  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.
