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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

GPU-based ultrafast IMRT plan optimization

Chunhua Men,Xuejun Gu,Dongju Choi,Amitava Majumdar,Ziyi Zheng,Klaus Mueller,Steve B. Jiang

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Tags: CUDA, Medicine, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla C1060, Tesla C1070

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

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

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

Programming

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

GPU-based ultrafast IMRT plan optimization

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

GPU sample sort

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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