@article {990,
	title = {On the performance and energy efficiency of sparse linear algebra on GPUs},
	journal = {International Journal of High Performance Computing Applications},
	year = {2016},
	month = {2016-10},
	abstract = {In this paper we unveil some performance and energy efficiency frontiers for sparse computations on GPU-based supercomputers. We compare the resource efficiency of different sparse matrix{\textendash}vector products (SpMV) taken from libraries such as cuSPARSE and MAGMA for GPU and Intel{\textquoteright}s MKL for multicore CPUs, and develop a GPU sparse matrix{\textendash}matrix product (SpMM) implementation that handles the simultaneous multiplication of a sparse matrix with a set of vectors in block-wise fashion. While a typical sparse computation such as the SpMV reaches only a fraction of the peak of current GPUs, we show that the SpMM succeeds in exceeding the memory-bound limitations of the SpMV. We integrate this kernel into a GPU-accelerated Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) eigensolver. LOBPCG is chosen as a benchmark algorithm for this study as it combines an interesting mix of sparse and dense linear algebra operations that is typical for complex simulation applications, and allows for hardware-aware optimizations. In a detailed analysis we compare the performance and energy efficiency against a multi-threaded CPU counterpart. The reported performance and energy efficiency results are indicative of sparse computations on supercomputers.},
	doi = {10.1177/1094342016672081},
	url = {http://hpc.sagepub.com/content/early/2016/10/05/1094342016672081.abstract},
	author = {Hartwig Anzt and Stanimire Tomov and Jack Dongarra}
}
