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hgpu.org » AMD

APUNet: Revitalizing GPU as Packet Processing Accelerator

Younghwan Go, Muhammad Jamshed, YoungGyoun Moon, Changho Hwang, KyoungSoo Park
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March 28, 2017 by hgpu

TREES: A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization

Blake A. Hechtman, Andrew D. Hilton, Daniel J. Sorin
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August 4, 2016 by hgpu

Fast reconstruction of 3D volumes from 2D CT projection data with GPUs

M. Leeser, S. Mukherjee, J. Brock
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September 1, 2014 by hgpu

Implementation of Just In Time Value Specialization for the Optimization of Data Parallel Kernels

N. Tuck
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March 28, 2014 by hgpu

Multi-tier Dynamic Vectorization for Translating GPU Optimizations into CPU Performance

Hee-Seok Kim, Izzat El Hajj, John A. Stratton, Wen-Mei W. Hwu
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February 12, 2014 by hgpu

Hybrid strategy for stencil computations on the APU

Pacome Eberhart, Issam Said, Pierre Fortin
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January 26, 2014 by hgpu

OmniDB: Towards Portable and Efficient Query Processing on Parallel CPU/GPU Architectures

Shuhao Zhang, Jiong He, Bingsheng He, Mian Lu
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July 12, 2013 by hgpu

Recent source codes

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications
CL-kmeans: Efficient k-Means in OpenCL

CL-kmeans: Efficient k-Means in OpenCL

Efficient and Scalable k-Means on GPUs
Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records
pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms
CUTLASS: CUDA Templates for Linear Algebra Subroutines

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs
Integer sorting on multicore

Integer sorting on multicore

A study of integer sorting on multicores
Kernel Tuner: A search-optimizing GPU code auto-tuner

Kernel Tuner: A search-optimizing GPU code auto-tuner

Kernel Tuner: A search-optimizing GPU code auto-tuner
libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
See all packages

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