloads show that Griffon-based programs can accelerate from 1.5 up to 89 times faster than their sequential implementation running on CPU.