high performance computing on graphics processing units: hgpu.org

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CuMF_SGD: Fast and Scalable Matrix Factorization

Xiaolong Xie, Wei Tan, Liana L. Fong, Yun Liang

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Tags: Computer science, CUDA, Factorization, Machine learning, nVidia, nVidia GeForce GTX Titan X, Package

October 22, 2016 by hgpu

Machine Learning Based Intrusion Detection in Controller Area Networks

Roar Elias Georgsen

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Tags: Computer science, Deep learning, DSP, FPGA, Machine learning, OpenCL, Package, Python, Security, SoC

October 15, 2016 by hgpu

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

Daniel Hernandez-Juarez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, Antonio Manuel Lopez

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Package

October 15, 2016 by hgpu

GPU-accelerated real-time stixel computation

Daniel Hernandez-Juarez, Antonio Espinosa, David Vazquez, Antonio Manuel Lopez, Juan Carlos Moure

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Package

October 15, 2016 by hgpu

Neural Network Computing Using On-Chip Accelerators

Schuyler Eldridge

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Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, Neural networks, Package, Thesis

October 12, 2016 by hgpu

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi

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Tags: Benchmarking, Caffe, Computer science, Deep learning, FPGA, Machine learning, Neural networks, OpenCL, Package

October 4, 2016 by hgpu

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Troels Henriksen, Martin Dybdal, Henrik Urms, Anna Sofie Kiehn, Daniel Gavin, Hjalte Abelskov, Martin Elsman, Cosmin Oancea

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Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, PyOpenCL, Python, Visualization

October 4, 2016 by hgpu

Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package, Theano

October 4, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

Solving Batched Linear Programs on GPU and Multicore CPU

Amit Gurung, Rajarshi Ray

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Tags: Computer science, CUDA, nVidia, Package, Performance, Tesla K40

September 27, 2016 by hgpu

Tuning Stencil Codes in OpenCL for FPGAs

Qi Jia, Huiyang Zhou

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Package, Stencil computation

September 22, 2016 by hgpu

MCS 572: Introduction to Supercomputing

Jan Verschelde

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Tags: Algorithms, Computer science, CUDA, MPI, Numerical Analysis, nVidia, nVidia GeForce 9400 M, OpenMP, Package, Tesla C2050, Tesla C2070, Tesla K20, Tutorial

September 22, 2016 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

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high performance computing on graphics processing units: hgpu.org

Packages

CuMF_SGD: Fast and Scalable Matrix Factorization

Machine Learning Based Intrusion Detection in Controller Area Networks

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

GPU-accelerated real-time stixel computation

Neural Network Computing Using On-Chip Accelerators

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Training a Feedback Loop for Hand Pose Estimation

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Solving Batched Linear Programs on GPU and Multicore CPU

Tuning Stencil Codes in OpenCL for FPGAs

MCS 572: Introduction to Supercomputing

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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