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

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

ScaleHLS Project

ScaleHLS: Scalable High-Level Synthesis through MLIR

See all packages

* * *

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

HPCToolkit

NVPTX-SPIR-V Translator

Taichi Lattice Boltmann

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ScaleHLS Project

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