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KBLAS is a new open source high performance library that provides optimizedkernels for a subset of Level 2 BLAS functionalities on CUDA-enabled GPUs.Since performance of dense matrix-vector multiplication is hindered by theoverhead of memory accesses, a double-buffering optimization technique isemployed to overlap data motion with computation. After identifying a properset of tuning parameters, KBLAS is able to efficiently run on various GPUarchitectures across different generations, avoiding the time-consuming step ofcode rewriting, while still being compliant with the standard BLAS API. Anotheradvanced optimization technique allows to ensure coalesced memory access whendealing with submatrices, especially in the context of high level dense linearalgebra algorithms. All four precisions KBLAS kernels have been leveraged tomulti-GPUs environment, which requires the introduction of new APIs to easeusers' experiences on these challenging systems. The KBLAS performanceoutperforms existing state-of-the-art implementations on all matrix sizes,achieves asymptotically up to 50% and 60% speedup on single GPU and multi-GPUssystems, respectively, and validates our performance model. A subset of KBLAShigh performance kernels has been integrated into NVIDIA's standard BLASimplementation (cuBLAS) for larger dissemination, starting version 6.0.

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators