Reference Histogram Image Convolution Bilateral Filtering
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| Titel: | Reference Histogram Image Convolution Bilateral Filtering |
|---|---|
| Autoren: | Performance Analysis Of Histogram, Image Convolution |
| Weitere Verfasser: | The Pennsylvania State University CiteSeerX Archives |
| Quelle: | http://datasys.cs.iit.edu/reports/2014_GCASR14_poster-apps-gpu.pdf. |
| Bestand: | CiteSeerX |
| Schlagwörter: | Aarhus • Performance comparison of application kernels. • Image Convolution, Histogram and Bilateral filtering • Multi-core CPU, many-core NVIDIA GPUs and GeMTC (GPU enabled Many Task Computing |
| Beschreibung: | filtering. • These kernels have a large amount of data-level parallelism. • All these applications are executed in CPU, GPU and GeMTC. • GeMTC is an execution model and runtime system which enables NVIDIA GPUs to be programmed with many concurrent and independent tasks of potentially short or variable duration. • The target test bed for this implementation is GTX 670 GPU with AMD Phenom(tm) II X6 1100T Processor with 6GB RAM. • For GPU, the test are conducted with varying threads and varying problem size. • Throughput and FLOPS are taken as performance analysis factor. • Through this we better understand the behavior of different applications that belong to the Many-Task Computing paradigm. Conclusion & Future Work |
| Publikationsart: | text |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.644.2920 |
| Verfügbarkeit: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.644.2920 http://datasys.cs.iit.edu/reports/2014_GCASR14_poster-apps-gpu.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Dokumentencode: | edsbas.911B16E2 |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | filtering. • These kernels have a large amount of data-level parallelism. • All these applications are executed in CPU, GPU and GeMTC. • GeMTC is an execution model and runtime system which enables NVIDIA GPUs to be programmed with many concurrent and independent tasks of potentially short or variable duration. • The target test bed for this implementation is GTX 670 GPU with AMD Phenom(tm) II X6 1100T Processor with 6GB RAM. • For GPU, the test are conducted with varying threads and varying problem size. • Throughput and FLOPS are taken as performance analysis factor. • Through this we better understand the behavior of different applications that belong to the Many-Task Computing paradigm. Conclusion & Future Work |
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