high performance computing on graphics processing units: hgpu.org

Programming

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MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU

Jiaying Feng, Xiaowang Zhang, Zhiyong Feng

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Databases, MapReduce, nVidia, nVidia GeForce GTX 590

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

Improved Lossless Image Compression Model Using Coefficient Based Discrete Wavelet Transform

T. Velumani, S. Sukumaran

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Tags: Compression, Computational Complexity, Discrete Wavelet Transform, Image processing, OpenCL, Probability

February 14, 2017 by hgpu

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr

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Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, 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

GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device

Yi-Yan Nan, Quan-Zhe Li, Jin-Chun Piao, Shin-Dug Kim

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Tags: Algorithms, AMD Radeon R9 M200X, Android, ATI, Computer science, Image processing, Matrix multiplication, OpenCL, Pattern recognition

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

Backpropagation Training for Fisher Vectors within Neural Networks

Patrick Wieschollek, Fabian Groh, Hendrik P.A. Lensch

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan X

February 10, 2017 by hgpu

Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs

Xiangyu Guo, Qi Chu, Shin Kee Chung, Zhihui Du, Linqing Wen

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Tags: Astrophysics, CUDA, Filtering, Gravitation, Gravitational waves, nVidia, nVidia GeForce GTX 980

February 10, 2017 by hgpu

Machines and Algorithms

Peter A Boyle

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Tags: Algorithms, Computational Physics, CUDA, HEP, High Energy Physics - Lattice, Intel Xeon Phi, nVidia, Physics, QCD, Review

February 7, 2017 by hgpu

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

Maciej Wielgosz, Andrzej Skoczen, Matej Mertik

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Tags: CUDA, Deep learning, Instrumentation and Detectors, Keras, LSTM, Machine learning, Neural networks, nVidia, Physics, RNN, Tesla K80, Theano

February 7, 2017 by hgpu

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU

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

cellGPU: massively parallel simulations of dynamic vertex models

Improved Lossless Image Compression Model Using Coefficient Based Discrete Wavelet Transform

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

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

GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Backpropagation Training for Fisher Vectors within Neural Networks

Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs

Machines and Algorithms

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

Recent source codes

Awesome LLM-Driven Kernel Generation

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

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