CSC580 Parallel Processing Assignment Example Malaysia
The assignment CSC580 combines theoretical and practical learning to introduce students of all levels, from beginners up through advanced courses. Topics include parallel architectures & algorithms for improving computational performance on GPU accelerated systems as well as applications in computer science that use these new techniques
The introduction into both theory-based discussions about how computers work together more efficiently or discuss specific topics outside their field will provide you with various skills needed when working professionally in the technology field.
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The course is a survey of the most important programming techniques for parallel computing on multi-core processors and clusters of computers. The class focuses on practical issues: How do you parallelize your application? How do you structure the communication between processors? Which library should you use? We cover several different programming models (OpenMP, MPI, MapReduce), but we spend more time on ideas than on language details.
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Assignment Activity 1: Apply the concept, design and approach of parallel processing
Parallel processing is a system architecture where two or more independent computing processes are executed at the same time to speed up the performance of a given task. As anyone who has ever heard one person speaking with another knows, two people speaking as one voice leads to slower and less fluid conversation. Similarly, any computer process designed for human interaction like exploring a website or using software needs some level of control over what happens next; otherwise, the load would be unpredictable and it would make for difficult use by those interacting with it.
Parallel processing allows these processors (at minimum) to execute in parallel and thereby giving each processor an equal share in work dependency. This means that tasks can be processed quickly and users do not have to wait until the completion of another task to carry on.
Parallel processing is when many activities are processed at the same time. Parallel processing has been proven to be the best way to take on large projects, which I hope you can find out by reading this article. The general idea behind parallel processing is that every few seconds, you have a new step completed in your project. This technique not only makes it far less stressful but also allows you to work on other things while progress occurs in each “processing stage.”
To put this into perspective for you, think of an assembly line at a factory. Jobs are finished in series and need all parts together before they can move forward. You need both input and output stages done before moving on to another part of the line. With parallel processing, the factory is split up into individual stations and each station does its particular job. Every few seconds, another task is finished. This not only saves time but also makes the entire process much easier to manage.
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Assignment Activity 2: Explain the fundamentals of parallelism and the relationship between parallel programming architecture, algorithm and application.
The term “parallelism” can refer to two different situations. It usually refers to reducing the number of instructions in a single process by performing them at the same time. But, it can also refer to running multiple processes in parallel on a system – doing one set of work while another set is idle and vice versa.
To achieve this form of parallelism, you take advantage of the natural overlap between CPUs: many cores executing work simultaneously and in turn. This reduces power usage since the CPU isn’t always working at full capacity and speeds up execution times because it takes less time for an instruction to get from your computer’s memory cache all the way up to its destination cores if more cores are waiting there, waiting for work to do.
Parallel architecture is very different from traditionally run architectures, and it has been around for quite some time. In the simplest form of parallelism, tasks are divided into equal parts and these tasks are performed on separate processors; this is known as the ‘divide-and-conquer’ method. A slightly more complex variation on this approach is the parallelimineal calculations with results combined at the end (known as ‘Sipos’). Last but not least, we have asynchronous data processing with feedback (what we would call “GP computers” today).
Parallel programming is a computer programming paradigm that splits up the problems into a small, more manageable subsets of problems and solves these problems simultaneously. Parallel programming can be used to solve computational problems faster by distributing them more efficiently on multiple processors
Parallelism is the concept of executing commands at the same time or at overlapping times. It is a process that has been done by machines for a very long time, but through it, people have been able to make things happen much faster. These machines today are computers and they carry out more than one programme at once, e.g. an internet browser opens as you work on your spreadsheet programme on Wordpad.
Assignment Activity 3: Demonstrate team skills in implementing and testing parallel modules.
When implementing a parallel module, each of the modules in the set should have a similar interface and perform a comparable amount of work. The algorithms that will be executed in parallel can be thought of as being run on different machines in order to optimize the processing time. Any given algorithm’s execution is dependent on how it interacts with other algorithms rending them all interdependent to one another, so one correctly implemented system should take care not to include any unnecessary dependencies between modules; simple dependence won’t cut it here.
Testing for this would consist primarily of unit tests that test specific functionality or parts (or subsystems) of the system, but also may require some degree of integration testing to make sure that correct communication and information flow is maintained.
Note that the interfaces should be designed such that they can operate independently of one another, and may not even need to know about other algorithms. Communication between these modules (and external components) must be established via a set standard interface.
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