high performance computing on graphics processing units: hgpu.org

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Efficient Graph Comparison and Visualization Using GPU

Wojciech Czech, David A. Yuen

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Tags: Algorithms, Computer science, CUDA, Graph theory, Java, Matrix decomposition, nVidia, Tesla C2070, Visualization

November 15, 2011 by hgpu

FPGA Based High Performance and Scalable Block LU Decomposition Architecture

Manish Kumar Jaiswal, Nitin Chandrachoodan

Tags: Computer science, FPGA, Linear Algebra, Matrix decomposition

June 14, 2011 by hgpu

GPU-based LU decomposition for large method of moments problems

E. Lezar, D.B. Davidson

Tags: Electrodynamics, Electromagnetics simulation, Matrix decomposition

April 27, 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

Takagi Factorization on GPU using CUDA

Gagandeep S. Sachdev, Vishay Vanjani and Mary W. Hall

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Tags: Computer science, CUDA, Factorization, Matrix decomposition, nVidia, nVidia GeForce GTX 260

February 17, 2011 by hgpu

QR decomposition on GPUs

Andrew Kerr, Dan Campbell, Mark Richards

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280

February 5, 2011 by hgpu

Algorithmic performance studies on graphics processing units

O. Schenk, M. Christen, H. Burkhart

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Tags: Computer science, Linear Algebra, Matrix decomposition, Nonlinear optimization, nVidia, nVidia GeForce 8800 GTX, Sparse direct solvers

November 2, 2010 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Efficient Graph Comparison and Visualization Using GPU

FPGA Based High Performance and Scalable Block LU Decomposition Architecture

GPU-based LU decomposition for large method of moments problems

Performance Comparison of Cholesky Decomposition on GPUs and FPGAs

Takagi Factorization on GPU using CUDA

QR decomposition on GPUs

Algorithmic performance studies on graphics processing units

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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