high performance computing on graphics processing units: hgpu.org

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Using GPU VSIPL & CUDA to Accelerate RF Clutter Simulation

Dan Campbell, Mark McCans, Mike Davis, Mike Brinkmann

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Tags: Computer science, CUDA, Mixed precision, nVidia, nVidia GeForce 9800 GX2, Signal processing

February 19, 2011 by hgpu

Power-aware Performance of Mixed Precision Linear Solvers for FPGAs and GPGPUs

JunKyu Lee, Junqing Sun, Gregory D. Peterson, Robert J. Harrison, Robert J. Hinde

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Tags: Computer science, Energy-efficient computing, FPGA, Linear Algebra, Mixed precision, nVidia, Performance, Tesla C1060

February 18, 2011 by hgpu

Accelerating Double Precision Floating-point Hessenberg Reduction on FPGA and Multicore Architectures

Miaoqing Huang, Lingyuan Wang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, Extended precision, FPGA, nVidia, nVidia GeForce GTX 480, OpenMP, Performance, Tesla C1060

February 18, 2011 by hgpu

GPU Acceleration of Near-Minimal Logic Minimization

Ibrahim Savran, Jason D. Bakos

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla S1070

February 18, 2011 by hgpu

Sparse systems solving on GPUs with GMRES

Raphael Couturier, Stephane Domas

Tags: Computer science, CUBLAS, CUDA, nVidia, Sparse linear iterative solvers, Sparse matrix

February 18, 2011 by hgpu

Accelerating Power Flow studies on Graphics Processing Unit

Jaideep Singh, Ipseeta Aruni

Tags: Computer science, CUDA, nVidia, Performance

February 18, 2011 by hgpu

Performance Comparison of Cholesky Decomposition on GPUs and FPGAs

Depeng Yang, Junqing Sun, JunKu Lee, Getao Liang, David D. Jenkins, Gregory D. Peterson, and Husheng Li

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Tags: Computer science, FPGA, Matrix decomposition, nVidia, nVidia GeForce GTX 480, OpenCL, Performance

February 18, 2011 by hgpu

OpenCL Evaluation for Numerical Linear Algebra Library Development

Peng Du, Piotr Luszczek, Jack Dongarra

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Tags: ATI Radeon HD 5870, Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla C2050

February 17, 2011 by hgpu

Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

GPU Accelerated Particle System for Triangulated Surface Meshes

Brad Peterson, Manasi Datar, Mary Hall, Ross Whitaker

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Particle simulation, Triangular meshes

February 17, 2011 by hgpu

Medium-Grained Functions Mapping using Modern GPUs

Jiri Filipovic, Jan Fousek

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance

February 17, 2011 by hgpu

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Hatem Ltaief, Raymond Namyst, Jean Roman, Samuel Thibault, Stanimire Tomov

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro FX 5800, Task scheduling

February 17, 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

Applications

Using GPU VSIPL & CUDA to Accelerate RF Clutter Simulation

Power-aware Performance of Mixed Precision Linear Solvers for FPGAs and GPGPUs

Accelerating Double Precision Floating-point Hessenberg Reduction on FPGA and Multicore Architectures

GPU Acceleration of Near-Minimal Logic Minimization

Sparse systems solving on GPUs with GMRES

Accelerating Power Flow studies on Graphics Processing Unit

Performance Comparison of Cholesky Decomposition on GPUs and FPGAs

OpenCL Evaluation for Numerical Linear Algebra Library Development

Evaluating one-sided programming models for GPU cluster computations

GPU Accelerated Particle System for Triangulated Surface Meshes

Medium-Grained Functions Mapping using Modern GPUs

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

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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