high performance computing on graphics processing units: hgpu.org

Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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

September 29, 2019 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Benchmarking Deep Learning Models on Jetson TX2

FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Hacking Neural Networks: A Short Introduction

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Deep Learning Based FPGA-CPU Acceleration

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Research on OpenCL optimization for FPGA deep learning application

AI Benchmark: All About Deep Learning on Smartphones in 2019

Characterizing Deep Learning Training Workloads on Alibaba-PAI

MIOpen: An Open Source Library For Deep Learning Primitives

Exascale Deep Learning for Scientific Inverse Problems

Recent source codes

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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CFAL-bench

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