Recently, hybrid architectures using accelerators like GPGPUs or the Cell processor have gained much interest in the HPC community. The RapidMind Multi-Core Development Platform is a programming environment that allows generating code which is able to seamlessly run on hardware accelerators like GPUs or the Cell processor and multicore CPUs both from AMD and Intel. This paper describes the ports of three mathematical kernels to RapidMind which are chosen as synthetic benchmarks and representatives of scientific codes. Performance of these kernels has been measured on various RapidMind backends (cuda, cell and x86) and compared to other - hardware-specific - implementations (using CUDA, Cell SDK and Intel MKL). The results give an insight in the degree of portability of RapidMind code and code performance across different architectures.
Recently, hybrid architectures using accelerators like GPGPUs or the Cell processor have gained much interest in the HPC community. The RapidMind Multi-Core Development Platform is a programming environment that allows generating code which is able to seamlessly run on hardware accelerators like GPUs or the Cell processor and multicore CPUs both from AMD and Intel. This paper describes the ports of three mathematical kernels to RapidMind which are chosen as synthetic benchmarks and representatives of scientific codes. Performance of these kernels has been measured on various RapidMind backends (cuda, cell and x86) and compared to other hardware-specific implementations (using CUDA, Cell SDK and Intel MKL). The results give an insight in the degree of portability of RapidMind code and code performance across different architectures.
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