000 04092nam a22005295i 4500
001 978-3-319-06548-9
003 DE-He213
005 20200421111852.0
007 cr nn 008mamaa
008 140703s2014 gw | s |||| 0|eng d
020 _a9783319065489
_9978-3-319-06548-9
024 7 _a10.1007/978-3-319-06548-9
_2doi
050 4 _aQA297-299.4
072 7 _aUYA
_2bicssc
072 7 _aCOM051300
_2bisacsh
082 0 4 _a518
_223
245 1 0 _aNumerical Computations with GPUs
_h[electronic resource] /
_cedited by Volodymyr Kindratenko.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aX, 405 p. 107 illus., 49 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aAccelerating 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.
520 _aThis book brings together research on numerical methods adapted for Graphics Processing Units (GPUs). It explains recent efforts to adapt classic numerical methods, including solution of linear equations and FFT, for massively parallel GPU architectures. This volume consolidates recent research and adaptations, covering widely used methods that are at the core of many scientific and engineering computations. Each chapter is written by authors working on a specific group of methods; these leading experts provide mathematical background, parallel algorithms and implementation details leading to reusable, adaptable and scalable code fragments. This book also serves as a GPU implementation manual for many numerical algorithms, sharing tips on GPUs that can increase application efficiency. The valuable insights into parallelization strategies for GPUs are supplemented by ready-to-use code fragments. Numerical Computations with GPUs targets professionals and researchers working in high performance computing and GPU programming. Advanced-level students focused on computer science and mathematics will also find this book useful as secondary text book or reference.
650 0 _aComputer science.
650 0 _aArchitecture, Computer.
650 0 _aComputer programming.
650 0 _aProgramming languages (Electronic computers).
650 0 _aNumerical analysis.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aComputer Science.
650 2 4 _aNumeric Computing.
650 2 4 _aProgramming Techniques.
650 2 4 _aComputer System Implementation.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aProgramming Languages, Compilers, Interpreters.
700 1 _aKindratenko, Volodymyr.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319065472
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-06548-9
912 _aZDB-2-SCS
942 _cEBK
999 _c56198
_d56198