Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation
Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation
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通过以用户为中心的自动代码翻译提高 GPU 在加速工业应用中的效用
DOI：
10.1109/tii.2017.2731362
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发表时间：
2018
影响因子：
12.3
通讯作者：
Geyong Min
中科院分区：
文献类型：
--
作者：
Po Yang;Feng Dong;V. Codreanu;David Williams;J. Roerdink;Baoquan Liu;A. Anvari‐Moghaddam;Geyong Min
中小型企业（中小型企业），特别是那些专注于开发创新作品的企业，受到许多应用程序的计算速度的主要瓶颈的限制。领域，但由于缺乏专业的GPUROGRAMING技能，在没有经验的用户中，GPU功率的爆炸率尚未完全利用现有的CPU-GPU代码转换器主要是为了研究目的而设计的，用户界面设计差，很难使用这些工具，对普通用户的适用性，可用性和可学习性。我们提出了一个在线自动化的CPU-to-GPU源翻译系统（GPSME），用于没有经验的用户来加速GPU功能。使用新的内核生成方案和内存管理层次结构实现指令模型，以优化其性能。 GPSME系统可以有效地加速使用至少4倍的现实世界应用，并且比相比具有更好的适用性，可用性和可学习性现有的自动CPU至GPU源翻译器。
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.