Abstract
CUDA is a data parallel programming model that supports several key abstractions - thread blocks, hierarchical memory and barrier synchronization - for writing applications. This model has proven effective in programming GPUs. In this paper we describe a framework called MCUDA, which allows CUDA programs to be executed efficiently on shared memory, multi-core CPUs. Our framework consists of a set of source-level compiler transformations and a runtime system for parallel execution. Preserving program semantics, the compiler transforms threaded SPMD functions into explicit loops, performs fission to eliminate barrier synchronizations, and converts scalar references to thread-local data to replicated vector references. We describe an implementation of this framework and demonstrate performance approaching that achievable from manually parallelized and optimized C code. With these results, we argue that CUDA can be an effective data-parallel programming model for more than just GPU architectures.
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Stratton, J.A., Stone, S.S., Hwu, Wm.W. (2008). MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs. In: Amaral, J.N. (eds) Languages and Compilers for Parallel Computing. LCPC 2008. Lecture Notes in Computer Science, vol 5335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89740-8_2
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DOI: https://doi.org/10.1007/978-3-540-89740-8_2
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