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Computer architecture / GPGPU / Graphics hardware / Microprocessors / Parallel Thread Execution / CUDA / Thread / OpenCL / Central processing unit / Computing / Computer hardware / Parallel computing


Modeling GPU-CPU Workloads and Systems Andrew Kerr, Gregory Diamos, and Sudhakar Yalamanchili School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA
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Document Date: 2011-06-30 03:27:08

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File Size: 618,11 KB

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City

Pittsburgh / /

Company

GPU / CPU / AMD / NVIDIA / Intel / CUDA / /

Country

United States / /

Currency

USD / /

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Facility

Computer Engineering Georgia Institute of Technology Atlanta / /

IndustryTerm

compiler infrastructure / given processor / software runtime / heterogeneous processors / data-parallel applications / compilation infrastructure / selected applications / hotspot applications / Heterogeneous systems / multi-core processor / benchmark applications / image processing / multi-core superscalar processors / structured algorithms / individual applications / memory coalescing protocol / power processor / groups applications / dynamic compiler infrastructure / even new processors / /

Organization

Electrical and Computer Engineering Georgia Institute of Technology Atlanta / Sudhakar Yalamanchili School of Electrical / US Federal Reserve / /

Person

Like Ocelot / Andrew Kerr / Gregory Diamos / /

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Position

driver / CUDA driver / General / predictive analytical performance model for GPUs / high-level source-tosource translator / representative / scheduler / CUDA driver for execution on attached GPUs / Controller / /

Product

Yamaha RH1 Headphone/Headset / 280GTX GPU / /

ProgrammingLanguage

C / /

PublishedMedium

the PCI Express / /

RadioStation

Core / /

Technology

Virtual Machine / given processor / memory coalescing protocol / API / same processor / shared memory / image processing / MRI / power processor / Magnetic Resonance Imaging / Well structured algorithms / executing processor / simulation / /

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