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hgpu.org » Applications » Computer science

Tiling for Performance Tuning on Different Models of GPUs

Chang Xu, Steven R. Kirk, Samantha Jenkins

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 260, Performance

November 13, 2010 by hgpu

RapidMind: Portability across Architectures and its Limitations

Iris Christadler, Volker Weinberg

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Tags: Cell processor, Computer science, CUDA, nVidia, Performance, Programming Languages, Tesla C1060, Tesla S1070

November 12, 2010 by hgpu

Exploring the Limits of GPUs With Parallel Graph Algorithms

Frank Dehne, Kumanan Yogaratnam

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 260, Performance

November 12, 2010 by hgpu

Deterministic Sample Sort For GPUs

Frank Dehne, Hamidreza Zaboli

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Tags: Algorithms, Computational Complexity, Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 285, Sorting

November 12, 2010 by hgpu

Measuring Bandwidth for Super Computer Workloads

A. Neela Madheswari, R. S. D. Wahida Banu

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Tags: Astrophysics, Computer science, Instrumentation and Methods for Astrophysics

November 12, 2010 by hgpu

Exact Sparse Matrix-Vector Multiplication on GPU’s and Multicore Architectures

Brice Boyer, Jean-Guillaume Dumas, Pascal Giorgi

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Tags: Computer science, CUDA, Mathematical Software, nVidia, nVidia GeForce GTX 280, OpenMP, Package, Sparse matrix, Symbolic Computation

November 11, 2010 by hgpu

A Flexible Patch-Based Lattice Boltzmann Parallelization Approach for Heterogeneous GPU-CPU Clusters

Christian Feichtinger, Johannes Habich, Harald Koestler, Georg Hager, Ulrich Ruede, Gerhard Wellein

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Tags: Computer science, CUDA, Lattice Boltzmann model, MPI, nVidia, Tesla C1060, Tesla S1070

November 11, 2010 by hgpu

Fully automatic extraction of salient objects from videos in near real-time

Akamine Kazuma, Ken Fukuchi, Akisato Kimura, Shigeru Takagi

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce 9800 GT, Pattern recognition

November 10, 2010 by hgpu

A GPU-based hyperbolic SVD algorithm

Vedran Novakovic, Sanja Singer

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Tags: Computer science, CUDA, Hyperbolic system, Mathematics, MPI, Numerical Analysis, nVidia, nVidia GeForce GTX 275, nVidia GeForce GTX 280, Tesla C1060

November 10, 2010 by hgpu

Faster Radix Sort via Virtual Memory and Write-Combining

Jan Wassenberg, Peter Sanders

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Tags: Algorithms, Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 480, Sorting

November 10, 2010 by hgpu

How to obtain efficient GPU kernels: an illustration using FMM & FGT algorithms

Felipe A. Cruz, Simon K. Layton, Lorena A. Barba

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Tags: Computer science, CUDA, Fast summation methods, Mathematical Software, nVidia, Tesla C1060

November 9, 2010 by hgpu

Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

Antonio Wendell De Oliveira Rodrigues, Frederic Guyomarch, Yvonnick Le Menach, Jean-Luc Dekeyser

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Sparse matrix, Tesla C870

November 9, 2010 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

Applications

Tiling for Performance Tuning on Different Models of GPUs

RapidMind: Portability across Architectures and its Limitations

Exploring the Limits of GPUs With Parallel Graph Algorithms

Deterministic Sample Sort For GPUs

Measuring Bandwidth for Super Computer Workloads

Exact Sparse Matrix-Vector Multiplication on GPU’s and Multicore Architectures

A Flexible Patch-Based Lattice Boltzmann Parallelization Approach for Heterogeneous GPU-CPU Clusters

Fully automatic extraction of salient objects from videos in near real-time

A GPU-based hyperbolic SVD algorithm

Faster Radix Sort via Virtual Memory and Write-Combining

How to obtain efficient GPU kernels: an illustration using FMM & FGT algorithms

Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

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