TY - JOUR
T1 - Improving Utility of GPU in Accelerating Industrial Applications With User-Centered Automatic Code Translation
AU - Yang, Po
AU - Dong, Feng
AU - Codreanu, Valeriu
AU - Williams, David
AU - Roerdink, Jos B. T. M.
AU - Liu, Baoquan
AU - Anvari-Moghaddam, Amjad
AU - Min, Geyong
PY - 2018/4
Y1 - 2018/4
N2 - 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 4x and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.
AB - 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 4x and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.
KW - Automatic translation
KW - graphics processing unit (GPU)
KW - parallel computing
KW - usability
KW - FRAMEWORK
KW - OPTIMIZER
KW - GPGPU
U2 - 10.1109/TII.2017.2731362
DO - 10.1109/TII.2017.2731362
M3 - Article
SN - 1551-3203
VL - 14
SP - 1347
EP - 1360
JO - Ieee transactions on industrial informatics
JF - Ieee transactions on industrial informatics
IS - 4
ER -