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Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Chao Wu, Yaoxue Zhang, Yuezhi Zhou, Qiushi Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance

November 18, 2018 by hgpu

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

Guoping Long, Jun Yang, Kai Zhu, Wei Lin

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, nVidia, TensorFlow

November 18, 2018 by hgpu

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

Gabor Racz, Istvan Szapudi, Laszlo Dobos, Istvan Csabai, Alexander S. Szalay

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Tags: Astrophysics, Cosmology, CUDA, MPI, N-body simulation, nVidia, OpenMP, Package, Tesla K80

November 18, 2018 by hgpu

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

Denis Demidov

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Tags: Computer science, CUDA, Differential equations, Mathematical Software, multigrid, nVidia, OpenCL, OpenMP, Package, Partial differential equations, PDEs, Tesla K80

November 18, 2018 by hgpu

The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs

Ahmad Al Badawi, Jin Chao, Jie Lin, Chan Fook Mun, Sim Jun Jie, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, Vijay Ramaseshan Chandrasekhar

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Tags: CNN, Computer science, CUDA, Deep learning, nVidia, Security, Tesla V100

November 11, 2018 by hgpu

Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation

Ammar Ahmad Awan, Jeroen Bedorf, Ching-Hsiang Chu, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Computer science, CUDA, Deep learning, Distributed computing, MPI, nVidia, TensorFlow, Tesla K80

November 3, 2018 by hgpu

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

Patrick Diehl, Madhavan Seshadri, Thomas Heller, Hartmut Kaiser

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Tags: Computer science, CUDA, nVidia, Package, Programming Languages, Tesla K40, Tesla K80

November 3, 2018 by hgpu

A Comparative Measurement Study of Deep Learning as a Service Framework

Yanzhao Wu, Ling Liu, Calton Pu, Wenqi Cao, Semih Sahin, Wenqi Wei, Qi Zhang

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Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Tesla M60, Theano

November 3, 2018 by hgpu

The Ocean Tensor Package

Ewout van den Berg

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

October 28, 2018 by hgpu

Towards Efficient Large-Scale Graph Neural Network Computing

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai

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Tags: Computer science, CUDA, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, Tesla P100

October 28, 2018 by hgpu

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

David W. Brown

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 750 Ti, Package

October 6, 2018 by hgpu

Exascale Deep Learning for Climate Analytics

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston

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Tags: CUDA, Deep learning, Earth and Space Sciences, Neural networks, nVidia, TensorFlow, Tesla V100, Weather prediction

October 6, 2018 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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