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On Demand Solid Texture Synthesis Using Deep 3D Networks

Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut

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Tags: 3D Graphics and Realism, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080 Ti, OpenGL, Package, PyTorch

January 19, 2020 by hgpu

GPU Tensor Cores for fast Arithmetic Reductions

Cristóbal A. Navarro, Roberto Carrasco, Ricardo J. Barrientos, Javier A. Riquelme, Raimundo Vega

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, Tesla V100, TPU

January 19, 2020 by hgpu

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

Yap June Wai, Zulkalnain bin Mohd Yussof, Sani Irwan bin Md Salim

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Tags: Computer science, Computer vision, Deep learning, DSP, FPGA, Neural networks, OpenCL

January 19, 2020 by hgpu

Towards High Performance Java-based Deep Learning Frameworks

Athanasios Stratikopoulos, Juan Fumero, Zoran Sevarac, Christos Kotselidis

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Tags: Computer science, Computer vision, CUDA, Data mining, Deep learning, Heterogeneous systems, Java, Machine learning, nVidia, OpenCL, Package

January 19, 2020 by hgpu

Performance-Oriented Neural Architecture Search

Andrew Anderson, Jing Su, Rozenn Dahyot, David Gregg

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, SoC

January 12, 2020 by hgpu

An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs

Chaoyang Zhu, Kejie Huang, Shuyuan Yang, Ziqi Zhu, Hejia Zhang, Haibin Shen

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Neural networks

January 12, 2020 by hgpu

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

Lifu Zhang, Tarek S. Abdelrahman

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1060, PyTorch

January 5, 2020 by hgpu

Towards Unified INT8 Training for Convolutional Neural Network

Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, Junjie Yan

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

January 5, 2020 by hgpu

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

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

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