high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla V100

Elastic deep learning in multi-tenant GPU cluster

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

View

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

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

View

Download (PDF)

Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 by hgpu

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla P100, Tesla V100

July 21, 2019 by hgpu

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Wei Zhang, Xiaodong Cui, Ulrich Finkler, George Saon, Abdullah Kayi, Alper Buyuktosunoglu, Brian Kingsbury, David Kung, Michael Picheny

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, Distributed computing, GPU cluster, nVidia, OpenMPI, Speech recognition, Tesla V100

July 21, 2019 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

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

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

Most viewed papers (last 30 days)