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Practical Algorithms for Finding Extremal Sets

Martin Marinov, Nicholas Nash, David Gregg

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 580, Package

August 10, 2015 by hgpu

CRINK: Automatic CUDA code generation for affine C programs

Akanksha Singh

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Tags: Code generation, Computer science, CUDA, nVidia, Tesla C1060, Thesis

August 10, 2015 by hgpu

DenseCut: Densely Connected CRFs for Realtime GrabCut

M. M. Cheng, V. A. Prisacariu, S. Zheng, P. H. S. Torr, C. Rother

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Tags: Computer vision, CUDA, Image processing, nVidia, Package, Tesla K40

August 7, 2015 by hgpu

Towards Distortion-Predictable Embedding of Neural Networks

Axel Angel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Thesis

August 7, 2015 by hgpu

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

Artur Malinowski

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Tags: Algorithms, Computer science, Intel Xeon Phi, Java

August 7, 2015 by hgpu

Optimising Reconfigurable Systems for Real-time Applications

Thomas Chun Pong Chau

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, Mixed precision, nVidia, Package, Tesla C2070, Thesis

August 7, 2015 by hgpu

Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

Biagio Cosenza

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 960, OpenCL, OpenGL

August 7, 2015 by hgpu

A Survey Of Techniques for Architecting DRAM Caches

Sparsh Mittal, Jeffrey Vetter

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Tags: 3D DRAM, bandwidth, cpu, die-stacking, GPU, Memory, survey

August 7, 2015 by sparsh0mittal

Real-Time Pedestrian Detection With Deep Networks Cascades

Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K20

August 6, 2015 by hgpu

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Terry Cojean, Abdou Guermouche, Andra Hugo, Raymond Namyst, Pierre-Andre Wacrenier

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, OpenMP, Task scheduling, Tesla K40

August 5, 2015 by hgpu

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Jeremie Papon, Markus Schoeler

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Tags: CNN, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Rendering

August 5, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Practical Algorithms for Finding Extremal Sets

CRINK: Automatic CUDA code generation for affine C programs

DenseCut: Densely Connected CRFs for Realtime GrabCut

Towards Distortion-Predictable Embedding of Neural Networks

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

Optimising Reconfigurable Systems for Real-time Applications

Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

A Survey Of Techniques for Architecting DRAM Caches

Real-Time Pedestrian Detection With Deep Networks Cascades

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

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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