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Table of Contents: “...Programming Finite Element Computations --...”
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Table of Contents: “...Part 1: Parallel Algorithms and Data Structures - Paulius Micikevicius, NVIDIA 1 Large-Scale GPU Search 2 Edge v. Node Parallelism for Graph Centrality Metrics 3 Optimizing parallel prefix operations for the Fermi architecture 4 Building an Efficient Hash Table on the GPU 5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem 6 On Improved Memory Access Patterns for Cellular Automata Using CUDA 7 Fast Minimum Spanning Tree Computation on Large Graphs 8 Fast in-place sorting with CUDA based on bitonic sort Part 2: Numerical Algorithms - Frank Jargstorff, NVIDIA 9 Interval Arithmetic in CUDA 10 Approximating the erfinv Function 11 A Hybrid Method for Solving Tridiagonal Systems on the GPU 12 LU Decomposition in CULA 13 GPU Accelerated Derivative-free Optimization Part 3: Engineering Simulation - Peng Wang, NVIDIA 14 Large-scale gas turbine simulations on GPU clusters 15 GPU acceleration of rarefied gas dynamic simulations 16 Assembly of Finite Element Methods on Graphics Processors 17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications 18 Solving Wave Equations on Unstructured Geometries 19 Fast electromagnetic integral equation solvers on graphics processing units (GPUs) Part 4: Interactive Physics for Games and Engineering Simulation - Richard Tonge, NVIDIA 20 Solving Large Multi-Body Dynamics Problems on the GPU 21 Implicit FEM Solver in CUDA 22 Real-time Adaptive GPU multi-agent path planning Part 5: Computational Finance - Thomas Bradley, NVIDIA 23 High performance finite difference PDE solvers on GPUs for financial option pricing 24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations 25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method Part 6: Programming Tools and Techniques - Cliff Wooley, NVIDIA 26 Thrust: A Productivity-Oriented Library for CUDA 27 GPU Scripting and Code Generation with PyCUDA 28 Jacket: GPU Powered MATLAB Acceleration 29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation 30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot 31 Abstraction for AoS and SoA Layout in C++ 32 Processing Device Arrays with C++ Metaprogramming 33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision 34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs 35 Dynamic Load Balancing using Work-Stealing 36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads....”
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Table of Contents: “...Chapter 8: Load balancing -- 8.1 Introduction -- 8.2 Dynamic load balancing: the Linda legacy -- 8.3 Static Load Balancing: The Divisible LoadTheory Approach -- 8.3.1 Modeling Costs -- 8.3.2 Communication Configuration -- 8.3.3 Analysis -- 8.3.3.1 N-Port, Block-Type, Single-Installment Solution -- 8.3.3.2 One-Port, Block-Type, Single-Installment Solution -- 8.3.4 Summary -- Short Literature Review -- 8.4 DLTlib: A library for partitioning workloads -- 8.5 Case studies -- 8.5.1 Hybrid Computation of a Mandelbrot Set "Movie'':A Case Study in Dynamic Load Balancing -- 8.5.2 Distributed Block Cipher Encryption: A Case Study in Static Load Balancing -- Appendix A: Compiling Qt programs -- A.1 Using an IDE -- A.2 The qmake Utility -- Appendix B: Running MPI programs -- B.1 Preparatory Steps -- B.2 Computing Nodes discovery for MPI Program Deployment -- B.2.1 Host Discovery with the nmap Utility -- B.2.2 Automatic Generation of a Hostfile -- Appendix C: Time measurement -- C.1 Introduction -- C.2 POSIX High-Resolution Timing -- C.3 Timing in Qt -- C.4 Timing in OpenMP -- C.5 Timing in MPI -- C.6 Timing in CUDA -- Appendix D: Boost.MPI -- D.1 Mapping from MPI C to Boost.MPI -- Appendix E: Setting up CUDA -- E.1 Installation -- E.2 Issues with GCC -- E.3 Running CUDA without an Nvidia GPU -- E.4 Running CUDA on Optimus-Equipped Laptops -- E.5 Combining CUDA with Third-Party Libraries -- Appendix F: DLTlib -- F.1 DLTlib Functions -- F.1.1 Class Network: Generic Methods -- F.1.2 Class Network: Query Processing -- F.1.3 Class Network: Image Processing -- F.1.4 Class Network: Image Registration -- F.2 DLTlib Files -- Glossary -- Bibliography -- Index....”
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Table of Contents: “...Shared Memory Parallel Language Constructs; 4.1 Basic Concepts for Shared Memory Parallel Programming; 4.2 The Sample Problem on an SGI Challenge SMP; 4.3 Observations and Considerations; Chapter 5. ...”
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