high performance computing on graphics processing units: hgpu.org

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Fast convolutional neural networks on FPGAs with hls4ml

Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, Package

January 17, 2021 by hgpu

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique

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Tags: Artificial intelligence, Benchmarking, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Medicine, Neural networks, nVidia, Tesla V100

December 27, 2020 by hgpu

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

Alexander Matz

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, Package, Thesis

December 27, 2020 by hgpu

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

Anderson Faustino da Silva, Bruno Conde Kind, Jose Wesley de Souza Magalhaes, Jeronimo Nunes Rocha, Breno Campos Ferreira Guimaraes, Fernando Magno Quintao Pereira

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Tags: Algorithms, Benchmarking, Code generation, Compiler, Computer science, LLVM, Package

December 20, 2020 by hgpu

Toward Accurate Platform-Aware Performance Modeling for Deep Neural Networks

Chuan-Chi Wang, Ying-Chiao Liao, Ming-Chang Kao, Wen-Yew Liang, Shih-Hao Hung

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Performance

December 6, 2020 by hgpu

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Beau Johnston, Jeffrey S. Vetter, Josh Milthorpe

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Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, CUDA, Heterogeneous systems, HIP, Matrix multiplication, nVidia, OpenCL, Package, Performance, Tesla P100

November 29, 2020 by hgpu

Runtime Performances Benchmark for Knowledge Graph Embedding Methods

Angelica Sofia Valeriani

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 960, Package

November 15, 2020 by hgpu

When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization

Young-kyu Choi, Yuze Chi, Jie Wang, Licheng Guo, Jason Cong

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Tags: Benchmarking, Computer science, FPGA, HLS, OpenCL, Optimization

October 18, 2020 by hgpu

LoopBench: An Evaluation of Loop Acceleration in Heterogeneous Systems

Saman Biookaghazadeh, Fengbo Ren, Ming Zhao

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, OpenCL, Package

October 4, 2020 by hgpu

Designing Efficient Barriers and Semaphores for Graphics Processing Units

Rohan Mahapatra

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, Thesis

September 20, 2020 by hgpu

GPA: A GPU Performance Advisor Based on Instruction Sampling

Keren Zhou, Xiaozhu Meng, Ryuichi Sai, John Mellor-Crummey

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Tags: Benchmarking, Computer science, nVidia, Performance, Tesla V100

September 13, 2020 by hgpu

Performance portability through machine learning guided kernel selection in SYCL libraries

John Lawson

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Tags: AMD R9 Nano, ATI, Auto-Tuning, Benchmarking, Clustering, Computer science, Machine learning, Package, Performance, performance portability, SYCL

September 6, 2020 by hgpu

waLBerla: (widely applicable Lattice Boltzmann from Erlangen), massively parallel framework for multi physics applications

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Systolic CNN

Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing

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2021

April

27-29

Munich, Germany

9th International Workshop on OpenCL and SYCL, 2021

2020

December

10-14

Virtually in Barcelona, Spain

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

2020

November

24-27

Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2021

January

13-15

Shenzhen, China

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

2020

December

14-16

Bangkok, Thailand

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020

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high performance computing on graphics processing units: hgpu.org

Fast convolutional neural networks on FPGAs with hls4ml

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

Toward Accurate Platform-Aware Performance Modeling for Deep Neural Networks

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Runtime Performances Benchmark for Knowledge Graph Embedding Methods

When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization

LoopBench: An Evaluation of Loop Acceleration in Heterogeneous Systems

Designing Efficient Barriers and Semaphores for Graphics Processing Units

GPA: A GPU Performance Advisor Based on Instruction Sampling

Performance portability through machine learning guided kernel selection in SYCL libraries

Recent source codes

hls4ml: A package for machine learning inference in FPGAs

Adaptive Partial STEM

Linus: easy-to-share, interactive 3D figures

Compound Word Transformer

SAPFOR

CUDA Memtrace

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

waLBerla: (widely applicable Lattice Boltzmann from Erlangen), massively parallel framework for multi physics applications

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Systolic CNN

Most viewed papers (last 30 days)

Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020