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A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on GPU

Hao Peng, Rongke Liu, Yi Hou, Ling Zhao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 980, Signal processing

August 4, 2016 by hgpu

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Zhehuan Zhao, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

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Tags: Bioinformatics, Biology, CUDA, Deep learning, Neural networks, NLP, nVidia, Package, Python, Tesla K20, Text mining

August 1, 2016 by hgpu

Automatic Loop Partitioning for Heterogeneous Systems

Alexander Baier

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan Black, Performance, PTX, Thesis

August 1, 2016 by hgpu

Many-Core Algorithms for Combinatorial Optimization

Francesco Strappaveccia

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 770, nVidia GeForce GTX Titan, OpenMP, Optimization, Path problems, Thesis

July 28, 2016 by hgpu

PASSATA – Object oriented numerical simulation software for adaptive optics

G. Agapito, A. Puglisi, S. Esposito

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Numerical simulation, nVidia, Optics

July 28, 2016 by hgpu

Genetic Improvement of GPU Software

William B. Langdon, Brian Yee Hong Lam, Marc Modat, Justyna Petke, Mark Harman

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Tesla C2050, Tesla K20, Tesla T10

July 28, 2016 by hgpu

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Brian Ichter, Edward Schmerling, Ali-akbar Agha-mohammadi, Marco Pavone

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package, Robotics

July 28, 2016 by hgpu

Strategies for Protecting Intellectual Property when Using CUDA Applications on Graphics Processing Units

X. Bellekens, G. Paul, J. M. Irvine, C. Tachtatzis, R. C. Atkinson

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Tags: CUDA, nVidia

July 26, 2016 by xhgpu

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Marius Erbert, Steffen Rechner, Matthias Muller-Hannemann

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, Package

July 26, 2016 by hgpu

Lowering IrGL to CUDA

Sreepathi Pai, Keshav Pingali

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Tags: Compilers, Computer science, CUDA, nVidia, Programming Languages

July 20, 2016 by hgpu

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Joao M. Mendonca, Simon L. Grimm, Luc Grosheintz, Kevin Heng

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Tags: Astrophysics, CUDA, Earth and Planetary Astrophysics, Earth and Space Sciences, nVidia, Tesla K20

July 20, 2016 by hgpu

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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high performance computing on graphics processing units: hgpu.org

Programming

A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on GPU

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Automatic Loop Partitioning for Heterogeneous Systems

Many-Core Algorithms for Combinatorial Optimization

PASSATA – Object oriented numerical simulation software for adaptive optics

Genetic Improvement of GPU Software

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs

Strategies for Protecting Intellectual Property when Using CUDA Applications on Graphics Processing Units

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Lowering IrGL to CUDA

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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