high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

January 20, 2018 by hgpu

Evaluation of Machine Learning Fameworks on Finis Terrae II

Andres Gomez Tato

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Tesla K80

January 20, 2018 by hgpu

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Compilers, Heterogeneous systems, Image processing, Machine learning, nVidia, nVidia GeForce GTX 960, OpenCL, Package, performance portability, Tesla K40

January 13, 2018 by hgpu

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Zeyi Wen, Jiashuai Shi, Bingsheng He, Qinbin Li, Jian Chen

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, OpenMP, Package, Tesla P100

January 6, 2018 by hgpu

A Generic Inverted Index Framework for Similarity Search on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Lubos Krcal, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Hashing, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, OpenMPI, Package

December 28, 2017 by hgpu

A Survey of FPGA Based Neural Network Accelerator

Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang

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Tags: Computer science, Computer vision, FPGA, Machine learning, Neural networks

December 28, 2017 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

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Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

A survey on graphic processing unit computing for large-scale data mining

Alberto Cano

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Tags: Computer science, CUDA, Data mining, Distributed computing, Machine learning, nVidia

November 21, 2017 by hgpu

Unified Deep Learning with CPU, GPU, and FPGA Technologies

Allen Rush, Ashish Sirasao, Mike Ignatowski

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Machine learning, OpenCL

November 21, 2017 by hgpu

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

Peter Boyle, Michael Chuvelev, Guido Cossu, Christopher Kelly, Christoph Lehner, Lawrence Meadows

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, MPI, OpenMP, Package, Performance, Physics

November 16, 2017 by hgpu

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Ole Martin Skafsa

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Tags: CNN, Computer science, Deep learning, FPGA, Machine learning, Neural networks, NLP, nVidia, nVidia Tegra TX1, OpenCL, Thesis

October 3, 2017 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Evaluation of Machine Learning Fameworks on Finis Terrae II

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

ThunderSVM: A Fast SVM Library on GPUs and CPUs

A Generic Inverted Index Framework for Similarity Search on the GPU

A Survey of FPGA Based Neural Network Accelerator

An MPI-Based Python Framework for Distributed Training with Keras

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

A survey on graphic processing unit computing for large-scale data mining

Unified Deep Learning with CPU, GPU, and FPGA Technologies

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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