Numerical Computations with GPUs

From the book reviews: "This book attempts to provide some guidance for researchers who develop HPC codes and want to run them on GPU-based systems. ... The intended readership consists of people who already have a certain amount of experience in working with GPUs ... . For readers with such a background, it will prove to be useful reading." (Kai Diethelm, Computing Reviews, November, 2014)

• Accelerating Numerical Dense Linear Algebra Calculations with GPUs.- A Guide to Implement Tridiagonal Solvers on GPUs.- Batch Matrix Exponentiation.- Efficient Batch LU and QR Decomposition on GPU.- A Flexible CUDA LU-Based Solver for Small, Batched Linear Systems.- Sparse Matrix-Vector Product.- Solving Ordinary Differential Equations on GPUs.- GPU-based integration of large numbers of independent ODE systems.- Finite and spectral element methods on unstructured grids for flow and wave propagation problems.- A GPU implementation for solving the Convection Diffusion equation using the Local Modified SOR method.- Pseudorandom numbers generation for Monte Carlo simulations on GPUs: Open CL approach.- Monte Carlo Automatic Integration with Dynamic Parallelism in CUDA.- GPU-Accelerated computation routines for quantum trajectories method.- Monte Carlo Simulation of Dynamic Systems on GPUs.- Fast Fourier Transform (FFT) on GPUs.- A Highly Efficient FFT Using Shared-Memory Multiplexing.- Increasing parallelism and reducing thread contentions in mapping localized N-body simulations to GPUs.