high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Francisco Javier Ordonez, Daniel Roggen

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, Package, Python, RNN, Theano

January 26, 2016 by hgpu

Compositional Compilation for Sparse, Irregular Data Parallelism

Adam Harries, Michel Steuwer, Murray Cole, Alan Gray, Christophe Dubach

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Tags: Computer science, Data parallelism, nVidia, OpenCL, Sparse matrix, Tesla K40

January 26, 2016 by hgpu

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Kyungjoo Kim, Sivasankaran Rajamanickam, George Stelle, H. Carter Edwards, Stephen L. Olivier

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Tags: Algorithms, Computer science, Factorization, Intel Xeon Phi, Mathematical Software, Package, Sparse matrix

January 26, 2016 by hgpu

Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer

Alina Sirbu, Ozalp Babaoglu

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Package, Python, Task scheduling

January 26, 2016 by hgpu

Parallel Explicit FEM Algorithms Using GPU’s

Seyed Parsa Banihashemi

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Tags: Algorithms, AMD Radeon R9 280X, ATI, ATI Radeon HD 7970, Computer science, FEM, Finite element method, nVidia, nVidia GeForce GTX Titan, OpenCL, Particle swarm optimization, Performance, Tesla M2090, Thesis

January 22, 2016 by hgpu

Heterogeneous (CPU+GPU) Working-set Hash Tables

Ziaul Choudhury, Suresh Purini

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Tags: Computer science, CUDA, Hashing, Heterogeneous systems, nVidia, Tesla K20

January 22, 2016 by hgpu

Exploring LLVM Infrastructure for Simplified Multi-GPU Programming

Alexander Matz, Mark Hummel, Holger Froning

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Tags: Computer science, CUDA, LLVM, nVidia, OpenCL, Tesla K80

January 22, 2016 by hgpu

Basker: A Threaded Sparse LU Factorization Utilizing Hierarchical Parallelism and Data Layouts

Joshua Dennis Booth, Sivasankaran Rajamanickam, Heidi K. Thornquist

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Tags: Algorithms, Computer science, Factorization, Intel Xeon Phi, Sparse direct solvers, Sparse matrix

January 22, 2016 by hgpu

GPU Multisplit

Saman Ashkiani, Andrew Davidson, Ulrich Meyer, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 750 Ti, Tesla K40

January 22, 2016 by hgpu

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Mohammed Abdelrahim

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Tags: Computer science, CUDA, Heterogeneous systems, LSTM, Machine learning, nVidia, nVidia GeForce GTX 770, OpenMP, Thesis

January 19, 2016 by hgpu

A framework for efficient execution on GPU and CPU+GPU systems

Jean-Francois Dollinger

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Performance, Thesis

January 19, 2016 by hgpu

Homomorphic Autocomplete

Gizem S. Cetin, Wei Dai, Yarkin Doroz, Berk Sunar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 690, Security

January 16, 2016 by hgpu

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

An HPC Benchmark Survey and Taxonomy for Characterization

HDM: Home made Diffusion Models

Home-made Diffusion Model from Scratch to Hatch

General Matrix Multiplication (GEMM)

High Performance Matrix Multiplication

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high performance computing on graphics processing units: hgpu.org

Applications

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Compositional Compilation for Sparse, Irregular Data Parallelism

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer

Parallel Explicit FEM Algorithms Using GPU’s

Heterogeneous (CPU+GPU) Working-set Hash Tables

Exploring LLVM Infrastructure for Simplified Multi-GPU Programming

Basker: A Threaded Sparse LU Factorization Utilizing Hierarchical Parallelism and Data Layouts

GPU Multisplit

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

A framework for efficient execution on GPU and CPU+GPU systems

Homomorphic Autocomplete

Recent source codes

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

TRUST: a thermalhydraulic software package for CFD simulations

Modular: The Modular Platform (includes MAX & Mojo)

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

HDM: Home made Diffusion Models

General Matrix Multiplication (GEMM)

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