Abstract
Programming heterogeneous parallel computer systems is notoriously difficult, but MIMD models have proven to be portable across multi-core processors, clusters, and massively parallel systems. It would be highly desirable for GPUs (Graphics Processing Units) also to be able to leverage algorithms and programming tools designed for MIMD targets. Unfortunately, most GPU hardware implements a very restrictive multi-threaded SIMD-based execution model.
This paper presents a compiler, assembler, and interpreter system that allows a GPU to implement a richly featured MIMD execution model that supports shared-memory communication, recursion, etc. Through a variety of careful design choices and optimizations, reasonable efficiency is obtained on NVIDIA CUDA GPUs. The discussion covers both the methods used and the motivation in terms of the relevant aspects of GPU architecture.
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Dietz, H.G., Young, B.D. (2010). MIMD Interpretation on a GPU. In: Gao, G.R., Pollock, L.L., Cavazos, J., Li, X. (eds) Languages and Compilers for Parallel Computing. LCPC 2009. Lecture Notes in Computer Science, vol 5898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13374-9_5
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DOI: https://doi.org/10.1007/978-3-642-13374-9_5
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