Optimizing a GPU algorithm through hardware profiling analysis
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ArticleDescription: 1 archivo (606,2 KB)Subject(s): Online resources: Summary: Usage of GPU-based architectures for scientific computing has been steadily increasing in the last years. This new paradigm for both programming and execution has been applied to solve several classic problems much faster than using the conventional multiprocessor and/or multicomputer approach. These architectures allow an increase in performance - compared to conventional CPU processors - for specific types of algorithms that are particularly suitable for its greater number of simpler cores which execute one single instruction at a time, each one for different sets of data. Since this is still a relative new technology, GPU device manufacturers as well as independent researchers have published several experiences (success stories), best practices, and optimization guides to aid developers for obtaining the maximum program performance. However, there is still little information about the possible optimizations that can only be harnessed by analyzing the specific device's hardware performance counters. In this paper, we discuss several optimizations based on hardware profiling and share our learned lessons about how such data can be used to optimize a scientific algorithm on a GPU using CUDA.
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Biblioteca de la Facultad de Informática | Biblioteca digital | A0659 (Browse shelf(Opens below)) | Link to resource | Recurso en Línea |
Formato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
Usage of GPU-based architectures for scientific computing has been steadily increasing in the last years. This new paradigm for both programming and execution has been applied to solve several classic problems much faster than using the conventional multiprocessor and/or multicomputer approach. These architectures allow an increase in performance - compared to conventional CPU processors - for specific types of algorithms that are particularly suitable for its greater number of simpler cores which execute one single instruction at a time, each one for different sets of data. Since this is still a relative new technology, GPU device manufacturers as well as independent researchers have published several experiences (success stories), best practices, and optimization guides to aid developers for obtaining the maximum program performance. However, there is still little information about the possible optimizations that can only be harnessed by analyzing the specific device's hardware performance counters. In this paper, we discuss several optimizations based on hardware profiling and share our learned lessons about how such data can be used to optimize a scientific algorithm on a GPU using CUDA.
International Conference on Computational Science and Computational Intelligence (2014 mar. 10-13 : Las Vegas). Proceedings, vol. 1, pp. 45-51