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Training Neural Networks Without Gradients: A Scalable ADMM Approach

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

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

May 9, 2016 by hgpu

Parallelizing Word2Vec in Shared and Distributed Memory

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Tesla K40

May 9, 2016 by hgpu

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Jiantao Qiu, Jie Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, Yu Wang, Huazhong Yang

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

May 7, 2016 by hgpu

JIT-Compilation for Interactive Scientific Visualization

J. S. Mueller-Roemer, C. Altenhofen

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Tags: Cloud, Computer science, LLVM, nVidia, nVidia Grid K2, OpenCL, PTX, Visualization, Vulkan

May 7, 2016 by hgpu

Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters

Yongchao Liu, Tony Pan, Srinivas Aluru

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Tags: Algorithms, Bioinformatics, Computer science, Intel Xeon Phi, OpenMP

May 7, 2016 by hgpu

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

Seppo Enarvi, Mikko Kurimo

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, Package, Python, RNN, Speech recognition, Theano

May 7, 2016 by hgpu

TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

Jin-Hwa Kim, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang

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Tags: Computer science, CUDA, Deep learning, Lua, Machine learning, Neural networks, NLP, nVidia, Package, RNN, Torch

May 3, 2016 by hgpu

Automatic Test Case Reduction for OpenCL

Moritz Pflanzer, Alastair F. Donaldson, Andrei Lascu

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Tags: Code generation, Compilers, Computer science, nVidia, nVidia GeForce GTX Titan, OpenCL

May 3, 2016 by hgpu

Polly-ACC: Transparent compilation to heterogeneous hardware

Tobias Grosser, Torsten Hoefler

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, LLVM, nVidia, nVidia GeForce GT 730 M, nVidia GeForce GTX Titan Black

May 3, 2016 by hgpu

Exposing Errors Related to Weak Memory in GPU Applications

Tyler Sorensen, Alastair F. Donaldson

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia Quadro K5200, Tesla C2050, Tesla C2075, Tesla K20

May 3, 2016 by hgpu

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Andreas Klockner, Lucas C. Wilcox, T. Warburton

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Code generation, Computer science, Fortran, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, Prefetch, Programming Languages, Python, Tesla K40

April 29, 2016 by hgpu

On the design of sparse hybrid linear solvers for modern parallel architectures

Stojce Nakov

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, MPI, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla K40, Tesla M2070, Thesis

April 29, 2016 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

Applications

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Parallelizing Word2Vec in Shared and Distributed Memory

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

Automatic Test Case Reduction for OpenCL

Polly-ACC: Transparent compilation to heterogeneous hardware

Exposing Errors Related to Weak Memory in GPU Applications

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

On the design of sparse hybrid linear solvers for modern parallel architectures

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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