high performance computing on graphics processing units: hgpu.org

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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

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

Odyssey: A Public GPU-Based Code for General-Relativistic Radiative Transfer in Kerr Spacetime

Hung-Yi Pu, Kiyun Yun, Ziri Younsi, Suk-Jin Yoon

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Tags: Astrophysics, CUDA, High Energy Astrophysical Phenomena, MPI, nVidia, nVidia GeForce GTX 780 Ti, Package, Raytracing

January 14, 2016 by hgpu

Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

Alessio Sclocco, Joeri van Leeuwen, Henri E. Bal, Rob V. van Nieuwpoort

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Tags: Astrophysics, ATI, ATI Radeon HD 7970, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce Titan, OpenCL, OpenMP, Package, Tesla K20

January 12, 2016 by hgpu

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

Phillipe A. Pereira, Higo F. Albuquerque, Hendrio M. Marques, Isabela S. Silva, Celso B. Carvalho, Lucas C. Cordeiro

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Tags: Computer science, CUDA, nVidia, Package

January 7, 2016 by hgpu

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tvei, Torbjorn Morland, Thomas Brox Rost

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Tags: Computer science, Deep learning, Machine learning, Neural networks, OpenCL, Package, Vulkan

January 7, 2016 by hgpu

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing

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Tags: Caffe, Computer science, Computer vision, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Package, Tesla K20

December 23, 2015 by hgpu

On the Development and Implementation of High-Order Flux Reconstruction Schemes for Computational Fluid Dynamics

Freddie David Witherden

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Tags: AMD FirePro W9100, ATI, CUDA, Finite difference, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

December 23, 2015 by hgpu

OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures

Konstantinos Krommydas, Wu-chun Feng, Christos D. Antonopoulos, Nikolaos Bellas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, OpenCL, Package

December 22, 2015 by hgpu

Autotuning Stencils Codes with Algorithmic Skeletons

Chris Cummins

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Package

December 19, 2015 by hgpu

Efficient Query Processing in Co-Processor-accelerated Databases

Sebastian Bress

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Tags: Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 640, Package, Tesla C1060, Tesla C2050, Thesis

December 19, 2015 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

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high performance computing on graphics processing units: hgpu.org

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

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

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

Odyssey: A Public GPU-Based Code for General-Relativistic Radiative Transfer in Kerr Spacetime

Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

Verifying CUDA Programs using SMT-Based Context-Bounded Model Checking

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

On the Development and Implementation of High-Order Flux Reconstruction Schemes for Computational Fluid Dynamics

OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures

Autotuning Stencils Codes with Algorithmic Skeletons

Efficient Query Processing in Co-Processor-accelerated Databases

Recent source codes

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

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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