high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Porting FEASTFLOW to the Intel Xeon Phi: Lessons Learned

Ioannis E. Venetis, Georgios Goumas, Markus Geveler, Dirk Ribbrock

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Tags: Computer science, Fluid dynamics, Intel Xeon Phi, OpenCL, OpenMP, Sparse matrix

February 14, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 by hgpu

Multi-tier Dynamic Vectorization for Translating GPU Optimizations into CPU Performance

Hee-Seok Kim, Izzat El Hajj, John A. Stratton, Wen-Mei W. Hwu

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Tags: AMD, Computer science, nVidia, OpenCL, Performance

February 12, 2014 by hgpu

Increasing precision of uniform pseudorandom number generators

Vadim Demchik, Alexey Gulov

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Tags: Algorithms, Computer science, Extended precision, Mathematical Software, Pseudo-random number generators, Security

February 12, 2014 by hgpu

Designing Bit-Reproducible Portable High-Performance Applications

Andrea Arteaga, Oliver Fuhrer, Torsten Hoefler

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Portability, Tesla K20

February 12, 2014 by hgpu

GROMACS on Hybrid CPU-GPU and CPU-MIC Clusters: Preliminary Porting Experiences, Results and Next Steps

Sadaf Alam, Ugo Varetto

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, Intel Xeon Phi, MPI, nVidia, OpenCL, Tesla K20

February 12, 2014 by hgpu

Transparent use of Java objects on the GPU in the JaMP/OpenMP framework

Carolin Wolf

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Tags: Computer science, CUDA, Java, nVidia, OpenMP, Tesla M1060, Thesis

February 12, 2014 by hgpu

Minerals detection for hyperspectral images using adapted linear unmixing: LinMin

Schmidt Frederic, Legendre Maxime, Le Mouelic Stephane

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Tags: Algorithms, Astrophysics, CUDA, Earth and Planetary Astrophysics, nVidia, Tesla C2050

February 12, 2014 by hgpu

Yang-Mills lattice on CUDA

Richard Forster, Agnes Fulop

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Tags: CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, Physics, QCD

February 12, 2014 by hgpu

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Tarun Beri, Sorav Bansal, Subodh Kumar

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Tags: Computer science, CUDA, FFT, GPU cluster, Heterogeneous systems, Matrix multiplication, Memory model, nVidia, Prefetch, Task scheduling, Tesla M2070

February 11, 2014 by hgpu

Confidentiality Issues on a GPU in a Virtualized Environment

Clementine Maurice, Christoph Neumann, Olivier Heen, Aurelien Francillon

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Tags: Cloud, Computer science, CUDA, nVidia, Security, Tesla K20, Virtualization

February 11, 2014 by hgpu

Exploiting GPU Parallelism to Optimize Real-World Problems

Hasan Furhad, Fahmida Ahmed, Faisal Faruque, Iqbal Hasan Sarker

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 580, Optimization

February 11, 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

Programming

Porting FEASTFLOW to the Intel Xeon Phi: Lessons Learned

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Multi-tier Dynamic Vectorization for Translating GPU Optimizations into CPU Performance

Increasing precision of uniform pseudorandom number generators

Designing Bit-Reproducible Portable High-Performance Applications

GROMACS on Hybrid CPU-GPU and CPU-MIC Clusters: Preliminary Porting Experiences, Results and Next Steps

Transparent use of Java objects on the GPU in the JaMP/OpenMP framework

Minerals detection for hyperspectral images using adapted linear unmixing: LinMin

Yang-Mills lattice on CUDA

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Exploiting GPU Parallelism to Optimize Real-World Problems

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