high performance computing on graphics processing units: hgpu.org

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Using graphics devices in reverse: GPU-based Image Processing and Computer Vision

James Fung,Steve Mann

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia Quadro FX 5600

October 27, 2010 by hgpu

Stackless KD-Tree Traversal for High Performance GPU Ray Tracing

S. Popov,J. Gunther,H. P. Seidel,P. Slusallek

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Tags: Computer science, CUDA, KD-tree, nVidia, nVidia GeForce 8800 GTX, Raytracing

October 27, 2010 by hgpu

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing

Andreas Klockner,Nicolas Pinto,Yunsup Lee,Bryan Catanzaro,Paul Ivanov,Ahmed Fasih

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package

October 27, 2010 by hgpu

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Victor W. Lee,Changkyu Kim,Jatin Chhugani,Michael Deisher,Daehyun Kim,Anthony D. Nguyen,Nadathur Satish,Mikhail Smelyanskiy,Srinivas Chennupaty,Per Hammarlund,Ronak Singhal,Pradeep Dubey

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Tags: Algorithm optimization, Computer science, nVidia, nVidia GeForce GTX 280, Performance

October 27, 2010 by hgpu

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

Daniel Ruijters,Bart ter Haar Romeny,Paul Suetens

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 9800 GTX, Package

October 27, 2010 by hgpu

Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

K. Fatahalian,J. Sugerman,P. Hanrahan

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Tags: Algorithms, ATI, ATI Radeon 9800 XT, Computer science, Linear Algebra, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce FX 5900 Ultra

October 27, 2010 by hgpu

GPU Based Acceleration of Telegraph Equation

Vaclav Simek, Michal Kraus, Kunovsky Jiri, Jiri Petrek

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Tags: Algorithms, Computer science, CUDA, nVidia

October 27, 2010 by hgpu

Importance of Explicit Vectorization for CPU and GPU Software Performance

Neil G. Dickson, Kamran Karimi, Firas Hamze

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Tags: Computer science, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, Physics

October 27, 2010 by hgpu

On the limits of GPU acceleration

Richard Vuduc, Aparna Chandramowlishwaran, Jee Choi, Murat Guney, Aashay Shringarpure

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Tags: Computer science, nVidia, nVidia GeForce GTX 285, Performance, Tesla C1060, Tesla S1070

October 27, 2010 by hgpu

GPU sample sort

Nikolaj Leischner, Vitaly Osipov, Peter Sanders

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 285, Sorting, Tesla C1060

October 27, 2010 by hgpu

Accelerating video decoding using GPU

Guobin Shen,Lihua Zhu,Shipeng Li,Heung-Yeung Shum,Ya-Qin Zhang

Tags: Computer science, nVidia, Video decoding

October 27, 2010 by hgpu

GPU implementation of neural networks

K. Oh

Tags: ATI, ATI Radeon 9700 Pro, Computer science, Image processing, Neural networks, Text detection

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Using graphics devices in reverse: GPU-based Image Processing and Computer Vision

Stackless KD-Tree Traversal for High Performance GPU Ray Tracing

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

GPU Based Acceleration of Telegraph Equation

Importance of Explicit Vectorization for CPU and GPU Software Performance

On the limits of GPU acceleration

GPU sample sort

Accelerating video decoding using GPU

GPU implementation of neural networks

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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