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I/O Lower Bounds for Auto-tuning of Convolutions in CNNs

Xiaoyang Zhang, Junmin Xiao, Guangming Tan

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Tags: Auto-Tuning, CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

January 3, 2021 by hgpu

Solving Mixed Integer Programs Using Neural Networks

Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols

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Tags: Computer science, Deep learning, Machine learning, Mathematics, Neural networks, nVidia

December 27, 2020 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

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Package, Programming Languages, Python, Video

December 13, 2020 by hgpu

EasyPBR: A Lightweight Physically-Based Renderer

Radu Alexandru Rosu, Sven Behnke

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Tags: 3D Graphics and Realism, Deep learning, nVidia, nVidia GeForce RTX 2060, OpenGL, Package, Python, Rendering, Visualization

December 13, 2020 by hgpu

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun

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

December 13, 2020 by hgpu

Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

Chao Jiang, David Ojika, Sofia Vallecorsa, Thorsten Kurth, Prabhat, Bhavesh Patel, Herman Lam

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks

December 6, 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

Python Workflows on HPC Systems

Dominik Strassel, Philipp Reusch, Janis Keuper

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Tags: Computer science, CUDA, Deep learning, GPU cluster, HPC, Machine learning, nVidia, PC cluster, Python

December 6, 2020 by hgpu

Efficient Deep Neural Network Inference for Embedded Systems: A Mixture of Experts Approach

Benjamin David Taylor

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Tags: Algorithms, Cloud, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TX2, Thesis

November 29, 2020 by hgpu

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

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

November 22, 2020 by hgpu

TopicBERT for Energy Efficient Document Classification

Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze

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Tags: classification, Computer science, CUDA, Deep learning, NLP, nVidia, Tesla T4

November 8, 2020 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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