high performance computing on graphics processing units: hgpu.org

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Massive Exploration of Neural Machine Translation Architectures

Denny Britz, Anna Goldie, Thang Luong, Quoc Le

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Tags: Computer science, Deep learning, Neural networks, NLP, nVidia, Package, Python, TensorFlow, Tesla K40

March 14, 2017 by hgpu

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

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Tags: Computer science, Computer vision, Databases, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1, Package, TensorFlow, Tesla P100

March 9, 2017 by hgpu

Improving the Neural GPU Architecture for Algorithm Learning

Karlis Freivalds, Renars Liepins

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, Memory model, nVidia, Package, Python, TensorFlow, Tesla K40

March 5, 2017 by hgpu

Billion-scale similarity search with GPUs

Jeff Johnson, Matthijs Douze, Herve Jegou

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Package

March 5, 2017 by hgpu

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Wensen Feng, Yunjin Chen

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Tags: CNN, Computer vision, Filtering, Image processing, Matlab, nVidia, nVidia GeForce GXT 780 Ti, Package, SAR

February 28, 2017 by hgpu

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

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

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

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

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

Packages

Massive Exploration of Neural Machine Translation Architectures

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Improving the Neural GPU Architecture for Algorithm Learning

Billion-scale similarity search with GPUs

Speckle Reduction with Trained Nonlinear Diffusion Filtering

liquidSVM: A Fast and Versatile SVM package

Dynamic Buffer Overflow Detection for GPGPUs

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

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

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

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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