high performance computing on graphics processing units: hgpu.org

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Parallel volume rendering implementation on graphics cards using CUDA

Jens Fangerau, Susanne Kromker

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 280, OpenGL, Rendering, Visualization

September 13, 2011 by hgpu

Gate-Level Simulation with GPU Computing

Debapriya Chatterjee, Andrew Deorio, Valeria Bertacco

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Tags: Algorithms, CAD, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Performance

September 13, 2011 by hgpu

Collision-streams: fast GPU-based collision detection for deformable models

Min Tang, Dinesh Manocha, Jiang Lin, Ruofeng Tong

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL

September 11, 2011 by hgpu

Parallel programming with NVIDIA CUDA

Alejandro Segovia

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Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8600 GT, Package, Tutorial

September 11, 2011 by hgpu

High-performance SIMT code generation in an active visual effects library

Jay L.T. Cornwall, Lee Howes, Paul H.J. Kelly, Phil Parsonage, Bruno Nicoletti

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Tags: Algorithms, Code generation, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 260, Performance, Software Engineering, Visualization

September 11, 2011 by hgpu

Solving diffractive optics problems using graphics processing units

D. L. Golovashkin, N. L. Kasanskiy

Tags: Algorithms, ATI, ATI FireStream, Brook, Cg, CUDA, FDTD, Finite-difference time-domain, nVidia, OpenGL, Optics, Overview, Physics, Raytracing, Review

September 11, 2011 by hgpu

Enabling multiple accelerator acceleration for Java/OpenMP

Ronald Veldema, Thorsten Blass, Michael Philippsen

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Tags: Benchmarking, Computer science, CUDA, Java, nVidia, OpenCL, OpenMP, OpenMPI, Tesla M1060

September 9, 2011 by hgpu

CUDACS: securing the cloud with CUDA-enabled secure virtualization

Flavio Lombardi, Roberto Di Pietro

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Tags: Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8400 M, nVidia GeForce GT 220, Security, Virtualization

September 9, 2011 by hgpu

Experiences with Mapping Non-linear Memory Access Patterns into GPUs

Eladio Gutierrez, Sergio Romero, Maria A. Trenas, Oscar Plata

Tags: Computer science, CUDA, Memory, nVidia, Optimization, Programming techniques

September 8, 2011 by hgpu

A Fast GPU Implementation for Solving Sparse Ill-Posed Linear Equation Systems

Florian Stock, Andreas Koch

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Tags: Cell processor, Conjugate gradient solver, CUDA, FPGA, Image processing, Image reconstruction, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 280, Optimization, Sparse matrix

September 8, 2011 by hgpu

Programming Many-Core Chips

Andras Vajda

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Tags: Book, Cluster computing, Computer science, CUDA, MPI, nVidia, OpenCL, OpenMP, Programming techniques

September 8, 2011 by hgpu

GPU Computation in Bioinspired Algorithms: A Review

M. G. Arenas, A. M. Mora, G. Romero and P. A. Castillo

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Tags: Algorithms, ATI, Bioinformatics, Biology, Computational biology, CUDA, nVidia, OpenCL, Overview, Review

September 8, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

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high performance computing on graphics processing units: hgpu.org

Parallel volume rendering implementation on graphics cards using CUDA

Gate-Level Simulation with GPU Computing

Collision-streams: fast GPU-based collision detection for deformable models

Parallel programming with NVIDIA CUDA

High-performance SIMT code generation in an active visual effects library

Solving diffractive optics problems using graphics processing units

Enabling multiple accelerator acceleration for Java/OpenMP

Experiences with Mapping Non-linear Memory Access Patterns into GPUs

A Fast GPU Implementation for Solving Sparse Ill-Posed Linear Equation Systems

Programming Many-Core Chips

GPU Computation in Bioinspired Algorithms: A Review

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

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