CSC780 Parallel Processing UITM Assignment Example Malaysia
The course teaches you about parallel computing and its various aspects, including architectures for ice-like computers that can execute multiple tasks at once. You’ll learn how to optimize your code so it takes full advantage of this new technology in order to get better performance than ever before!
By the end of the course, you’ll have built a complete application that can be used to visualize various real-world phenomena. You will have an understanding of how this technology works, and will know how to make the right decisions when designing code for it.
The parallel operations course will teach you all about the many aspects of this exciting field. You’ll learn what it takes to be successful in a job where your skills can make an impact on thousands or even millions waiting for YOU!
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Assignment Brief 1: Demonstrate an understanding of parallelism fundamentals and algorithm design.
Parallelism is a programming technique that enables you to run multiple tasks at the same time. This can be done by splitting up a task into smaller pieces that can be executed concurrently, or by using multiple cores or processors to work on different parts of the same task simultaneously.
Algorithm design is the process of designing an algorithm to solve a problem. The goal is to come up with an efficient solution that runs as quickly as possible, while also taking into account potential bottlenecks and other performance concerns.
Queues are one of the most commonly used data structures in concurrent algorithms. It is a simple way to store a collection of values that can be accessed from multiple threads, and it also provides two basic operations: enqueue and dequeue. Enqueuing adds an element to the queue while dequeuing removes the first element from the queue. If the queue is empty when dequeuing, the element will not be removed.
In order to demonstrate an understanding of parallelism fundamentals and algorithm design, one must be able to identify opportunities for parallelism in an algorithm and split the problem into manageable chunks that can be solved in parallel.
Additionally, one must understand how to preserve data dependencies so that the results of each computation can be used by the following computations. Furthermore, one must understand how to correctly synchronize threads so that the correct result is produced. Finally, one must take into account practical considerations such as memory usage and execution time when designing algorithms for parallel execution.
Assignment Brief 2: Design and develop a parallel program for solving a simplified computational problem.
There are many ways to design and develop a parallel program for solving a simplified computational problem. One way would be to start by designing and developing a serial program, and then split the problem into multiple parts that can be run in parallel. Another way would be to start with a parallel program and divide the problem into smaller parts that can be run in sequence. There are pros and cons to both methods, so it’s important to choose the approach that will work best for the specific problem at hand.
When designing and developing a parallel program, it’s also important to consider the communication between the different processors. If the processors need to communicate frequently, then the performance of the program will be limited by the speed of that communication.
The communication between processors can be through direct memory access (DMA), global memory, or by making it explicit that one processor is waiting for data from another.
The latter method has the advantage that the software developer is in control of when communication occurs, whereas with DMA and global memory, this control might instead fall to the hardware developers. One major factor in parallel programming is the concept of synchronization between processors.
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Assignment Brief 3: Evaluate the performance of a parallel program
It is difficult to evaluate the performance of a parallel program without knowing more about the specific program and hardware. In general, though, a well-written parallel program should be able to take advantage of multiple processors or cores to speed up execution.
There are many factors that can affect the performance of a parallel program, such as the number of processors available, the type of data being processed, the communication pattern between processors, and the software libraries used. It is often necessary to do some experimentation to find the best configuration for a particular application.
It is difficult to make generalized statements about how well a parallel program will run. For example, simply adding more processors might not improve performance if the problem size does not increase proportionately (that is, if there are diminishing returns).
Or, it may be that the communication overhead between processors cancels out any improvement in running time. In this section, we consider some of the factors that determine how well a program runs on multiple processors.
A parallel program is designed to take advantage of multiple processors or cores. This can result in a significant performance boost, especially for tasks that can be divided into smaller parts that can be processed independently.
There are several factors that can affect the performance of a parallel program, including the number of processors or cores available, the type of task being performed, and the algorithms used. Generally speaking, the more processors or cores available, the better the performance will be.
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