Titlebook: Numerical Computations with GPUs; Volodymyr Kindratenko Book 2014 Springer International Publishing Switzerland 2014 Differential equation

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Batch Matrix Exponentiationebra packages is closely tied to the performance of matrix–matrix multiplication. Batch matrix–matrix multiplication, the matrix–matrix multiplication of a large number of relatively small matrices, is a developing area within dense linear algebra and is relevant to various application areas such as

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novelty

A Flexible CUDA LU-Based Solver for Small, Batched Linear Systemscations such as reactive flow transport models, which apply the Newton–Raphson technique to linearize and iteratively solve the sets of non linear equations that represent the reactions for ten of thousands to millions of physical locations. The implementation exploits somewhat counterintuitive GPGP

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Solving Ordinary Differential Equations on GPUs in engineering, economics and social sciences. Given their vast appearance, it is of crucial importance to develop efficient numerical routines for solving ODEs that employ the computational power of modern GPUs. Here, we present a high-level approach to compute numerical solutions of ODEs by devel

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prosperity

A GPU Implementation for Solving the Convection Diffusion Equation Using the Local Modified SOR Methfor GPUs. We demonstrate two generally applicable programming techniques, memory reordering as a means of coalescing and recomputation of stored data as a means of alleviating the memory bandwidth bottleneck and increasing the feasible problem size. We focus on the local relaxation version of SOR. I

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Finite-Difference in Time-Domain Scalable Implementations on CUDA and OpenCLlarge spaces, or long non-sinusoidal waveforms, imply high computational floating-point performance, it is of practical interest to take advantage of current and emergent multicore architectures, namely Graphics Processing Units (GPUs) (Pratas, et al.: Fine-grain parallelism using multi-core, cell/B