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A Novel Mapping of Arbitrary Precision Integer Operations to the GPU

Matthias Gusenbauer

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Tags: Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Thesis

March 8, 2016 by hgpu

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Francisco Javier Ramírez-Gil, Emilío Carlos Nelli Silva, Wilfredo Montealegre-Rubio

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Tags: CUDA

March 7, 2016 by fjramireg

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Sriram Kankatala

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Tags: Computer science, CUDA, Data mining, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 550 Ti, Pattern recognition, Pthreads, Thesis

March 5, 2016 by hgpu

Input Space Splitting for OpenCL

Simon Moll, Johannes Doerfert, Sebastian Hack

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Tags: Compilers, Computer science, Intel, LLVM, OpenCL

March 5, 2016 by hgpu

Fast LZW compression using a GPU

Shunji Funasaka, Koji Nakano, Yasuaki Ito

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, nVidia GeForce GTX 980

March 5, 2016 by hgpu

Heterogeneous parallel algorithms for Computational Fluid Dynamics on unstructured meshes

Guillermo Andres Oyarzun Altamirano

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenCL, SoC, Tesla M2090, Thesis

March 5, 2016 by hgpu

Metamorphic Testing for (Graphics) Compilers

Alastair F. Donaldson, Andrei Lascu

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Tags: Compilers, Computer science, GLSL, nVidia, nVidia GeForce GTX 980 M, OpenCL, OpenGL

March 5, 2016 by hgpu

Hierarchical Semantic Parsing for Object Pose Estimation in Densely Cluttered Scenes

Chi Li, Jonathan Bohren, Eric Carlson, Gregory D. Hager

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Source codes

Tags: Computer science, Computer vision, CUDA, nVidia, OpenCV, Package, SIFT

March 3, 2016 by hgpu

Hadoop Mapreduce OpenCL Plugin

Vivek Viswanathan

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Tags: AMD Radeon R9 290X, ATI, Computer science, Heterogeneous systems, Java, MapReduce, OpenCL

March 3, 2016 by hgpu

Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

Qadha'a AlEnezi, Hosam AboElFotoh, Bader AlBdaiwi, Mohammad Ali AlMulla

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 560 M, P systems

March 3, 2016 by hgpu

Full reconstruction of a 14-qubit state within four hours

Zhibo Hou, Han-Sen Zhong, Ye Tian, Daoyi Dong, Bo Qi, Li Li, Yuanlong Wang, Franco Nori, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo

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Tags: CUDA, nVidia, nVidia GeForce GTX 780, Physics, Quantum computing, Quantum Physics

March 3, 2016 by hgpu

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Hanjoo Kim, Jaehong Park, Jaehee Jang, Sungroh Yoon

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Tags: Caffe, Computer science, CUDA, Deep learning, Distributed computing, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Package

March 1, 2016 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

A Novel Mapping of Arbitrary Precision Integer Operations to the GPU

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Input Space Splitting for OpenCL

Fast LZW compression using a GPU

Heterogeneous parallel algorithms for Computational Fluid Dynamics on unstructured meshes

Metamorphic Testing for (Graphics) Compilers

Hierarchical Semantic Parsing for Object Pose Estimation in Densely Cluttered Scenes

Hadoop Mapreduce OpenCL Plugin

Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

Full reconstruction of a 14-qubit state within four hours

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

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Tutoring LLM into a Better CUDA Optimizer

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

Kernel Library for LLM Serving

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

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