high performance computing on graphics processing units: hgpu.org

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Medusa: A Parallel Graph Processing System on Graphics Processors

Jianlong Zhong, Bingsheng He

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Tags: Computer science, CUDA, Graph theory, nVidia, Package, Tesla C2050

June 19, 2014 by hgpu

Synergia CUDA: GPU-accelerated accelerator modeling package

Q. Lu, J. Amundson

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Tags: CUDA, FFT, GPU cluster, MPI, nVidia, Package, Particle-in-cell methods, Physics, Tesla C1070, Tesla K20

June 17, 2014 by hgpu

A Fast Batched Cholesky Factorization on a GPU

Tingxing Dong, Azzam Haidar, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Tesla K40

June 12, 2014 by hgpu

Mapping parallel programs to heterogeneous multi-core systems

Dominik Grewe

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Thesis

June 5, 2014 by hgpu

Adventures in the microlensing cloud: large datasets, eResearch tools, and GPUs

Georgios Vernardos, Christopher J Fluke

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Package, Tesla C2070, Tesla M2090

June 5, 2014 by hgpu

Loo.py: transformation-based code generation for GPUs and CPUs

Andreas Klockner

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Tags: ATI, ATI Radeon HD 7990, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, Programming Languages, PyOpenCL, Python

June 2, 2014 by hgpu

An implementation of a reordering approach for increasing the product of diagonal entries in a sparse matrix

Ang Li, Radu Serban, Dan Negrut

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Sparse matrix

June 1, 2014 by hgpu

SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

Thomas C.P. Chau, Maciej Kurek, James S. Targett, Jake Humphrey, George Skouroupathis, Alison Eele, Jan Maciejowski, Benjamin Cope, Kathryn Cobden, Philip Leong, Peter Y.K. Cheung, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, nVidia, Package, Tesla C2070

May 29, 2014 by hgpu

Engineering a static verification tool for GPU kernels

Ethel Bardsley, Adam Betts, Nathan Chong, Peter Collingbourne, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema, Daniel Liew, Shaz Qadee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Python

May 25, 2014 by hgpu

GPU accelerated toolbox for real-time beam-shaping in multimode fibres

M. Ploschner, B. Straka, K. Dholakia, T. Cizmar

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Tags: Computer holography, CUDA, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 780, Optics, Package, Physics

May 25, 2014 by hgpu

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 480, Package, Python, Sequence alignment, Tesla K10, Tesla K20

May 25, 2014 by hgpu

Real-time Image Processing on Low Cost Embedded Computers

Sunil Shah

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Tags: ARM, Computer vision, Image processing, OpenCV, Package, Performance, Thesis

May 15, 2014 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

Packages

Medusa: A Parallel Graph Processing System on Graphics Processors

Synergia CUDA: GPU-accelerated accelerator modeling package

A Fast Batched Cholesky Factorization on a GPU

Mapping parallel programs to heterogeneous multi-core systems

Adventures in the microlensing cloud: large datasets, eResearch tools, and GPUs

Loo.py: transformation-based code generation for GPUs and CPUs

An implementation of a reordering approach for increasing the product of diagonal entries in a sparse matrix

SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

Engineering a static verification tool for GPU kernels

GPU accelerated toolbox for real-time beam-shaping in multimode fibres

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Real-time Image Processing on Low Cost Embedded Computers

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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