Abstract
OpenMP is an explicit parallel programming model that offers reasonable productivity. Its memory model assumes a shared address space, and hence the direct translation - as done by common OpenMP compilers - requires an underlying shared-memory architecture. Many lab machines include 10s of processors, built from commodity components and thus include distributed address spaces. Despite many efforts to provide higher productivity for these platforms, the most common programming model uses message passing, which is substantially more tedious to program than shared-address-space models. This paper presents a compiler/runtime system that translates OpenMP programs into message passing variants and executes them on clusters up to 64 processors. We build on previous work that provided a proof of concept of such translation. The present paper describes compiler algorithms and runtime techniques that provide the automatic translation of a first class of OpenMP applications: those that exhibit regular write array subscripts and repetitive communication. We evaluate the translator on representative benchmarks of this class and compare their performance against hand-written MPI variants. In all but one case, our translated versions perform close to the hand-written variants.
This work was supported, in part, by the National Science Foundation under grants No. 0751153-CNS, 0707931-CNS, 0833115-CCF, and 0916817-CCF.
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Kwon, O., Jubair, F., Min, SJ., Bae, H., Eigenmann, R., Midkiff, S.P. (2013). Automatic Scaling of OpenMP Beyond Shared Memory. In: Rajopadhye, S., Mills Strout, M. (eds) Languages and Compilers for Parallel Computing. LCPC 2011. Lecture Notes in Computer Science, vol 7146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36036-7_1
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