Leonid Oliker

TL;DR: This work explores multicore stencil (nearest-neighbor) computations --- a class of algorithms at the heart of many structured grid codes, including PDF solvers, and develops a number of effective optimization strategies, and builds an auto-tuning environment that searches over these strategies to minimize runtime, while maximizing performance portability.

TL;DR: This work examines sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs, and presents several optimization strategies especially effective for the multicore environment.

TL;DR: In this article, the authors examine sparse matrix-vector multiply (SpMV) kernels across a broad spectrum of multicore designs and present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations.

TL;DR: In this article, the authors examined the potential of using the STI Cell processor as a building block for future high-end computing systems and proposed modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations.

TL;DR: This work introduces a performance model for Cell and applies it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs, and proposes modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations.