Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation

Abstract

Small to medium enterprises (SMEs), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck in the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPUprogramming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, the existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and are hard to use. Little attentions have been paid to the applicability, usability, and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system (GPSME) for inexperienced users to utilize the GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with a new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with nonexpert GPU users in four SMEs reflect that a GPSME system can efficiently accelerate real-world applications with at least 4× and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.