high performance computing on graphics processing units: hgpu.org

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CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

Yongchao Liu, Bertil Schmidt, Douglas Maskell

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Smith-Waterman algorithm

November 6, 2010 by hgpu

Automatic fitting of spiking neuron models to electrophysiological recordings

Cyrille Rossant, Dan F. Goodman, Jonathan Platkiewicz, Romain Brette

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Tags: Adaptive threshold, Biology, CUDA, Distributed computing, Neuroscience, nVidia, nVidia GeForce GTX 295, Optimization, Package

November 6, 2010 by hgpu

Exploring the multiple-GPU design space

Dana Schaa, David Kaeli

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce 8800 GTX

November 6, 2010 by hgpu

GPU Computing for Atmospheric Modeling

Rory Kelly

Tags: CUDA, Earth and Space Sciences, nVidia

November 6, 2010 by hgpu

A Practical Quicksort Algorithm for Graphics Processors

Daniel Cederman, Philippas Tsigas

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Sorting

November 6, 2010 by hgpu

StoreGPU: exploiting graphics processing units to accelerate distributed storage systems

Samer Al-Kiswany, Abdullah Gharaibeh, Elizeu Santos-Neto, George Yuan, Matei Ripeanu

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 9800 GTX, Package, Storage system

November 6, 2010 by hgpu

Maintaining constant frame rates in 3D texture-based volume rendering

D. Weiskopf, M. Weiler, T. Ertl

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Tags: 3D Graphics and Realism, ATI, ATI Radeon 9800 Pro, Computer science, nVidia, nVidia GeForce FX 5950 Ultra, OpenGL, Raytracing, Rendering

November 6, 2010 by hgpu

Accelerating Dust Temperature Calculations with Graphics Processing Units

Patrik Jonsson, Joel Primack

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Package, Physics, Tesla C1060

November 6, 2010 by hgpu

Fast scale invariant feature detection and matching on programmable graphics hardware

Nico Cornelis, Luc Van Gool

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce 8800 GTX

November 6, 2010 by hgpu

Real-time visualization of large volume datasets on standard PC hardware

Kai Xie, Jie Yang, Y. M. Zhu

Tags: Computed tomography, CT, Image processing, Medicine, nVidia, nVidia GeForce 6600

November 6, 2010 by hgpu

Efficient simulation of large-scale spiking neural networks using CUDA graphics processors

Jayram M. Nageswaran, Nikil Dutt, Jeffrey L. Krichmar, Alex Nicolau, Alex Veidenbaum

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Tags: Biology, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, STDP

November 6, 2010 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

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high performance computing on graphics processing units: hgpu.org

CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

Automatic fitting of spiking neuron models to electrophysiological recordings

Exploring the multiple-GPU design space

GPU Computing for Atmospheric Modeling

A Practical Quicksort Algorithm for Graphics Processors

StoreGPU: exploiting graphics processing units to accelerate distributed storage systems

Maintaining constant frame rates in 3D texture-based volume rendering

Accelerating Dust Temperature Calculations with Graphics Processing Units

Fast scale invariant feature detection and matching on programmable graphics hardware

Real-time visualization of large volume datasets on standard PC hardware

Efficient simulation of large-scale spiking neural networks using CUDA graphics processors

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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