Parallel Computing and its Applications
18 Questions
0 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What is a significant application area of High Performance Computing in life sciences?

  • Financial modeling
  • Weather forecasting
  • Social media analytics
  • Genetic sequencing (correct)

    • Which of the following factors is primarily driving the growth of High Performance Computing?

  • Improvements in user interfaces
  • Increase in data generation (correct)
  • Decreased cost of computing hardware
  • Reduction in data storage needs

    • Why can't single-core processors meet the simulation needs in fields like climate science?

  • They lack sufficient resources for complex calculations. (correct)
  • They process data too slowly for real-time analyses.
  • They are too expensive to produce.
  • They consume too much energy for long-term use.

    • What is the main purpose of aggregating computing power in High Performance Computing?

    <p>To deliver performance surpassing typical desktop systems.</p> Signup and view all the answers

    In which of the following areas is HPC NOT typically applied?

    <p>Sports management</p> Signup and view all the answers

    What is a primary application of High Performance Computing (HPC) in Life Science?

    <p>Drug Designing</p> Signup and view all the answers

    Which of the following areas is NOT an application of HPC in Space Science?

    <p>Geoengineering</p> Signup and view all the answers

    In the context of HPC applications, which area involves the study of genome sequencing?

    <p>Life Science</p> Signup and view all the answers

    Which application does NOT belong to the HPC in Space Science category?

    <p>Geological Structure Analysis</p> Signup and view all the answers

    Which application within Life Science contributes to advancements in cancer treatment?

    <p>Protein Folding</p> Signup and view all the answers

    Which application area within HPC is focused on the exploration of climatic conditions?

    <p>Atmospheric Science</p> Signup and view all the answers

    What critical role does HPC play in enhancing multimedia and animation experiences?

    <p>Real-Time Rendering</p> Signup and view all the answers

    Which application focuses on modeling and simulation in drug design?

    <p>Genome Sequencing</p> Signup and view all the answers

    Which type of parallelism allows multiple independent modules of a program to execute simultaneously?

    <p>Functional Parallelism</p> Signup and view all the answers

    What does MISD represent in Flynn's Classical Taxonomy?

    <p>Multiple Instructions Single Data</p> Signup and view all the answers

    What type of computing architecture does SIMD represent?

    <p>Single Instruction Multiple Data</p> Signup and view all the answers

    In which scenario is pipelining particularly effective?

    <p>When a sequence of tasks can be executed in an overlapped fashion</p> Signup and view all the answers

    In terms of memory, how does MIMD differ from SISD?

    <p>MIMD can have shared or distributed memory for parallelism.</p> Signup and view all the answers

    Study Notes

    Parallel Computing

    • Parallel computing is a form of computation where multiple calculations are performed concurrently.
    • This approach divides large problems into smaller, independent tasks that are solved simultaneously.

    Application Areas of HPC

    • Science:

      • Space science: Used in astrophysics and astronomy.
      • Earth science: Utilized for understanding geological structure properties, water resource modeling, and seismic exploration.
      • Atmospheric science: Employed for climate and weather forecasting, as well as air quality analysis.
      • Life science: Applications include drug design, genome sequencing, and protein folding.
      • Nuclear science: Used in nuclear power, nuclear medicine for cancer treatment, and defense.
      • Nano science: Applications include semiconductor physics, microfabrication, molecular biology, and exploration of new materials.

    • Engineering:

      • Crash simulation: Used for analyzing impacts in automotive and mechanical engineering.
      • Aerodynamics simulation and aircraft designing: Applications include aeronautics and mechanical engineering.
      • Structural Analysis: Utilized for analyzing structures in civil engineering and architecture.

    • Multimedia and Animation:

      • Increased Complexity of Content:
        • High Resolution: The demand for 4K, 8K, and beyond requires significant processing power.
        • Complex Effects: Advanced visual effects (VFX) and rendering techniques necessitate heavy computations.
      • Real-Time Rendering: HPC enables real-time rendering for gaming and VR applications, delivering interactive experiences.
      • Simulation: HPC enables realistic physics simulations for animations and gaming environments.
      • Large Data Processing: HPC is crucial for managing and processing massive datasets like video files and 3D models.

    Key Drivers of HPC

    • Growth in data generation: The increasing generation of data across various fields drives the need for more powerful computing.
    • Complex simulations and modeling: Fields like climate science, physics, and bioinformatics require complex simulations, driving the need for greater computing power.
    • Single-core processor limitations: Single-core processors lack the necessary resources for demanding simulations.

    Parallel Processing

    • Von Neumann Architecture:
      • Stores both program instructions and data in a memory unit.
      • Processes instructions by fetching, decoding, retrieving data, executing, and storing results.

    Flynn's Classical Taxonomy

    • Based on the source of parallelism:
      • SISD (Single Instruction Single Data): Uniprocessor architecture, essentially sequential processing.
      • SIMD (Single Instruction Multiple Data): Parallelism achieved by processing multiple data items simultaneously with the same instruction.
      • MISD (Multiple Instructions Single Data): This category is less common and involves multiple instructions operating on a single data stream. This could be seen in specialized architectures like systolic arrays and pipelines.
      • MIMD (Multiple Instruction Multiple Data): Parallelism comes from processing multiple instructions on multiple data streams. There are two main types:
        • Shared Memory: Processors access and share data in a common memory space.
        • Distributed Memory: Processors have their own private memory and communicate with each other over a network.

    Pipelining

    • Example: A 3-stage pipeline processor can execute 4 tasks with 3 subtasks each, taking 6 clock cycles instead of 12.

    Types of Parallelism

    • Data Parallelism: Many data items are processed simultaneously using the same operation (fine-grained parallelism).
    • Functional Parallelism: Different independent modules of a program are executed concurrently (coarse-grained parallelism).
    • Overlapped/Temporal Parallelism: Tasks in a program are executed in an overlapped manner for efficiency, with pipelining being a significant example.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    High Performance Computing (HPC) Lecture PDF

    Description

    This quiz explores parallel computing, a method involving simultaneous calculations to solve large problems. Learn about its diverse applications in various fields such as science and engineering, including space science, life science, and crash simulation.

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser