Optimizing CUDA code by kernel fusion: application on BLAS

Contemporary GPUs have significantly higher arithmetic throughput than a memory throughput. Hence, many GPU kernels are memory bound and cannot exploit arithmetic power of the GPU. Examples of memory-bound kernels are BLAS-1 (vector–vector) and BLAS-2 (matrix–vector) operations. However, when kernels share data, kernel fusion can improve memory locality by placing shared data, originally passed via off-chip global memory, into a faster, but distributed on-chip memory. In this paper, we show how kernels performing map, reduce or their nested combinations can be fused automatically by our source-to-source compiler. To demonstrate the usability of the compiler, we have implemented several BLAS-1 and BLAS-2 routines and show how the performance of their sequences can be improved by fusions. Compared with similar sequences using CUBLAS, our compiler is able to generate code that is up to 2.24 × faster for the examples tested.