high performance computing on graphics processing units: hgpu.org

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liquidSVM: A Fast and Versatile SVM package

Ingo Steinwart, Philipp Thomann

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Tags: Computer science, CUDA, Java, nVidia, Package, Python, Spark

February 26, 2017 by hgpu

Dynamic Buffer Overflow Detection for GPGPUs

Christopher Erb, Mike Collins, Joseph L. Greathouse

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Tags: AMD FirePro W9100, ATI, Benchmarking, Computer science, OpenCL, Package, Security

February 22, 2017 by hgpu

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

A. A. Alves Jr, M. D. Sokoloff

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Intel Xeon Phi, Monte Carlo simulation, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan Z, nVidia Quadro K 2200, OpenMP, Package, Physics, Tesla K40

February 22, 2017 by hgpu

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Mathias Bourgoin, Emmanuel Chailloux, Anastasios Doumoulakis

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance

February 18, 2017 by hgpu

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

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Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance

February 14, 2017 by hgpu

cellGPU: massively parallel simulations of dynamic vertex models

Daniel M. Sussman

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Tags: Biological Physics, CUDA, nVidia, Package, Physics, Tesla K40

February 14, 2017 by hgpu

Development of JavaScript-based deep learning platform and application to distributed training

Masatoshi Hidaka, Ken Miura, Tatsuya Harada

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Tags: Computer science, Deep learning, Java, Neural networks, nVidia, OpenCL, Package, Tesla K80, WebCL

February 10, 2017 by hgpu

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Peter Steinbach, Matthias Werner

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Tags: Algorithms, Benchmarking, Computer science, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K80, Tesla P100

February 10, 2017 by hgpu

GraviDy: a GPU modular, parallel N-body integrator

Cristian Maureira-Fredes, Pau Amaro-Seoane

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Tags: Astrophysics, CUDA, Globular clusters, Gravitation, Instrumentation and Methods for Astrophysics, MPI, nVidia, OpenMP, Package, Physics, Tesla C2050, Tesla C2070, Tesla M2050

February 5, 2017 by hgpu

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Rui Fan, Ke Xu, Jichang Zhao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package

January 26, 2017 by hgpu

DeepBach: a Steerable Model for Bach chorales generation

Gaetan Hadjeres, Francois Pachet

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Keras, nVidia, nVidia GeForce GTX 980 Ti, Package

January 23, 2017 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

liquidSVM: A Fast and Versatile SVM package

Dynamic Buffer Overflow Detection for GPGPUs

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

cellGPU: massively parallel simulations of dynamic vertex models

Development of JavaScript-based deep learning platform and application to distributed training

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

GraviDy: a GPU modular, parallel N-body integrator

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

DeepBach: a Steerable Model for Bach chorales generation

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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