high performance computing on graphics processing units: hgpu.org

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Generic Inverted Index on the GPU

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

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Tags: Computer science, CUDA, Databases, Hashing, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Tesla K80

March 29, 2016 by hgpu

GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

Tetsuya Takaishi

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Tags: Bayesian, Computational finance, CUDA, Finance, Fortran, nVidia, nVidia GeForce GTX 760, OpenACC

March 29, 2016 by hgpu

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Pascal Vincent, Alexandre de Brebisson, Xavier Bouthillier

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black, Package

March 25, 2016 by hgpu

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

Linnan Wang, Yi Yang, Martin Renqiang Min, Srimat Chakradhar

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Tags: Caffe, Computer science, CUDA, Data parallelism, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

March 25, 2016 by hgpu

An Efficient Implementation of the Longest Common Subsequence Algorithm with Bit-Parallelism on GPUs

Katsuya Kawanami

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, Thesis

March 25, 2016 by hgpu

Wanted: Floating-Point Add Round-off Error instruction

Marat Dukhan, Richard Vuduc, Jason Riedy

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Tags: Computer science, CUDA, Matrix multiplication, OpenCL, Package

March 25, 2016 by hgpu

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Lukas Einkemmer

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Tags: Computational Physics, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, Mixed precision, nVidia, OpenMP, Tesla K80

March 25, 2016 by hgpu

Recurrent neural networks for language modeling

Emil Sauer Lynge

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Tags: Computer science, CUDA, Deep learning, LSTM, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Python, RNN, Thesis

March 22, 2016 by hgpu

Acceleration of ensemble machine learning methods using many-core devices

A. Tamerus, A. Washbrook, D. Wyeth

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Tags: Computer science, CUDA, Machine learning, nVidia, Physics, Tesla K40

March 20, 2016 by hgpu

A Massively Parallel Algorithm for Cell Classification Using CUDA

Samuel Schmidt

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Tags: Bioinformatics, Biology, CUDA, Machine learning, nVidia, nVidia GeForce GTX 770, RNA, Thesis

March 20, 2016 by hgpu

Analyzing and Improving the Performance of Spatial Database Processing

Bogdan Simion

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Thesis

March 20, 2016 by hgpu

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, Fahim Kawsar

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia Jetson TK1, SoC

March 15, 2016 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Generic Inverted Index on the GPU

GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

An Efficient Implementation of the Longest Common Subsequence Algorithm with Bit-Parallelism on GPUs

Wanted: Floating-Point Add Round-off Error instruction

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Recurrent neural networks for language modeling

Acceleration of ensemble machine learning methods using many-core devices

A Massively Parallel Algorithm for Cell Classification Using CUDA

Analyzing and Improving the Performance of Spatial Database Processing

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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