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Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

Christian Pinto

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Tags: Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce GTX 480, Thesis, Virtualization

July 17, 2015 by hgpu

Overhauling SC atomics in C11 and OpenCL

John Wickerson

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Tags: Computer science, Heterogeneous systems, Memory model, OpenCL

July 17, 2015 by hgpu

DeepFont: Identify Your Font from An Image

Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang

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

July 17, 2015 by hgpu

Performance analysis of GPGPU and CPU On AES Encryption

Akash Kiran Neelap

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Tags: AES, Algorithms, Computer science, CUDA, nVidia, nVidia Quadro K4000, Pthreads, Security, Thesis

July 15, 2015 by hgpu

Concurrent Manipulation of Dynamic Data Structures in OpenCL

Henk Mulder

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Tags: Algorithms, Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GT 310 M, OpenCL, Package

July 15, 2015 by hgpu

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Kazuhiko Ohno, Tomoharu Kamiya, Takanori Maruyama, Masaki Matsumoto

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Tags: Benchmarking, Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 980

July 15, 2015 by hgpu

Recovering Historical Climate Records using Artificial Neural Networks in GPU

Juan Pablo Balarini, Sergio Nesmachnow

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Tags: CUDA, Earth and Space Sciences, Image processing, Neural networks, nVidia, nVidia GeForce GTX 480

July 15, 2015 by hgpu

GPU-accelerated discontinuous Galerkin methods on hybrid meshes

Jesse Chan, Zheng Wang, Axel Modave, Jean-Francois Remacle, T. Warburton

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Tags: CUDA, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX 980, OpenCL, OpenMP, Spectral elements, Wave equation

July 13, 2015 by hgpu

Optimization, Specification and Verification of the Prefix Sum Program in an OpenCL Environment

Thijs Wiefferink

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 770, OpenCL, Package

July 13, 2015 by hgpu

PLB-HeC: A Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters

Luis Sant'Ana, Raphael Camargo, Daniel Cordeiro

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Tesla K20

July 13, 2015 by hgpu

A Study of Data Partitioning on OpenCL-based FPGAs

Zeke Wang, Bingsheng He, Wei Zhang

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Tags: Computer science, Databases, FPGA, OpenCL

July 13, 2015 by hgpu

Contributions to Music Semantic Analysis and Its Acceleration Techniques

Boyang Gao

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Tags: Acoustics, Algorithms, Computer science, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 580, Thesis

July 10, 2015 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

Programming

Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

Overhauling SC atomics in C11 and OpenCL

DeepFont: Identify Your Font from An Image

Performance analysis of GPGPU and CPU On AES Encryption

Concurrent Manipulation of Dynamic Data Structures in OpenCL

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Recovering Historical Climate Records using Artificial Neural Networks in GPU

GPU-accelerated discontinuous Galerkin methods on hybrid meshes

Optimization, Specification and Verification of the Prefix Sum Program in an OpenCL Environment

PLB-HeC: A Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters

A Study of Data Partitioning on OpenCL-based FPGAs

Contributions to Music Semantic Analysis and Its Acceleration Techniques

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Tutoring LLM into a Better CUDA Optimizer

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

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