high performance computing on graphics processing units: hgpu.org

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Tuned and GPU-accelerated parallel data mining from comparable corpora

Krzysztof Wolk, Krzysztof Marasek

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Tags: Algorithms, Computer science, Data mining, Machine learning, NLP, nVidia, nVidia GeForce GTX 660

October 3, 2015 by hgpu

Brute-Force k-Nearest Neighbors Search on the GPU

Shengren Li, Nina Amenta

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Tags: Computer science, CUDA, Matrix multiplication, Nearest neighbour, nVidia, nVidia GeForce GTX 460, Sorting

October 3, 2015 by hgpu

Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards

Ang Li, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Package, Sparse direct solvers, Tesla K20

September 30, 2015 by hgpu

Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic

Mujeeb B Jimoh

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX Titan, Security, Thesis

September 29, 2015 by hgpu

The Dynamical Kernel Scheduler – Part 1

Andreas Adelmann, Uldis Locans, Andreas Suter

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Tags: Algorithms, Computational Physics, CUDA, FFT, Intel Xeon Phi, Monte Carlo simulation, nVidia, OpenCL, OpenMP, Physics

September 29, 2015 by hgpu

A Design Framework for Mapping Dataflow Graphs onto Heterogeneous Multiprocessor Platforms

Shuoxin Lin

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Tags: Benchmarking, Computer science, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, Thesis

September 29, 2015 by hgpu

Solving prime-field ECDLPs on GPUs with OpenCL

Erik Boss

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Tags: ATI, ATI Radeon HD 7850, Computer science, nVidia, nVidia GeForce GTX 780, OpenCL, Security, Thesis

September 29, 2015 by hgpu

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh

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Tags: ARM, ASIC, Computer science, FPGA, Machine learning, nVidia, nVidia GeForce GTX 650 Ti, nVidia Jetson TK1, Performance, Tesla K40

September 29, 2015 by hgpu

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

John-Alexander M. Assael

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Tags: CNN, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Tesla K40, Thesis

September 26, 2015 by hgpu

A GPU accelerated Barnes-Hut Tree Code for FLASH4

Gunther Lukat, Robi Banerjee

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Tags: Astrophysics, CUDA, Fortran, Gravitation, Instrumentation and Methods for Astrophysics, nVidia, Package, Python, Tesla C2075

September 26, 2015 by hgpu

Efficient Simulation Techniques for Large-Scale Applications

Jen-Cheng Huang

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Tags: Analytical model, Computer science, CUDA, nVidia, nVidia Quadro 6000, Performance, Thesis

September 26, 2015 by hgpu

Fast Exact Bayesian Inference for High-Dimensional Models

Joao Filipe Ferreira, Pablo Lanillos, Jorge Dias

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Tags: Bayesian, Computer science, Computer vision, nVidia, nVidia GeForce GTX 780, OpenCL, Robotics

September 26, 2015 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

Tuned and GPU-accelerated parallel data mining from comparable corpora

Brute-Force k-Nearest Neighbors Search on the GPU

Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards

Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic

The Dynamical Kernel Scheduler – Part 1

A Design Framework for Mapping Dataflow Graphs onto Heterogeneous Multiprocessor Platforms

Solving prime-field ECDLPs on GPUs with OpenCL

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

A GPU accelerated Barnes-Hut Tree Code for FLASH4

Efficient Simulation Techniques for Large-Scale Applications

Fast Exact Bayesian Inference for High-Dimensional Models

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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