high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

A Graph-based Model for GPU Caching Problems

Lingda Li, Ari B. Hayes, Stephen A. Hackler, Eddy Z. Zhang, Mario Szegedy, Shuaiwen Leon Song

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 680, Task scheduling

May 9, 2016 by hgpu

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 Wavelet Schemes for Images

David Barina, Michal Kula, Pavel Zemcik

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Tags: AMD Radeon R9 290X, AMD Radeon R9 Fury, ATI, Compression, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan X, OpenCL, Package

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

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

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

Fast hyperbolic Radon transform represented as convolutions in log-polar coordinates

Viktor V. Nikitin, Fredrik Andersson, Marcus Carlsson, Anton A. Duchkov

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Tags: CUDA, FFT, Geoscience, Mathematics, Numerical Analysis, nVidia, Radon transform, Seismic modeling

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

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

A Graph-based Model for GPU Caching Problems

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 Wavelet Schemes for Images

TheanoLM – An Extensible Toolkit for Neural Network Language Modeling

Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters

TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing

Fast hyperbolic Radon transform represented as convolutions in log-polar coordinates

Automatic Test Case Reduction for OpenCL

Polly-ACC: Transparent compilation to heterogeneous hardware

Recent source codes

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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