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MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Jieyang Chen, Chenhao Xie, Jesun S Firoz, Jiajia Li, Shuaiwen Leon Song, Kevin Barker, Mark Raugas, Ang Li

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, OpenMP, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Yun Fei, Yuhan Huang, Ming Gao

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Tags: Computer science, CUDA, Graphics, nVidia, nVidia DGX-1, Physics-based simulation, Tesla V100

November 7, 2021 by hgpu

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Akio Hayakawa, Masato Ishii, Yoshiyuki Kobayashi, Akira Nakamura, Takuya Narihira, Yukio Obuchi, Andrew Shin, Takuya Yashima, Kazuki Yoshiyama

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, Package, Tesla V100

March 7, 2021 by hgpu

Design, Implementation and Test of Efficient GPU to GPU Communication Methods

Stepan Vanecek

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Tags: AMD Radeon Instinct Mi50, ATI, Computer science, Heterogeneous systems, HIP, MPI, nVidia DGX-1, Thesis

January 3, 2021 by hgpu

A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs

Lingqi Zhang, Mohamed Wahib, Haoyu Zhang, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia DGX-1, Package, Performance, Tesla P100, Tesla V100

April 19, 2020 by hgpu

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

Jiajia Li, Mahesh Lakshminarasimhan, Xiaolong Wu, Ang Li, Catherine Olschanowsky, Kevin Barker

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Tags: Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia DGX-1, Package, Performance, Tesla P100, Tesla V100

January 12, 2020 by hgpu

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Ryan Senanayake, Fredrik Kjolstad, Changwan Hong, Shoaib Kamil, Saman Amarasinghe

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Tags: Code generation, Compilers, Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, Package, Sparse matrix, Tesla V100

January 5, 2020 by hgpu

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, Machine learning, nVidia, nVidia DGX-1, Tesla V100

October 20, 2019 by hgpu

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

Improving Resource Efficiency in Virtualized Datacenters

Marcelo Amaral

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Tags: Cloud, Computer science, CUDA, nVidia, nVidia DGX-1, Thesis, Virtualization

May 12, 2019 by hgpu

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Design, Implementation and Test of Efficient GPU to GPU Communication Methods

A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Blink: Fast and Generic Collectives for Distributed ML

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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