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GPU utilization, measured as occupancy, is limited by the parallel threads'combined usage of on-chip resources, such as registers and theprogrammer-managed shared memory. Higher resource demand means lower effectiveparallel thread count, and therefore lower program performance. Ourinvestigation found that registers are often the occupancy limiters.  The de-facto nvcc compiler-based approach spills excessive registers to theoff-chip memory, ignoring the shared memory and leaving the on-chip resourcesunderutilized. To mitigate the register demand, this paper presents a binarytranslation technique, called RegDem, that spills excessive registers to theunderutilized shared memory by transforming the GPU assembly code (SASS). MostGPU programs do not fully use shared memory, thus allowing RegDem to use it forregister spilling. The higher occupancy achieved by RegDem outweighs theslightly higher cost of accessing shared memory instead of placing data inregisters. The paper also presents a compile-time performance predictor thatmodels instructions stalls to choose the best version from a set of programvariants. Cumulatively, these techniques outperform the nvcc compiler with a 9%geometric mean, the highest observed being 18%.

RegDem: Increasing GPU Performance via Shared Memory Register Spilling