فایل ورد کامل محاسبه خوشه برمبنای MPI برای ارزیابی عملکرد برنامه های موازی

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تعداد صفحات این فایل: ۱۸ صفحه

در اینجا مدلی را مطرح کردیم که در مورد سودو زیان عملکرد حاصله از پردازش موازی توضیح می دهد. همچنین مدلی را مطرح کردیم که در مورد ارزیابی وابستگی عملکرد برنامه های کاربردی بر مبنای MPI موازی و ورژن سریالی و متوالی اش روی RAM توضیح داده و نشان می دهد محاسبه متوالی شامل RAM ناچیزی بوده و محاسبه موازی از RAMبیشتری به ویژه برای ورودی های بزرگتر استفاده می کند. اجرای متوالی برای ورودی کوچکتر، سریعتر می باشد که دلیل این امر سربارهای ارتباطی و اتصال در اجرای موازی می باشد. عملکرد اجرای موازی بسیار بیشتر از اجرای متوالی است زمانی که اندازه ورودی بسیار بزرگ می باشد. زمان کل صرف شده برای محاسبه نتیجه با افزایش تعداد گره ها، به طور چشمگیری کاهش می یابد.

عنوان انگلیسی:MPI Based Cluster Computing for Performance Evaluation of Parallel Applications~~en~~

INTRODUCTION Merge sort is an efficient divide-and-conquer sorting algorithm. Because merge sort is easier to understand than other useful divide-and-conquer methods. One common example of parallel processing is the implementation of the merge sort within a parallel processing environment. This paper deals how to handle merge sort problem that can be split into sub-problems and each sub-problem can be solved simultaneously. With computers being networked today, it has become possible to share resources like files, printers, scanners, fax machines, email servers, etc. One such resource that can be shared but is generally not, is the CPU. Today’s processors are highly advanced and very fast, capable of thousands of operations per second. If this computing power is used collaboratively to solve bigger problems, the time taken to solve the problem can reduce drastically. However the whole operation of parallel processing also depends on the RAM available to the processors for their computation. A. Existing Frameworks 1) MPI: The specification of the Message Passing Interface (MPI) standard 1.0 [9] was Completed in April of 1994. This was the result of a community effort to try and define Both the syntax and semantics of a core messagepassing library that would be useful to a Wide range of users and implemented on a wide range of Massively Parallel Processor (MPP) platforms. 2) MPI2: All major computer vendors supported the MPI standard and work began on MPI-2, where new functionality, dynamic process management, one-sided communication, cooperative I/O, C++ bindings, Fortran 90 additions, extended collective operations, and miscellaneous other functionality were added to the MPI-1 standard [9]. MPI-1.2 and MPI-2 were released at the same time in July of 1997. The main advantage of establishing a message-passing standard is portability. 3) Openmp: It has emerged as the standard for sharedmemory parallel programming. The openmp application program interface (API) provides programmers with a simple way to develop parallel application for shared memory parallel computing. 4) MPICH2: An all-new implementation of MPI designed to support both MPI-1 and MPI-2. In MPICH2, the collective routines are significantly faster and has very low communication overhead than the “classic” MPI and MPICH versions [10]. B. Framework Used in the Proposed System This paper deals with the implementation of parallel application such as parallel merge sorting under MPI using MPICH2 for communication between the cores and for the computation. Because it is very much suitable to implement in LINUX systems. II. RELATED WORKS Traditionally, multiple processors were provided within a specially designed “parallel computer”; along these lines, Linux now supports SMP Pentium systems in which multiple processors share a single memory and bus interface within a single computer. It is also possible for a group of computers (for example, a group of PCs each running Linux) to be interconnected by a network to form a parallel-processing cluster [8]. Amit Chhabra, Gurvinder Singh [1] proposed Cluster based parallel computing framework which is based on the Master-Slave computing paradigm and it emulates the parallel computing environment. Alaa Ismail El-Nashar [2] used the dual core Window-based platform to study the effect of parallel processes number and also the number of cores on the performance of three MPI parallel implementations for some sorting algorithms. Husain Ullah Khan, Rajesh Tiwari [3] estimated the performance and speedup of parallel merge sort algorithm. An adaptive framework towards analyzing the parallel merge sort is prposed by Husain Ullah Khan, Rajesh Tiwari [4]. Kalim Qureshi [5] presented the practical performance comparison of parallel sorting algorithms on homogeneous network of workstations. Atanas Radenski [6] implemented the shared memory, message passing, and hybrid merge sorts for standalone and clustered SMPs. Manwade K. B [7] conducted analysis of Parallel Merge Sort Algorithm and evaluated the performance of parallel merge sort algorithm on loosely coupled architecture and compared it with theoretical analysis. It has been found that there is no major difference between theoretical performance analysis and the actual result. We aim to present an architecture using MPI that demonstrates the performance gain and losses achieved through parallel processing. And also demonstrates the performance dependency of parallel applications on RAM.

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