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A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

Hasmik Osipyan, Martin Krulis, Stephane Marchand-Maillet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Multidimensional scaling, nVidia, nVidia GeForce GT 740 M, Pattern recognition

September 26, 2015 by hgpu

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Vlad Olaru, Mihai Florea, Cristian Sminchisescu

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Tags: Algorithms, Computer science, Computer vision, CUDA, Matlab, nVidia, nVidia GeForce GTX Titan Black, Package, Tesla K40

September 24, 2015 by hgpu

Adaptive and Transparent Cache Bypassing for GPUs

Ang Li, Gert-Jan van den Braak, Akash Kumar, Henk Corporaal

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Tags: Benchmarking, cache, Computer science, Memory, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 570, nVidia GeForce GTX 690, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 980, Performance, PTX, Tesla K40, Tesla K80

September 24, 2015 by hgpu

Parallel Decompression of Seismic Data on GPU Using a Lifting Wavelet Algorithm

Jairo A. Castelar, Carlos A. Angulo, Carlos A. Fajardo

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Tags: Algorithms, CUDA, Earth and Space Sciences, GPU cluster, nVidia, nVidia GeForce GTX 660, Wavelet

September 19, 2015 by hgpu

Autotuning Wavefront Patterns for Heterogeneous Architectures

Siddharth Mohanty

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

September 19, 2015 by hgpu

Automatic OpenCL code generation for multi-device heterogeneous architectures

Pei Li, Elisabeth Brunet, Francois Trahay, Christian Parrot, Gael Thomas, Raymond Namyst

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Tags: Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro FX 5800, OpenCL

September 19, 2015 by hgpu

Efficient Kernel Fusion Techniques for Massive Video Data Analysis on GPGPUs

Asif M Adnan, Sridhar Radhakrishnan, Suleyman Karabuk

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Tags: Algorithms, CUDA, Image processing, Neuroscience, nVidia, nVidia GeForce GTX 750 Ti, Tesla C1060, Tesla K20

September 17, 2015 by hgpu

SKMD: Single Kernel on Multiple Devices for Transparent CPU-GPU Collaboration

Janghaeng Lee, Mehrzad Samadi, Yongjun Park, Scott Mahlke

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 760, OpenCL

September 17, 2015 by hgpu

CLTune: A Generic Auto-Tuner for OpenCL Kernels

Cedric Nugteren, Valeriu Codreanu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel HD 5100, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Performance, Tesla K40

September 17, 2015 by hgpu

gSLICr: SLIC superpixels at over 250Hz

Carl Yuheng Ren, Victor Adrian Prisacariu, Ian D Reid

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, OpenCV, Package

September 17, 2015 by hgpu

Scalable Metropolis Monte Carlo for simulation of hard shapes

Joshua A. Anderson, M. Eric Irrgang, Sharon C. Glotzer

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Tags: Benchmarking, Chemistry, Condensed matter, CUDA, Monte Carlo simulation, nVidia, Physics, Soft Condensed Matter, Tesla K20

September 17, 2015 by hgpu

linalg: Matrix Computations in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Sparks, Alexander Ulanov, Matei Zaharia

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Scala, Tesla M2050

September 15, 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

A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Adaptive and Transparent Cache Bypassing for GPUs

Parallel Decompression of Seismic Data on GPU Using a Lifting Wavelet Algorithm

Autotuning Wavefront Patterns for Heterogeneous Architectures

Automatic OpenCL code generation for multi-device heterogeneous architectures

Efficient Kernel Fusion Techniques for Massive Video Data Analysis on GPGPUs

SKMD: Single Kernel on Multiple Devices for Transparent CPU-GPU Collaboration

CLTune: A Generic Auto-Tuner for OpenCL Kernels

gSLICr: SLIC superpixels at over 250Hz

Scalable Metropolis Monte Carlo for simulation of hard shapes

linalg: Matrix Computations in Apache Spark

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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