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New Row-grouped CSR format for storing the sparse matrices on GPU with implementation in CUDA

Tomas Oberhuber, Atsushi Suzuki, Jan Vacata

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Sparse matrix

December 13, 2010 by hgpu

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2

Adam Szalkowski, Christian Ledergerber, Philipp Kraehenbuehl, Christophe Dessimoz

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Tags: Biology, Cell processor, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Package, Sequence alignment, Smith-Waterman algorithm

December 12, 2010 by hgpu

Low viscosity flow simulations for animation

Jeroen Molemaker, Jonathan M. Cohen, Sanjit Patel, Jonyong Noh

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Tags: 3D Graphics and Realism, Computer science, CUDA, Fluid dynamics, nVidia, nVidia GeForce 8800 GTX, Partial differential equations, PDEs

December 12, 2010 by hgpu

Deforming a High-Resolution Mesh in Real-Time by Mapping onto a Low-Resolution Physical Model

Hans de Visser, Olivier Comas, David Conlan, Sebastien Ourselin, Josh Passenger, Olivier Salvado

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Tags: Computed tomography, CT, CUDA, Image processing, Medicine, nVidia, nVidia GeForce 8800 GTX

December 12, 2010 by hgpu

MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs

John Stratton, Sam Stone, Wen-Mei Hwu

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

December 12, 2010 by hgpu

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

W. Plishker, O. Dandekar, S. S. Bhattacharyya, R. Shekhar

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Tags: Computed tomography, CT, CUDA, Image processing, Image registration, Medicine, nVidia, nVidia GeForce 8800 GTX

December 12, 2010 by hgpu

Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA

Allan Rasmusson, Jesper Mosegaard, Thomas S. Sorensen

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Tags: Biology, CUDA, Medicine, nVidia, nVidia GeForce 8800 GTX

December 12, 2010 by hgpu

Map-reduce as a Programming Model for Custom Computing Machines

Jackson H. C. Yeung, C. C. Tsang, K. H. Tsoi, Bill S. H. Kwan, Chris C. C. Cheung, Anthony P. C. Chan, Philip H. W. Leong

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Tags: Computer science, CUDA, FPGA, MapReduce, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GTX, Signal processing

December 12, 2010 by hgpu

Real-time digital holographic microscopy using the graphic processing unit

Tomoyoshi Shimobaba, Yoshikuni Sato, Junya Miura, Mai Takenouchi, Tomoyoshi Ito

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Tags: Computer holography, CUDA, Electron microscopy, FFT, nVidia, nVidia GeForce 8800 GTS, Optics, Physics, Signal processing

December 12, 2010 by hgpu

A compiler framework for optimization of affine loop nests for gpgpus

Muthu M. Baskaran, Uday Bondhugula, Sriram Krishnamoorthy, J. Ramanujam, Atanas Rountev, P. Sadayappan

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, Optimization

December 12, 2010 by hgpu

Two-electron integral evaluation on the graphics processor unit

Koji Yasuda

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Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, nVidia, nVidia GeForce 8800 GTX

December 12, 2010 by hgpu

A Fast Similarity Join Algorithm Using Graphics Processing Units

M. D. Lieberman, J. Sankaranarayanan, H. Samet

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Text mining

December 12, 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

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high performance computing on graphics processing units: hgpu.org

New Row-grouped CSR format for storing the sparse matrices on GPU with implementation in CUDA

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2

Low viscosity flow simulations for animation

Deforming a High-Resolution Mesh in Real-Time by Mapping onto a Low-Resolution Physical Model

MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA

Map-reduce as a Programming Model for Custom Computing Machines

Real-time digital holographic microscopy using the graphic processing unit

A compiler framework for optimization of affine loop nests for gpgpus

Two-electron integral evaluation on the graphics processor unit

A Fast Similarity Join Algorithm Using Graphics Processing Units

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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