high performance computing on graphics processing units: hgpu.org

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Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

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Tags: Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX 1080, Tesla P100

March 10, 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

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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

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Tags: Computer science, CUDA, Deep learning, GPU cluster, Neural networks, nVidia, OpenMPI, Performance, Tesla P100, Tesla V100

February 24, 2019 by hgpu

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

Peifeng Yu, Mosharaf Chowdhury

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

February 17, 2019 by hgpu

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

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

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

February 10, 2019 by hgpu

Heterogeneous Distributed Big Data Clustering on Sparse Grids

David Pfander, Gregor Daiss, Dirk Pfluger

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Tags: Clustering, Computer science, Data mining, Distributed computing, Heterogeneous systems, Machine learning, MPI, nVidia, OpenCL, Package, performance portability, Tesla P100

February 10, 2019 by hgpu

Hierarchical Matrix Operations on GPUs: Matrix-Vector Multiplication and Compression

Wajih Halim Boukaram, George Turkiyyah, David E. Keyes

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Tags: Compression, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Tesla P100

February 10, 2019 by hgpu

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Zeyi Wen, Jiashuai Shi, Bingsheng He, Qinbin Li, Jian Chen

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, Package, Python, Tesla P100

February 3, 2019 by hgpu

High Performance Algorithms for Counting Collisions and Pairwise Interactions

Matheus Henrique Junqueira Saldanha, Paulo Sérgio Lopes de Souza

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Tags: Collision detection, Computer science, CUDA, nVidia, Package, Tesla P100

February 3, 2019 by hgpu

BitCracker: BitLocker meets GPUs

Elena Agostini, Massimo Bernaschi

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Tags: AES, ATI, ATI Radeon HD 7990, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Security, Tesla K80, Tesla P100, Tesla V100

January 13, 2019 by hgpu

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, Yanzhi Wang

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Tags: Caffe, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

January 6, 2019 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

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

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

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

Heterogeneous Distributed Big Data Clustering on Sparse Grids

Hierarchical Matrix Operations on GPUs: Matrix-Vector Multiplication and Compression

ThunderGBM: Fast GBDTs and Random Forests on GPUs

High Performance Algorithms for Counting Collisions and Pairwise Interactions

BitCracker: BitLocker meets GPUs

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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