high performance computing on graphics processing units: hgpu.org

Mark S. Friedrichs, Peter Eastman, Vishal Vaidyanathan, Mike Houston, Scott Legrand, Adam L. Beberg, Daniel L. Ensign, Christopher M. Bruns, Vijay S. Pande

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Tags: ATI, ATI Radeon HD 4870, ATI Stream, Biology, Brook, Chemistry, Computational chemistry, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package, Solvent effects

October 30, 2010 by hgpu

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

David Dynerman, Erick Butzlaff, Julie C. Mitchell

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Tags: Bioinformatics, Biology, Biophysics, Chemistry, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Solvent effects

October 29, 2010 by hgpu

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Yannick Allusse, Patrick Horain, Ankit Agarwal, Cindula Saipriyadarshan

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Tags: Computer science, Computer vision, CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 280, OpenGL, Package

October 28, 2010 by hgpu

Implementing Decision Trees and Forests on a GPU

Toby Sharp

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Tags: Computer science, Computer vision, HLSL, nVidia, nVidia GeForce GTX 280

October 28, 2010 by hgpu

An integrated GPU power and performance model

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, Performance

October 28, 2010 by hgpu

Fast and scalable list ranking on the GPU

M. Suhail Rehman, Kishore Kothapalli, P. J. Narayanan

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Tags: Algorithms, Computer science, CUDA, List ranking, nVidia, nVidia GeForce GTX 280

October 27, 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

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high performance computing on graphics processing units: hgpu.org

Optimal loop unrolling for GPGPU programs (thesis)

Optimal loop unrolling for GPGPU programs

Multilevel summation of electrostatic potentials using graphics processing units

Parallel external sorting for CUDA-enabled GPUs with load balancing and low transfer overhead

Improving Performance of Matrix Multiplication and FFT on GPU

Graphics Processing Units and High-Dimensional Optimization

Accelerating molecular dynamic simulation on graphics processing units

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Implementing Decision Trees and Forests on a GPU

An integrated GPU power and performance model

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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