METHODS FOR INCREASING SOFTWARE PORTABILITY WITH INCREASED PARALLELIZATION AND PERFORMANCE IN DISTRIBUTED AND MULTICORE SYSTEMS RANADIVE PRITI English
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TextPublication details: Pune SI(DU) 2016Description: 119Subject(s): Summary: solved one of the major practical problems in porting a sequential code on to a multicore platform. Since the exercise of porting to multicore is an expensive and time consuming, it would certainly help to know the speedup one would get. If one has a priori knowledge of possible speedup, one can make an informed decision. The model often used for this is the Amdahl’s law based. This method only gives the maximum possible theoretical speedup, and it is often far from what one gets in real life. Thus, the problem is how does one get a better model? In this thesis, we will describe the methodology used for developing a mathematical model, experimentation using benchmark codes and its use for validation of the model, and computation of percentage error in prediction. Our model is called ‘Performance Prediction Model Integrated with Overheads’ or PPMIO. Before we get into details, let us look at the need for solving the problem
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Thesis (Phd)
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Symbiosis International University Central Library | Reference | 006.22 RAN siu-th-167 (Browse shelf(Opens below)) | Not For Loan (Restricted Access) | It is available for consultation in the SI(DU) library. | siu-th-167 |
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| 005.7565 AHI siu-th-123 Scalable transactions in cloud data stores | 005.7565 PHA siu-th-121 Development of consistency rationing model in clod data stores | 005.8/DOO 43515 DNS security management / | 006.22 RAN siu-th-167 METHODS FOR INCREASING SOFTWARE PORTABILITY WITH INCREASED PARALLELIZATION AND PERFORMANCE IN DISTRIBUTED AND MULTICORE SYSTEMS | 006.3 DAL siu-th-163 OPTIMIZATION OF HOLE-MAKING OPERATIONS USING ADVANCED OPTIMIZATION TECHNIQUES | 006.3/KRU 43208 A methodological introduction. | 006.3 KUL Probability Collectives |
solved one of the major practical problems in porting a sequential code on to a multicore platform. Since the exercise of porting to multicore is an expensive and time consuming, it would certainly help to know the speedup one would get. If one has a priori knowledge of possible speedup, one can make an informed decision. The model often used for this is the Amdahl’s law based. This method only gives the maximum possible theoretical speedup, and it is often far from what one gets in real life. Thus, the problem is how does one get a better model? In this thesis, we will describe the methodology used for developing a mathematical model, experimentation using benchmark codes and its use for validation of the model, and computation of percentage error in prediction. Our model is called ‘Performance Prediction Model Integrated with Overheads’ or PPMIO. Before we get into details, let us look at the need for solving the problem
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