Recent technologies, like general purpose computing GPU, have a major limitation consisting in the difficulties that developers face when implementing parallel code using device-oriented languages. This paper aims to assist developers by automatically producing snippets of code handling GPU-oriented tasks. Our proposed approach is based on Aspect-Oriented-Programming and generates modules in CUDA C compliant code, which are encapsulated and connected by means of JNI. By means of a set of predefined functions we separate the application code from device-dependent concerns, including device memory allocation and management. Moreover, bandwidth utilisation and cores occupancy is automatically handled in order to minimise the overhead caused by host to device communications and the computational imbalance, which often tampers with the effective speedup of a GPU parallelised code.
An AO system for OO-GPU programming / Fornaia, A; Napoli, C; Pappalardo, G; Tramontana, E. - 1382:(2015), pp. 24-31. (Intervento presentato al convegno 16th Workshop on From Object to Agents, WOA 2015 tenutosi a Naples; Italy).
An AO system for OO-GPU programming
Napoli C
;
2015
Abstract
Recent technologies, like general purpose computing GPU, have a major limitation consisting in the difficulties that developers face when implementing parallel code using device-oriented languages. This paper aims to assist developers by automatically producing snippets of code handling GPU-oriented tasks. Our proposed approach is based on Aspect-Oriented-Programming and generates modules in CUDA C compliant code, which are encapsulated and connected by means of JNI. By means of a set of predefined functions we separate the application code from device-dependent concerns, including device memory allocation and management. Moreover, bandwidth utilisation and cores occupancy is automatically handled in order to minimise the overhead caused by host to device communications and the computational imbalance, which often tampers with the effective speedup of a GPU parallelised code.File | Dimensione | Formato | |
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