high performance computing on graphics processing units: hgpu.org

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Architecture-Aware Optimization on a 1600-core Graphics Processor

Mayank Daga, Thomas R.W. Scogland, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 5870, Cluster computing, Computer science, CUDA, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Tesla C2050

October 4, 2011 by hgpu

Berkeley Dwarfs on CUDA

Paul Springer

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

October 4, 2011 by hgpu

GPU-Accelerated DNA Distance Matrix Computation

Zhi Ying, Xinhua Lin, Simon Chong-Wee See, Minglu Li

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Tags: ATI, ATI Radeon HD 5850, Bioinformatics, Biology, Genetics, nVidia, OpenCL, Performance, Tesla C2050

October 3, 2011 by hgpu

A New Class of Parallel Scheduling Algorithms

Wojciech Bozejko

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Tags: Algorithms, Book, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Programming techniques, Task scheduling, Tesla C1060, Tesla C2050

October 2, 2011 by hgpu

Hardware/Software Co-design for Energy-Efficient Seismic Modeling

Jens Krueger, David Donofrio, John Shalf, Marghoob Mohiyuddin, Samuel Williams, Leonid Oliker, Franz-Josef Pfreundt

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Tags: CUDA, Earth and Space Sciences, FPGA, nVidia, PC cluster, Seismic modeling, Seismology, Tesla C2050, Wave equation

October 2, 2011 by hgpu

Development of a Chemically Reacting Flow Solver on the Graphic Processing Units

Hai Le

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Tags: Algorithms, CUDA, Finite volume method, Fluid dynamics, nVidia, Optimization, Tesla C2050

October 2, 2011 by hgpu

Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems

Mayank Daga

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Heterogeneous systems, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Tesla C2050, Thesis

September 30, 2011 by hgpu

Many-threaded implementation of differential evolution for the CUDA platform

Pavel Kromer, Vaclav Snasel, Jan Platos, Ajith Abraham

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Differential evolution, Genetic programming, nVidia, Optimization, Tesla C2050

September 29, 2011 by hgpu

Large-Scale High-Lundquist Number Reduced MHD Simulations of the Solar Corona Using GPU Accelerated Machines

L. Lin, Chung-Sang Ng, A. Bhattacharjee

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Tags: Astrophysics, Computational Physics, CUDA, GPU cluster, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, nVidia, Plasma physics, Solar and Stellar Astrophysics, Spectral methods, Tesla C2050

September 29, 2011 by hgpu

Global optimization model on power efficiency of GPU and multicore processing element for SIMD computing with CUDA

Da-Qi Ren, Reiji Suda

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8800 GTS, Optimization, Tesla C2050

September 28, 2011 by hgpu

Generating GPU Code from a High-level Representation for Image Processing Kernels

Richard Membarth, Anton Lokhmotov, Jurgen Teich

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5800, OpenCL, Optimization, Tesla C2050

September 26, 2011 by hgpu

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Peng Du, Rick Weber, Piotr Luszczek, Stanimire Tomov, Gregory Peterson, Jack Dongarra

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Performance, Tesla C2050

September 26, 2011 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Architecture-Aware Optimization on a 1600-core Graphics Processor

Berkeley Dwarfs on CUDA

GPU-Accelerated DNA Distance Matrix Computation

A New Class of Parallel Scheduling Algorithms

Hardware/Software Co-design for Energy-Efficient Seismic Modeling

Development of a Chemically Reacting Flow Solver on the Graphic Processing Units

Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems

Many-threaded implementation of differential evolution for the CUDA platform

Large-Scale High-Lundquist Number Reduced MHD Simulations of the Solar Corona Using GPU Accelerated Machines

Global optimization model on power efficiency of GPU and multicore processing element for SIMD computing with CUDA

Generating GPU Code from a High-level Representation for Image Processing Kernels

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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