high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

View

Download (PDF)

Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Tomas Oehberg, August Ernstsson, Christoph Kessler

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, MapReduce, nVidia, OpenCL, Package, Tesla K20

March 31, 2019 by hgpu

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Jiarui Fang, Liandeng Li, Haohuan Fu, Jinlei Jiang, Wenlai Zhao, Conghui He, Xin You, Guangwen Yang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, OpenMPI, Package, Tesla K40

March 24, 2019 by hgpu

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Hamidreza Khaleghzadeh

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Package, Thesis

March 17, 2019 by hgpu

Analyzing GPU Tensor Core Potential for Fast Reductions

Roberto Carrasco, Raimundo Vega, Cristóbal A. Navarro

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

March 17, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 2019 by hgpu

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

Paul Sathre, Mark Gardner, Wu-chun Feng

View

Download (PDF)

Source codes

Tags: AMD FirePro S9150, ATI, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, OpenCL, Package, performance portability, Tesla K80

March 10, 2019 by hgpu

Custom Code Generation for a Graph DSL

Bikash Gogoi, Unnikrishnan Cheramangalath, Rupesh Nasre

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, DSL, nVidia, Tesla K40

March 10, 2019 by hgpu

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

Tal Ben-Nun, Johannes de Fine Licht, Alexandros Nikolaos Ziogas, Timo Schneider, Torsten Hoefler

View

Download (PDF)

Tags: Computer science, CUDA, FPGA, nVidia, OpenCL, Optimization, Performance, Tesla P100

March 3, 2019 by hgpu

cuSten – CUDA Finite Difference and Stencil Library

Andrew Gloster, Lennon O'Naraigh

View

Download (PDF)

Source codes

Tags: Computational Physics, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX Titan X, Package, PDEs

March 3, 2019 by hgpu

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

Peng Sun, Wansen Feng, Ruobing Han, Shengen Yan, Yonggang Wen

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, GPU cluster, Neural networks, nVidia, OpenMPI, Performance, Tesla P100, Tesla V100

February 24, 2019 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Analyzing GPU Tensor Core Potential for Fast Reductions

TensorFlow Doing HPC

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

Custom Code Generation for a Graph DSL

Stateful Dataflow Multigraphs: A Data-Centric Model for High-Performance Parallel Programs

cuSten – CUDA Finite Difference and Stencil Library

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Most viewed papers (last 30 days)