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Atmospheric turbulence removal using convolutional neural network

Jing Gao, N. Anantrasirichai, David Bull

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Tags: Computer vision, Deep learning, Image processing, Neural networks, nVidia, Tesla P100

December 29, 2019 by hgpu

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Jocelyn Sunseri, David Ryan Koes

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Tags: Chemistry, Computer science, CUDA, Deep learning, Machine learning, Molecular modeling, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Package, PyTorch, TensorFlow

December 15, 2019 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Quadro GP100, Package, Python, PyTorch

December 8, 2019 by hgpu

GGNN: Graph-based GPU Nearest Neighbor Search

Fabian Groh, Lukas Ruppert, Patrick Wieschollek, Hendrik P.A. Lensch

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Tags: Compter science, Computer vision, Deep learning, Nearest neighbour, nVidia, nVidia GeForce GTX 1080 Ti, nVidia Titan RTX

December 8, 2019 by hgpu

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

Guoping Long, Jun Yang, Wei Lin

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

December 1, 2019 by hgpu

Benchmarking Deep Learning Models on Jetson TX2

Lucas Pedro Bordignon, Aldo von Wangenheim

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Image recognition, nVidia, nVidia Jetson TX2, Overview, Package, Tutorial

November 24, 2019 by hgpu

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

Ke He, Bo Liu, Yu Zhang, Andrew Ling, Dian Gu

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Tags: Caffe, Computer science, Deep learning, FPGA, Neural networks, OpenCL

November 24, 2019 by hgpu

Hacking Neural Networks: A Short Introduction

Michael Kissner

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Security

November 24, 2019 by hgpu

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Aliakbar Panahi, Seyran Saeedi, Tom Arodz

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Tesla V100

November 17, 2019 by hgpu

Deep Learning Based FPGA-CPU Acceleration

R. Luis Jalabert

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Tags: Code generation, Computer science, Deep learning, FPGA, Machine learning, Neural networks, Package, Thesis

November 17, 2019 by hgpu

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

José Á. Morell, Andrés Camero, Enrique Alba

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Tags: Computer science, Deep learning, Distributed computing, Package, TensorFlow

November 3, 2019 by hgpu

Research on OpenCL optimization for FPGA deep learning application

Shuo Zhang, Yanxia Wu, Chaoguang Men, Hongtao He, Kai Liang

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Tags: Computer science, Deep learning, FPGA, OpenCL, Package

November 3, 2019 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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