high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

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

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

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

Worst-Case Execution Time Guarantees for Runtime-Reconfigurable Architectures

Marvin Damschen

View

Download (PDF)

Tags: Computer science, OpenCL, Performance, Thesis, Timing analysis

February 24, 2019 by hgpu

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello

View

Download (PDF)

Tags: Compression, Computer science, Deep learning, Neural networks, nVidia, Performance, Tesla V100

February 3, 2019 by hgpu

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Chao Wu, Yaoxue Zhang, Yuezhi Zhou, Qiushi Li

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance

November 18, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

Power analysis of sorting algorithms on FPGA using OpenCL

Aidan O Mahony, Emanuel Popovici

View

Download (PDF)

Tags: Computer science, Energy-efficient computing, FPGA, OpenCL, Performance, Sorting

November 3, 2018 by hgpu

Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering

Ricardo Nobre, Luis Reis, Joao M. P. Cardoso

View

Download (PDF)

Tags: Computer science, nVidia, nVidia GeForce GTX 1070, OpenCL, Performance, PTX

October 28, 2018 by hgpu

A study of integer sorting on multicores

Alexandros V. Gerbessiotis

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, MPI, Package, Performance, Sorting

September 2, 2018 by hgpu

Performance Evaluation of OpenMP’s Target Construct on GPUs – Exploring Compiler Optimizations

Akihiro Hayashi, Jun Shirako, Etorre Tiotto, Robert Ho, Vivek Sarkar

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Performance, Tesla K80, Tesla P100

August 26, 2018 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Analyzing GPU Tensor Core Potential for Fast Reductions

TensorFlow Doing HPC

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

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

Worst-Case Execution Time Guarantees for Runtime-Reconfigurable Architectures

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Power analysis of sorting algorithms on FPGA using OpenCL

Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering

A study of integer sorting on multicores

Performance Evaluation of OpenMP’s Target Construct on GPUs – Exploring Compiler Optimizations

Recent source codes

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

Gaia AVU-GSR

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

Most viewed papers (last 30 days)