GPU Computing Gems Jade Edition

Wen-mei W. Hwuis a Senior Director of Research of NVIDIA and the Sanders-AMD Endowed Chair Professor Emeritus of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His work focuses on parallel computing—covering architecture, implementation, compilers, and algorithms. Dr. Hwu has received numerous honors, including the ACM/IEEE Eckert-Mauchly Award, ACM Grace Murray Hopper Award, IEEE B.R. Rau Award. He is an IEEE and ACM Fellow. He earned his Ph.D. in Computer Science from UC Berkele

• Part 1: Parallel Algorithms and Data Structures – Paulius Micikevicius, NVIDIA 1 Large-Scale GPU Search2 Edge v. Node Parallelism for Graph Centrality Metrics3 Optimizing parallel prefix operations for the Fermi architecture4 Building an Efficient Hash Table on the GPU5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem6 On Improved Memory Access Patterns for Cellular Automata Using CUDA7 Fast Minimum Spanning Tree Computation on Large Graphs8 Fast in-place sorting with CUDA based on bitonic sortPart 2: Numerical Algorithms – Frank Jargstorff, NVIDIA 9 Interval Arithmetic in CUDA10 Approximating the erfinv Function11 A Hybrid Method for Solving Tridiagonal Systems on the GPU12 LU Decomposition in CULA13 GPU Accelerated Derivative-free OptimizationPart 3: Engineering Simulation – Peng Wang, NVIDIA 14 Large-scale gas turbine simulations on GPU clusters15 GPU acceleration of rarefied gas dynamic simulations16 Assembly of Finite Element Methods on Graphics  Processors17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications18 Solving Wave Equations on Unstructured Geometries19 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 GPU21 Implicit FEM Solver in CUDA22 Real-time Adaptive GPU multi-agent path planningPart 5: Computational Finance – Thomas Bradley, NVIDIA 23 High performance finite difference PDE solvers on GPUs for financial option pricing24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations25 Financial Market Value-at-Risk Estimation using the Monte Carlo MethodPart 6: Programming Tools and Techniques – Cliff Wooley, NVIDIA 26 Thrust: A Productivity-Oriented Library for CUDA27 GPU Scripting and Code Generation with PyCUDA28 Jacket: GPU Powered MATLAB Acceleration29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot31 Abstraction for AoS and SoA Layout in C++32 Processing Device Arrays with C++ Metaprogramming33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs35 Dynamic Load Balancing using Work-Stealing36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

"It wasn't until recently that parallel [GPU] computing made people realize that there are whole areas in computing science that we can tackle. … When you can do something 10 or 100 times faster, something magical happens and you can do something completely different." --Jen-Hsun Huang, CEO, NVIDIA