Vasily Volkov

• 2011. Alcantara et al. Building an Efficient Hash Table on the GPU, in GPU Computing Gems Jade edition, 39–54.
• 2009. Datta et al. Auto-tuning the 27-point stencil for multicore, 4th International Workshop on Automatic Performance Tuning (iWAPT).
• 2008. Volkov and Demmel. Benchmarking GPUs to tune dense linear algebra, SC08.
• 2008. Datta et al. Stencil computation optimization and autotuning on state-of-the-art multicore architectures, SC08.
• 2008. Garland et al. Parallel computing experiences with CUDA, IEEE Micro 28, 4, 13–27.
• 2008. Volkov and Kazian. Fitting FFT onto the G80 Architecture, CS 258 final project report, University of California, Berkeley.
• 2008. Volkov and Demmel. LU, QR and Cholesky factorizations using vector capabilities of GPUs, Technical Report No. UCB/EECS-2008-49, EECS Department, University of California, Berkeley, May 13, 2008.
• 2007. Volkov and Demmel. Using GPUs to accelerate the bisection algorithm for finding eigenvalues of symmetric tridiagonal matrices, Technical Report No. UCB/EECS-2007-179, EECS Department, University of California, Berkeley, December 29, 2007.

• 2011. Volkov. Unrolling parallel loops, tutorial talk at SC11.
• 2010. Volkov. Better performance at lower occupancy, GPU Technology Conference 2010 (GTC 2010).
• 2010. Volkov. Use registers and multiple outputs per thread on GPU, International Workshop on Parallel Matrix Algorithms and Applications 2010 (PMAA'10).
• 2010. Volkov. Programming inverse memory hierarchy: case of stencils on GPUs, International Conference on Parallel Computational Fluid Dynamics 2010 (ParCFD 2010).
• 2008. Volkov and Demmel. Benchmarking GPUs to tune dense linear algebra, 2008 ACM/IEEE Conference on Supercomputing (SC08).
• 2008. Volkov and Demmel. Using GPUs to accelerate linear algebra routines, Poster at PAR Lab Winter Retreat, January 9, 2008.

• 2009. LU and other factorizations.
• 2008. Matrix-matrix multiply (SGEMM). It was included in NVIDIA CUBLAS. See also my symmetric rank-k update (SSYRK).
• 2008. My FFT prototype was 3x faster than NVIDIA CUFFT. The ideas behind it were used in OpenCL FFT and later in CUFFT.
• 2007. Matrix-matrix multiply for ATI GPUs using DirectX.