high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla V100

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader

View

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Performance, Sparse, Tesla P100, Tesla V100

October 13, 2019 by hgpu

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand

View

Download (PDF)

Source codes

Tags: 3D Graphics and Realism, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 1080 Ti, Package, Rendering, Tesla V100

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

View

Download (PDF)

Source codes

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

September 29, 2019 by hgpu

Elastic deep learning in multi-tenant GPU cluster

Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, James Cheng

View

Download (PDF)

Tags: Computer science, Deep learning, Distributed computing, GPU cluster, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

September 29, 2019 by hgpu

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, NLP, nVidia, nVidia DGX-2, Package, Tesla V100

September 22, 2019 by hgpu

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu

View

Download (PDF)

Tags: AI, AMD Radeon R7, ATI, Computer science, Deep learning, Neural networks, nVidia, PyTorch, TensorFlow, Tesla P100, Tesla V100, TPU

September 22, 2019 by hgpu

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Abhinav Jangda, Arjun Guha

View

Download (PDF)

Tags: CUDA, Image procesing, nVidia, nVidia GeForce GTX 1080 Ti, Python, Tesla V100

September 22, 2019 by hgpu

Efficient Interleaved Batch Matrix Solvers for CUDA

Andrew Gloster, Enda Carroll, Miguel Bustamante, Lennon O'Naraigh

View

Download (PDF)

Source codes

Tags: Algorithms, Computational Physics, Computer science, CUDA, Matrix inversion, nVidia, Package, Tesla V100

September 15, 2019 by hgpu

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

Eleftherios Avramidis, Marta Lalik, Ozgur E. Akman

View

Download (PDF)

Source codes

Tags: AMD FirePro W8100, ATI, Computer science, Differential equations, Mathematical Software, nVidia, OpenCL, Package, Tesla V100

August 18, 2019 by hgpu

Performance Comparison for Neuroscience Application Benchmarks

Andreas Herten, Thorsten Hater, Wouter Klijn, Dirk Pleiter

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Neuroscience, nVidia, Performance, Tesla P100, Tesla V100

August 11, 2019 by hgpu

GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

July 28, 2019 by hgpu

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

Faial: finds bugs in CUDA kernels

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Intel® SHMEM: Device initiated shared memory based communication library

Intel(R) SHMEM: GPU-initiated OpenSHMEM using SYCL

miniLB: Lattice Botlzmann miniapp w/SYCL

miniLB: A Performance Portability Study of Lattice-Boltzmann Simulations

AFOCL

Bitstream Database-Driven FPGA Programming Flow Based on Standard OpenCL

2domination

HPC acceleration of large (min, +) matrix products to compute domination-type parameters in graphs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Exascale Deep Learning for Scientific Inverse Problems

Elastic deep learning in multi-tenant GPU cluster

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Efficient Interleaved Batch Matrix Solvers for CUDA

Demystifying the MLPerf Benchmark Suite

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

Performance Comparison for Neuroscience Application Benchmarks

GPU-Accelerated Atari Emulation for Reinforcement Learning

Recent source codes

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

EnergyUCB-Bandit

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

Faial: finds bugs in CUDA kernels

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Intel® SHMEM: Device initiated shared memory based communication library

miniLB: Lattice Botlzmann miniapp w/SYCL

AFOCL

2domination

Most viewed papers (last 30 days)