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Performance Analysis of Algorithms on Shared Memory

Performance examination of algorithms on shared memory, concept passing and cross models for stand-alone and clustered SMPs



Parallel computing is a form of computation that allows many instructions to be run together, in parallel in a program. This can be achieved by breaking up an application into self-employed parts so that each processor can execute its area of the program simultaneously with the other processors. This is achieved about the same computer with multiple processors or with amount of individual computers linked by a network or a combination of the two.

Parallel computing has grown beyond the high-performance processing community because of the intro of multi-core3 and multi-processor computers at a reasonable price for the average consumer.

Recent desktop and powerful processors provide multiple hardware threads theoretically became aware by hardware multithreading and multiple processor chip cores about the same chip. Programmers will be confronted with hundreds of hardware threads per processor chip as exploitable education level parallelism in applications is limited and the processor clock frequency cannot be increased any further due to power consumption and heating problems exploiting thread level parallelism becomes unavoidable if further improvement in processsor performance is necessary and there is no doubt that our requirements and expectations of machine performance increase further. Which means that parallel programming will actually concern a majority of request and system developers later on even in the desktop and embedded website. A style of parallel computation includes a parallel coding model and a matching cost model.

A parallel encoding model represents an abstract parallel machine by its basic businesses such as arithmetic functions spawning of tasks reading from and writing to shared ram or sending and receiving announcements. Their effects on the status of the computation the constraints of when and where these can be applied and how they could be composed specifically a parallel programming model also includes at least for shared

memory development models a memory space model that describes how so when memory accesses can become visible to different parts of a parallel computer. The memory model sometimes is given implicitly a parallel cost model that affiliates a cost which usually details parallel execution time and source of information job with each basic operation and details how to anticipate the gathered cost of constructed businesses up to entire parallel programs A parallel development model is often associated with one or several parallel encoding languages or libraries that realize the model Parallel algorithms that are usually formulated in terms of a specific parallel encoding model.

OpenMP (Start Multi-Processing), Message passing Software (MPI) and Cross types OpenMP/MPI is a parallel development model where communication between processes is performed by interchanging information. OpenMP is an API that helps multi-platform shared memory space multi-processing development in C, C++ and Fortran of all processor architectures and os's, including Solaris Linux, , AIX, HP-UX, Macintosh Operating-system X and Home windows platforms.

MPI is a model for a distributed memory system where communication can't be achieved by writing of parameters. The Communication Passing User interface (MPI) is the de-facto standard for development distributed memory systems as it offers a straightforward communication API and eases the task of developing portable parallel applications.

Hybrid OpenMP+MPI facilitates cooperative distributed memory development across clustered SMP nodes. MPI provides communication among various SMP nodes whereas OpenMP manages the workload on each SMP node. MPI and OpenMP are used in tandem to manage the overall concurrency of the application form.


As individual processors are not capable of handling the most important computational problems for their inherent complexity, the idea of putting multiple processors to focus on an individual program came into existence thus motivating the thought of parallel computing.

Parallel computing is the use of any parallel computer to lessen the time needed to solve an individual computational problem. this can be a multiple-processor computer system aiding parallel coding. Two categories of parallel computer systems are multi-computers and centralized multiprocessors. Multi-computer is a parallel computer designed out of multiple pcs and an interconnection network where the processors on different pcs interact by transferring messages to each other. Centralized multi-processor( also called as symmetrical multiprocessor or SMP) is one where all the CPUs show access to an individual global memory.


Applications were made to run on a single systems. But specific systems aren't capable of fixing the significant problems successfully for their inherent complexness.

The limitation is the fact that it cannot harness the capacity of the multi-core processor. Hence multi-threading the applications must be done.


Parallel coding combines the distributed memory space parallelization on the node interconnect with distributed storage area parallelization inside each node. The issues and the potentials of the prominent encoding models on hierarchically organized hardware is explained : Real MPI (subject matter passing software), genuine OpenMP (with sent out shared recollection extensions) and cross MPI+OpenMP in a number of flavors. We identify few situations where the hybrid coding model can indeed be the superior solution because of memory consumption or increased load balance and reduced communication needs.

Hybrid programming presents OpenMP into MPI applications makes more efficient use of the distributed memory on SMP nodes, thus mitigating the need for explicit intra-node communication. Bringing out MPI and OpenMP through the design/coding of a new request can help optimize efficiency, scaling and performance.

At the recent time, the cross model has started to catch the attention of more attention, for at least two reasons. The first is that it is relatively easy to choose a terminology/library instantiation of the cross types model: OpenMP plus MPI. While there may be other solutions, they remain research and development tasks, whereas OpenMP compilers and MPI libraries are now sturdy commercial products, with implementations from multiple distributors.

The second reason is the fact scalable parallel personal computers now may actually encourage this model. The most effective machines now practically all consist of multi-core nodes linked by a high speed network. The thought of using OpenMP threads to exploit the multiple cores per node (with one multithreaded process per node) when using MPI to converse among the list of nodes appears obvious. Yet you can also use an "MPI just about everywhere" approach on these architectures, and the data on which approach is better is complicated and inconclusive.


Multithreading of applications on a clustered system using cross methodology. The objective is to raise the performance of request on clusters using Hybrid methodology.

  • Network intrusion diagnosis, cryptography, multiparty computations are a few of the main users of parallel processing techniques.
  • Embedded systems progressively rely on sent out control algorithms.
  • A modern automobile involves tens of processors connecting to perform complex tasks for optimizing handling and performance.
  • conventional organized peer-peer networks impose overlay systems and utilize algorithms immediately from parallel computing.
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