Lecture_17-CSCI_U511_01-Jahangir_Majumder-Spring_2025

Review and Learning Outcomes

  • Continuation of discussion on parallel and distributed systems, frameworks that enable the execution of multiple processes concurrently.

  • Key topics include:

    • Parallel and Distributed Systems:

      • These systems allow for tasks to be processed simultaneously across various architectures. Understanding their differences is essential for optimizing performance.

    • Why Parallel and Distributed Systems:

      • The need arises from an increase in data size and complexity, demanding faster processing. Parallel systems offer speed, while distributed systems provide convenience in resource sharing.

    • Multiprogramming and Parallel Computing:

      • Multiprogramming focuses on keeping multiple processes in the run state to optimize CPU utilization.

      • Parallel computing divides computational tasks into subtasks that can run concurrently, essential for high-performance computing tasks such as simulations and data analysis.

    • Parallel Algorithm Design:

      • Important for efficiently solving problems that can be broken into smaller, concurrent tasks. This involves carefully considering partitioning, communication, agglomeration, and mapping to ensure optimal execution across processors.

    • Performance:

      • Performance metrics include speedup, efficiency, and scalability, which indicate how effectively a parallelized algorithm can execute relative to its serial counterpart.

  • Homework 5 key is available on Blackboard. Students are advised to review and understand the key concepts to prepare for upcoming assessments.

  • Quiz 5 is scheduled for today, covering material from lectures 14-16, ensuring a comprehensive understanding of the content presented.

  • Sign-up for the Engage Green SPACE visit on Friday (3/21) to broaden practical experience with ecological efforts.

Serving in SPACE

  • Event Date: Friday, March 21

  • Location: Spartanburg Area Conservancy (SPACE)

  • Focus: The event aims to enhance the Upper Chinquapin Greenway by removing invasive species, an important task in ecological preservation and management.

  • Meeting Time: 9:00-11:00 AM at the HEC west side parking lot

  • Dress Code:

    • Long pants, long socks, and long-sleeved shirts are required for safety and protection.

    • Participants should bring leather work gloves if available to ensure safety during the manual work.

    • It is important to dress appropriately according to the weather conditions to ensure comfort and safety during the event.

Pre-flecting on SPACE

  • Participants are encouraged to reflect on their expectations and any fears regarding the experience.

  • Consider how the service relates to the course, emphasizing the integration of theoretical knowledge with practical application.

  • Explore professional skills such as:

    • Collegiality: Working effectively with peers and understanding team dynamics.

    • Communication: Articulating ideas and instructions clearly, both verbally and in writing.

    • Initiative: Taking proactive steps to contribute meaningfully to the project.

    • Teamwork: Collaborating toward common goals, demonstrating flexibility and adaptability.

  • Career Readiness Goal: The experience aims to practice professionalism through the development of soft skills that are crucial in various fields of work.

Engage Green Rain Policy

  • Weather may necessitate adjustments for events:

    • Participants can work through mild showers; however, cancellations will be communicated the day prior by 5 PM.

    • If rescheduling is necessary, it typically occurs 7 days later at the same time.

    • Alternative plans can be established at the time of cancellation to ensure continuous engagement.

Review: A Multiprocessor, Tightly-Coupled Parallel System

  • Structure involves multiple processors sharing caches and memory via a system bus to facilitate efficient data access.

  • Example: CPUs sharing L1 and L2 caches with a common main memory display the benefits of tightly-coupled systems in reducing latency and increasing throughput.

Review: A Loosely-Coupled Computer Cluster

  • Composed of independent workstations connected through a network, loosely-coupled clusters provide reliability and flexibility in computation, adapting easily to varying workloads.

Parallel vs Distributed Computing

  • Comparison Chart:

    • Overall Goal:

      • Parallel: Aimed primarily at achieving speed through concurrent execution.

      • Distributed: Focuses on convenience in resource sharing and system elegance.

    • Interactions:

      • Parallel: Frequent communication and data exchange among processors required.

      • Distributed: Infrequent interactions, where processes may operate independently and coordination is minimal.

    • Granularity:

      • Parallel: Fine-grained tasks are divided into small segments for simultaneous processing.

      • Distributed: Coarse-grained tasks may run as separate processes with limited interaction.

    • Reliability Assumptions:

      • Parallel: Reliability is generally assumed due to consequent high levels of control over the environment.

      • Distributed: Reliability is not assumed; systems must handle node failures and communication breaks.

Multiprogramming

  • Definition: The practice of running multiple processes in a run state effectively increases overall system efficiency.

  • Benefits: Enhances CPU utilization, leading to higher throughput, and ensures that the CPU remains engaged.

  • IO Interrupt:

    • When I/O operations for program P1 occur, the CPU can seamlessly continue executing program P2's instructions, minimizing idle time.

  • Time Sharing:

    • Mechanism through which one process is interrupted to allow multiple programs to share CPU time efficiently.

Review: Process

  • Definition: The process is a fundamental unit of execution within an operating system.

  • Requirements:

    • Memory allocation for program execution and data storage.

    • CPU registers necessary for executing commands.

  • Notably, a process can consist of multiple concurrent instances of the same program running in parallel.

Review: Thread Synchronization

  • Problems can arise when multiple threads share access to the same variables, creating potential issues with integrity and consistency of data.

  • Concurrent reads are permitted, while concurrent writes may lead to non-deterministic outcomes.

  • Synchronization mechanisms are crucial to ensure deterministic outcomes by carefully controlling the execution order of threads.

Conventional Computing

  • Traditional software is typically designed for single-threaded execution on a single CPU.

  • Tasks defined by discrete sequences of instructions executed in a strict order, leading to potentially inefficient CPU resource utilization.

Parallelism: MIPS Pipeline Example

  • The MIPS architecture employs an instruction pipeline that enhances throughput by allowing overlapping instruction execution stages.

  • Execution example: 6 instructions taking 30 cycles without pipelining versus only 10 cycles required when pipelined demonstrates the significant efficiency gains achievable through parallelism.

Parallel Computing

  • Leverages multiple processors to perform computations simultaneously, significantly improving processing times for appropriate problems.

  • Problems must be divisible into segments that can be concurrently solved, catering to high-performance computing requirements in various application scenarios.

Parallel Algorithm Design

  • Algorithm defined: A systematic sequence of actions necessary for performing tasks, crucial for problem-solving.

  • A parallel algorithm optimizes task distribution across processors for efficient execution.

  • Key design steps include:

    • Partitioning: Effectively dividing projects into manageable, smaller pieces.

    • Communication: Establishing effective pathways for interaction among worker processes.

    • Agglomeration: Merging tasks when necessary to optimize performance and resource use.

    • Mapping: Systematically assigning tasks to specific processors based on criteria such as workload and capability.

Summary

  • The focus is on understanding the contrasts between conventional and parallel computing environments.

  • Future topics will delve further into steps for parallel algorithm design and assessing the performance metrics of parallel systems.