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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

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

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Vivek K. Pallipuram

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Performance, Tesla K20, Tesla M2075, Thesis

February 11, 2014 by hgpu

Benchmarking the Intel Xeon Phi Coprocessor

F. Masci

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Performance

February 11, 2014 by hgpu

Point Rendering in CUDA Path Tracer

Hsuan-Yueh Peng

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 780, Rendering

February 11, 2014 by hgpu

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla C2050, Tesla K20, Tesla M2090

February 11, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

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Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Genetically Improved CUDA C++ Software

W. B. Langdon, M. Harman

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Source codes

Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, nVidia Quadro NVS 290, Package, Tesla C2050, Tesla K20, Tesla T10

February 11, 2014 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

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

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

Exploiting GPU Parallelism to Optimize Real-World Problems

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Benchmarking the Intel Xeon Phi Coprocessor

Point Rendering in CUDA Path Tracer

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Implementing the Projected Spatial Rich Features on a GPU

Genetically Improved CUDA C++ Software

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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