Lecture-AI and Alogor Parallel Aglorithms.pptx

Lecture-AI and Alogor Parallel Aglorithms.pptx

1. Parllel Prgramming Systems Dr. Shahid Naseem Assistant Professor (IT) Division of Science & Technology University of Education, Township Campus, Lahore [email protected] Superior University, Gold Campus

2. Parallel Algorithms Superior University, Gold Campus A parallel algorithm is a type of algorithm that divides a larger problem into smaller independent sub-tasks, which can be executed simultaneously on multiple processing devices. After the sub-tasks are completed, their results are combined to form a solution to the original problem. This approach enhances computational efficiency by utilizing the capabilities of parallel computing, where multiple processors perform tasks concurrently.

3. Parallel Algorithms Superior University, Gold Campus Parallel algorithms offer several advantages, particularly in the context of parallel and distributed computing. Here are some key benefits: Increased Speed and Efficiency : By dividing tasks among multiple processors, parallel algorithms can significantly reduce the time required to solve complex problems, especially for large datasets or computationally intensive tasks. Scalability : Parallel algorithms can scale well as the number of processors increases. This means that as more processing units are added, the performance can continue to improve, allowing for the handling of larger problems .

4. Parallel Algorithms Superior University, Gold Campus Resource Utilization : They make better use of available hardware resources. Instead of relying on a single processor, parallel algorithms distribute workloads across multiple processors, which leads to more efficient use of memory and processing power. Handling Large Data Sets : Parallel algorithms are particularly effective for applications involving large data sets, such as scientific simulations, big data analytics, and image processing, where data can be partitioned and processed concurrently.

5. Parallel Algorithms Superior University, Gold Campus Improved Performance with Tasks with Low Latency : When tasks involve high latencies (like network communication), parallel algorithms can improve performance by allowing other processes to execute while waiting for operations to complete. Improved Fault Tolerance : In distributed systems, parallel algorithms can provide redundancy. If one processor fails, others can continue processing, allowing the system to be more resilient to failures. Enhanced Problem Solving : Some problems, particularly in fields like machine learning, optimization, and numerical analysis, can only be practically solved using parallel algorithms due to their complexity and size .

6. Concurrency and Synchronization Superior University, Gold Campus Concurrency can be achieved by using multiple processors, cores, or threads, depending on the level of parallelism that you want to achieve. Synchronization is the coordination of your concurrent tasks, to ensure that they do not interfere with each other, or access shared resources in an inconsistent or unsafe way.

7. Concurrency and Synchronization Superior University, Gold Campus Concurrency and synchronization are essential concepts in parallel and distributed computing. Concurrency allows multiple processes to execute simultaneously, improving system responsiveness and efficiency. In distributed systems, synchronization is crucial for maintaining consistency and coordination among different components, ensuring they can cooperate effectively.

8. Concurrency and Synchronization Superior University, Gold Campus This involves mechanisms that manage access to shared resources and prevent conflicts, which is vital for the integrity of distributed computations. It's important to double-check specific implementations and theories, as they can vary widely depending on the context and system architecture .

9. Techniques used for Synchronization Superior University, Gold Campus In parallel computing, various techniques are employed for synchronization to ensure that processes coordinate effectively and manage access to shared resources. Here are some common synchronization techniques: 1. Mutexes (Mutual Exclusions ): Mutexes are locks used to ensure that only one thread can access a resource at a time. If one thread holds a mutex , other threads are blocked until the mutex is released .

10. Techniques used for Synchronization Superior University, Gold Campus Semaphores: Semaphores are signaling mechanisms that can be used to control access to a common resource by multiple processes. They can be binary (similar to mutexes ) or counting, allowing a certain number of threads to access resources simultaneously . 3. Locks: Many languages and frameworks provide various types of locks (e.g., read/write locks) that allow threads to request permission to access a resource. Read locks allow multiple threads to read simultaneously, while write locks ensure exclusive access .

11. Techniques used for Synchronization Superior University, Gold Campus Barriers: Barriers are synchronization points where processes or threads wait until all participants reach the barrier before continuing. This is useful for ensuring that certain computations are completed before proceeding. Condition Variables: Condition variables allow threads to wait for a certain condition to be met before continuing execution. They are often used in conjunction with mutexes , allowing threads to sleep until notified of a condition change. Atomic Operations: Atomic operations are low-level operations that complete in a single step relative to other threads. They ensure that read-modify-write sequences can be performed without interruption, avoiding race conditions .

12. Techniques used for Synchronization Superior University, Gold Campus Message Passing: In distributed environments or certain parallel computing architectures, processes communicate and synchronize through message passing, where they send and receive messages to coordinate actions without shared memory. Fine-Grained Locking: Instead of locking a large section of code or a resource, fine-grained locking involves locking smaller sections, allowing more concurrency and reducing contention.

13. Granularity Superior University, Gold Campus In parallel computing, granularity is a measure of the ratio of computation to communication. Periods of computation are typically separated from periods of communication by synchronization events. The grain of parallelism is constrained by the inherent characteristics of the algorithms constituting the application.

14. Granularity Superior University, Gold Campus Granularity in parallel and distributed computing refers to the size of tasks or units of work within a computation. It measures the amount of computation relative to communication, often expressed as the ratio of computation time to communication time (G = Tcomp / Tcomm ).

15. Granularity Superior University, Gold Campus A finer granularity involves smaller tasks that may introduce more overhead due to communication, while coarser granularity consists of larger tasks that can be executed with less frequent communication but may lead to underutilization of resources .

16. Types of Granularity Superior University, Gold Campus Granularity can be categorized into two main types: 1. Coarse Granularity: Involves large tasks that take a significant amount of time to complete. Each task may involve a substantial amount of computation, and there are relatively few tasks running concurrently. This can lead to lower overhead in communication between tasks since there are fewer interactions, but it may not fully utilize the available processing resources .

17. Types of Granularity Superior University, Gold Campus 2. Fine Granularity: Involves breaking down tasks into smaller units of work that can be executed in parallel. This allows for a high degree of concurrency, as many small tasks can be processed simultaneously. However, fine granularity can also introduce overhead from frequent communication and synchronization between tasks, which can diminish the overall performance gains.

18. Load Balancing Superior University, Gold Campus Load balancing in parallel and distributed computing involves redistributing workloads among multiple computing nodes, such as processors or servers, to enhance performance and efficiency. This process helps ensure that no single node is overwhelmed while others are underutilized, leading to optimized resource use and faster processing times.

19. Load Balancing Superior University, Gold Campus Load balancing improves system performance in several ways: Optimized Resource Utilization : By distributing workloads evenly across all available nodes, load balancing prevents any individual node from becoming a bottleneck. This ensures that resources such as CPU, memory, and network bandwidth are used efficiently. Reduced Response Time : When workloads are balanced, each node can process requests more quickly, leading to faster response times for users. This is particularly important in high-traffic environments where timely responses are crucial .

20. Load Balancing Superior University, Gold Campus Increased Throughput : By maximizing the use of available resources, load balancing can increase the overall throughput of the system. More tasks can be completed in a given time period, enhancing productivity. Scalability : Load balancing facilitates scalability by allowing new nodes to be added easily to the system. As demand increases, additional nodes can take on more workload, maintaining performance levels. Fault Tolerance and Reliability : In the event that a node fails, load balancers can redirect traffic to other operational nodes, ensuring continued service availability. This redundancy increases system reliability .

21. Load Balancing Superior University, Gold Campus 6. Improved Latency Management : Load balancing can reduce latency by routing requests to the nearest or least-busy server, helping to enhance the user experience, especially in distributed systems.