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Heterogeneous big data cluster for computer vision applications / Hazem Abdelmegeed Elsayed Abdelhafez ; Supervised Hossam Ali Hassan Fahmy , Ameen Mohamed Nassar , Mohamed Mohamed Rehan

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Hazem Abdelmegeed Elsayed Abdelhafez , 2016Description: 78 P. : plans ; 30cmOther title:
  • مجموعة مشغلات غير متجانسة لحسابات البيانات الكبيرة فى تطبيقات رؤية الحاسب [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications Summary: A token based scheduler is developed to enable efficient utilization of graphics processing unit in big-data clusters specifically for computer vision applications. The scheduler addresses the racing conditions that occur on the graphics processing unit due to simultaneous access by the parallel instances of the running application. The presented scheduler enables the porting of computer vision applications to big-data cluster with heterogeneous computing capabilities where multi-core central processing units exist alongside graphical processing unit
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Item type Current library Home library Call number Copy number Status Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2016.Ha.H (Browse shelf(Opens below)) Not for loan 01010110071126000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2016.Ha.H (Browse shelf(Opens below)) 71126.CD Not for loan 01020110071126000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications

A token based scheduler is developed to enable efficient utilization of graphics processing unit in big-data clusters specifically for computer vision applications. The scheduler addresses the racing conditions that occur on the graphics processing unit due to simultaneous access by the parallel instances of the running application. The presented scheduler enables the porting of computer vision applications to big-data cluster with heterogeneous computing capabilities where multi-core central processing units exist alongside graphical processing unit

Issued also as CD

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