high performance computing on graphics processing units: hgpu.org

Jin-Hua Tao, Zi-Dong Du, Qi Guo, Hui-Ying Lan, Lei Zhang, Sheng-Yuan Zhou, Cong Liu, Hai-Feng Liu, Shan Tang, Allen Rush, Willian Chen, Shao-Li Liu, Yun-Ji Chen, Tian-Shi Chen

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Tags: ASIC, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 950, nVidia Tegra TX1, Tesla K40

October 24, 2017 by hgpu

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Hannes Mohr

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Tags: Algorithms, ASIC, CUDA, FPGA, HEP, nVidia, Physics, Tesla K40, Tesla K80, Thesis

April 20, 2017 by hgpu

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr

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Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Performance

February 14, 2017 by hgpu

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

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Tags: ASIC, CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, Tesla C2075

November 22, 2016 by hgpu

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Prateek Tandon

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Tags: ASIC, big data, Computer science, FPGA, Hadoop, hardware acceleration, Machine learning, MapReduce, NLP, Thesis

February 2, 2016 by hgpu

Origami: A Convolutional Network Accelerator

Lukas Cavigelli, David Gschwend, Christoph Mayer, Samuel Willi, Beat Muheim, Luca Benini

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Tags: Algorithms, Artificial intelligence, ASIC, Computer science, Computer vision, Machine learning, Neural networks

December 15, 2015 by hgpu

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh

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Tags: ARM, ASIC, Computer science, FPGA, Machine learning, nVidia, nVidia GeForce GTX 650 Ti, nVidia Jetson TK1, Performance, Tesla K40

September 29, 2015 by hgpu

DLIO Benchmark

DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

cuSZ: CUDA-Based Error-Bounded Lossy Compressor for Scientific Data

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

TornadoVM

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Floyd-Warshall function of this implementation for Intel Xeon Phi Knights Landing

Comparison of HPC Architectures for Computing All-Pairs Shortest Paths. Intel Xeon Phi KNL vs NVIDIA Pascal

NPBench: A Benchmarking Suite for High-Performance NumPy

* * *

2021

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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

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24-27

Naha, Okinawa, Japan

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

2021

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13-15

Shenzhen, China

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

2020

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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

Accelerating Deep Neural Networks implementation: A survey

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Survey and Benchmarking of Machine Learning Accelerators

The Accelerator Wall: Limits of Chip Specialization

BENCHIP: Benchmarking Intelligence Processors

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Origami: A Convolutional Network Accelerator

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Recent source codes

DLIO Benchmark

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

cuSZ: CUDA-Based Error-Bounded Lossy Compressor for Scientific Data

TornadoVM

Floyd-Warshall function of this implementation for Intel Xeon Phi Knights Landing

NPBench: A Benchmarking Suite for High-Performance NumPy

PeriPy - A High Performance OpenCL Peridynamics Package

PoCL: Portable Computing Language

Voxar MPS: Fluid simulation framework based on the MPS method

Easy and Efficient Transformer: Scalable Inference Solution For large NLP mode

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