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Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, TensorFlow, Tesla K80

June 17, 2017 by hgpu

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

C. Escalante, T. Morales de Luna, M.J. Castro

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, Mathematics, Numerical Analysis, nVidia

June 17, 2017 by hgpu

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla P100

June 10, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian

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Tags: Computer science, CUDA, Deep learning, Java, Neural networks, nVidia, Package, RNN, TensorFlow

June 10, 2017 by hgpu

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Amos Egel, Lorenzo Pattelli, Giacomo Mazzamuto, Diederik S. Wiersma, Uli Lemmer

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Tags: Computational Physics, CUDA, Matlab, nVidia, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan X, Optics, Package, Physics

June 10, 2017 by hgpu

A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit

Mark W. Lewis

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Tags: Algorithms, Artificial intelligence, Benchmarking, Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTS 780 Ti

June 5, 2017 by hgpu

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Lingyu Duan, Wei Sun, Xinfeng Zhang, Jie Chen, Jianxiong Yin, Simon See, Tiejun Huang, Alex C. Kot, Wen Gao

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Tags: Benchmarking, CUDA, Hybrid computing, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia Tegra TX1, Tesla M40

June 1, 2017 by hgpu

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Tesla K40

June 1, 2017 by hgpu

A Unified Optimization Approach for Sparse Tensor Operations on GPUs

Bangtian Liu, Chengyao Wen, Anand D.Sarwate, Maryam Mehri Dehnavi

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, nVidia GeForce GTX Titan X, Sparse matrix

June 1, 2017 by hgpu

Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Device Placement Optimization with Reinforcement Learning

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

A Unified Optimization Approach for Sparse Tensor Operations on GPUs

Implementing Efficient, Portable Computations for Machine Learning

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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