high performance computing on graphics processing units: hgpu.org

hgpu.org » Distributed computing

Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation

Ammar Ahmad Awan, Jeroen Bedorf, Ching-Hsiang Chu, Hari Subramoni, Dhabaleswar K. Panda

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Distributed computing, MPI, nVidia, TensorFlow, Tesla K80

November 3, 2018 by hgpu

GPU Virtualization and Scheduling Methods: A Comprehensive Survey

Cheol-Ho Hong, Ivor Spence, Dimitrios S. Nikolopoulos

View

Download (PDF)

Tags: Cloud, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, OpenCL, Review, Virtualization

June 9, 2018 by hgpu

Tiramisu: A Code Optimization Framework for High Performance Systems

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, Linear Algebra, LLVM, MPI, nVidia, OpenMPI, Tesla K40

May 5, 2018 by hgpu

HDArray: Parallel Array Interface for Distributed Heterogeneous Devices

Hyun Dok Cho, Okwan Kwon, Samuel P. Midkiff

View

Download (PDF)

Tags: Computer science, Distributed computing, Heterogeneous systems, MPI, nVidia, OpenCL, Tesla K80, Tesla P100

March 31, 2018 by hgpu

A survey on graphic processing unit computing for large-scale data mining

Alberto Cano

View

Download (PDF)

Tags: Computer science, CUDA, Data mining, Distributed computing, Machine learning, nVidia

November 21, 2017 by hgpu

GPUMap: A Transparently GPU-Accelerated Map Function

Ivan Pachev

View

Download (PDF)

Tags: Computer science, CUDA, Distributed computing, Java, MapReduce, nVidia, nVidia GeForce GTX 980, Python, Thesis

May 22, 2017 by hgpu

Parallelized Kendall’s Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors

Yongchao Liu, Tony Pan, Oded Green, Srinivas Aluru

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, Distributed computing, Intel Xeon Phi, MPI, Package, Sorting

April 15, 2017 by hgpu

Architectural Principles and Experimentation of Distributed High Performance Virtual Clusters

Andrew J. Younge

View

Download (PDF)

Tags: Cloud, Computer science, CUDA, Distributed computing, Molecular dynamics, nVidia, OpenCL, Tesla C2075, Tesla K20, Thesis, Virtualization

March 9, 2017 by hgpu

Akid: A Library for Neural Network Research and Production from a Dataism Approach

Shuai Li

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Distributed computing, Machine learning, Neural networks, nVidia, Package, Python, TensorFlow

January 8, 2017 by hgpu

dOpenCL – Evaluation of an API-Forwarding Implementation

Karsten Tausche, Max Plauth, Andreas Polze

View

Download (PDF)

Source codes

Tags: Computer science, Distributed computing, MPI, OpenCL, Package, Virtualization

December 31, 2016 by hgpu

dCUDA: hardware supported overlap of computation and communication

Tobias Gysi, Jeremia Bär, Torsten Hoefler

View

Download (PDF)

Tags: Distributed computing, latency hiding, programming model, remote memory access

November 29, 2016 by gysit

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Woohyuk Choi, Sumin Hong, Won-Ki Jeong

View

Download (PDF)

Tags: Clustering, Computer science, CUDA, Distributed computing, DSL, GPU cluster, Image processing, MapReduce, nVidia, nVidia GeForce GTX Titan, Python, Rendering, Spark

November 8, 2016 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

Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation

Tiramisu: A Code Optimization Framework for High Performance Systems

HDArray: Parallel Array Interface for Distributed Heterogeneous Devices

A survey on graphic processing unit computing for large-scale data mining

GPUMap: A Transparently GPU-Accelerated Map Function

Parallelized Kendall’s Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors

Akid: A Library for Neural Network Research and Production from a Dataism Approach

dOpenCL – Evaluation of an API-Forwarding Implementation

dCUDA: hardware supported overlap of computation and communication

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

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)