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Computational Biology and Applied Bioinformatics

Heitor Silverio Lopes, Leonardo Magalhaes Cruz

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Tags: Bioinformatics, Biology, Book, Computational biology, CUDA, nVidia, Review, Sequence alignment, Spectrometry

October 23, 2011 by hgpu

STT-RAM for Shared Memory in GPUs

Prateeksha Satyamoorthy

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 285, OpenCL, Thesis

October 23, 2011 by hgpu

Dataflow-based Design and Implementation of Image Processing Applications

Chung-Ching Shen, William Plishker, Shuvra S. Bhattacharyya

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Tags: CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 285, Signal processing

October 23, 2011 by hgpu

Accelerating Component-Based Dataflow Middleware with Adaptivity and Heterogeneity

Timothy D. R. Hartley

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro FX 5600, Optimization, Performance, Thesis

October 22, 2011 by hgpu

Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

Amirhassan Asgari Kamiabad

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Power Systems Simulation, Thesis

October 22, 2011 by hgpu

GPU performance prediction using parametrized models

Andreas Resios

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, Optimization, Performance, Thesis

October 22, 2011 by hgpu

CUDA Application Design and Development

Rob Farber

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Tags: Book, Computer science, CUDA, nVidia, Review, Software Engineering

October 22, 2011 by hgpu

Accelerating molecular docking and binding site mapping using FPGAs and GPUs

Bharat Sukhwani

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Tags: Algorithms, Biology, Biomechanics, CUDA, FPGA, Molecular modeling, nVidia, Tesla C1060, Thesis

October 22, 2011 by hgpu

Hardware Transactional Memory for GPU Architectures

Wilson W. L. Fung, Inderpreet Singh, Andrew Brownsword, Tor M. Aamodt

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Tags: Bloom filter, Computer science, CUDA, Memory, nVidia, nVidia Quadro FX 5800, OpenCL, Performance, Programming techniques

October 22, 2011 by hgpu

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Przemyslaw Stpiczynski

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Tags: Algorithms, BLAS, Computer science, CUDA, Hybrid computing, Linear Algebra, nVidia, Tesla C2050

October 21, 2011 by hgpu

Analysis and Implementation of eSTREAM and SHA-3 Cryptographic Algorithms

Deian Stefan

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Optimization, Security, Stream Ciphers, Thesis

October 21, 2011 by hgpu

On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

Jeyarajan Thiyagalingam, Daniel Goodman, Julia A. Schnabel, Anne Trefethen, Vicente Grau

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Tags: Algorithms, CUDA, Databases, Image processing, Medicine, nVidia, nVidia GeForce GTX 480, Tesla C2070

October 21, 2011 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Computational Biology and Applied Bioinformatics

STT-RAM for Shared Memory in GPUs

Dataflow-based Design and Implementation of Image Processing Applications

Accelerating Component-Based Dataflow Middleware with Adaptivity and Heterogeneity

Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

GPU performance prediction using parametrized models

CUDA Application Design and Development

Accelerating molecular docking and binding site mapping using FPGAs and GPUs

Hardware Transactional Memory for GPU Architectures

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Analysis and Implementation of eSTREAM and SHA-3 Cryptographic Algorithms

On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

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Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

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