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DBMS Index for Hierarchical Data Using Nested Intervals and Residue Classes

Vladimir Volonkin

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Tags: Computer science, CUDA, Databases, nVidia

April 18, 2014 by hgpu

Comparative Study of Frequent Itemset Mining Techniques on Graphics Processor

Dharmesh Bhalodiya, Chhaya Patel

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Tags: Algorithms, Computer science, CUDA, Data mining, Databases, nVidia

April 18, 2014 by hgpu

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Sparsh Mittal, Jeffrey S. Vetter

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Tags: comparison, Computer science, cpu, CUDA, Energy-efficient computing, FPGA, Hardware Architecture, nVidia

April 18, 2014 by sparsh0mittal

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

George Papandreou, Liang-Chieh Chen, Alan L. Yuille

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 650 M, SIFT, Tesla K20

April 17, 2014 by hgpu

Importance Sampling of Realistic Light Sources

Heqi Lu

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Tags: 3D Graphics and Realism, Computer science, CUDA, DirectX, nVidia, nVidia GeForce GTX 680, Rendering, Thesis

April 17, 2014 by hgpu

Feasibility Analysis of Bilateral Filtering by General Purpose Graphical Processing Unit Computing

Shruti S.Agrawal, C.K.Kurve

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Tags: Algorithms, CUDA, Filtering, Image processing, nVidia, Signal processing, Tesla K20

April 17, 2014 by hgpu

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

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Tags: Bayesian, Biology, CUDA, Machine learning, Neural networks, nVidia, Package, Python

April 17, 2014 by hgpu

Performance-aware component composition for GPU-based systems

Usman Dastgeer

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Programming techniques, Tesla C1060, Tesla C2050, Thesis

April 16, 2014 by hgpu

On optimization techniques for the matrix multiplication on hybrid CPU+GPU platforms

Luis-Pedro Garcia, Javier Cuenca, Domingo Gimenez

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 590, Tesla K20

April 16, 2014 by hgpu

Dynamic Instrumentation and Optimization for GPU Applications

Naila Farooqui, Christopher J. Rossbach, Yuan Yu

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla M2075

April 16, 2014 by hgpu

New Efficient Method To Solve Longest Overlap Region Problem For Noncoding DNA Sequence

Yukun Zhong, ZhiWei He, XianHong Wang, Liao Gang

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GT 610 M

April 16, 2014 by hgpu

A Way For Accelerating The DNA Sequence Reconstruction Problem By CUDA

Yukun Zhong, ZhiWei He, XianHong Wang, XiongBin Cao

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GT 610 M, Sorting

April 16, 2014 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

DBMS Index for Hierarchical Data Using Nested Intervals and Residue Classes

Comparative Study of Frequent Itemset Mining Techniques on Graphics Processor

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

Importance Sampling of Realistic Light Sources

Feasibility Analysis of Bilateral Filtering by General Purpose Graphical Processing Unit Computing

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Performance-aware component composition for GPU-based systems

On optimization techniques for the matrix multiplication on hybrid CPU+GPU platforms

Dynamic Instrumentation and Optimization for GPU Applications

New Efficient Method To Solve Longest Overlap Region Problem For Noncoding DNA Sequence

A Way For Accelerating The DNA Sequence Reconstruction Problem By CUDA

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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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