Abstract
Today's desktop PCs feature a variety of parallel processing units. Developing applications that exploit this parallelism is a demanding task, and a programmer has to obtain detailed knowledge about the hardware for efficient implementation. CGiS is a data-parallel programming language providing a unified abstraction for two parallel processing units: graphics processing units (GPUs) and the vector processing units of CPUs. The CGiS compiler framework fully virtualizes the differences in capability and accessibility by mapping an abstract data-parallel programming model on those targets. The applicability of CGiS for GPUs has been shown in previous work; this work presents the extension of the framework for SIMD instruction sets of CPUs. We show how to overcome the obstacles in mapping the abstract programming model of CGiS to the SIMD hardware. Our experimental results underline the viability of this approach: Real-world applications can be implemented easily with CGiS and result in efficient code.
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