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Lecture2

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  • 1. CS-416 Parallel and Distributed Systems
    JawwadShamsi
    Lecture #2
    19th January 2010
  • 2. How Much of Parallelism
    Decomposition: The process of partitioning a computer program into independent pieces that can be run simultaneously (in parallel).
    Data Parallelism
    Task Parallelism
  • 3. Scalar Instruction
    Single Instruction at a time
    Load R1 @ 1000
    Vector Instruction
    Vector operations
    C(1:100)=A(1:100)+B(1:100)
    Super computer
    Vector Instructions with pipelined floating point arithematic
  • 4. Pipelining
    Pipelining overlaps various stages of instruction execution to achieve performance.
    At a high level of abstraction, an instruction can be executed while the next one is being decoded and the next one is being fetched.
    This is akin to an assembly line for manufacture of cars.
  • 5. Pipeline-limitations
    Pipelining, however, has several limitations.
    The speed of a pipeline is eventually limited by the slowest stage.
    For this reason, conventional processors rely on very deep pipelines (20 stage pipelines in state-of-the-art Pentium processors).
    However, in typical program traces, every 5-6th instruction is a conditional jump! This requires very accurate branch prediction.
    The penalty of a misprediction grows with the depth of the pipeline, since a larger number of instructions will have to be flushed.
  • 6. Pipelining and Superscalar Execution
    One simple way of alleviating these bottlenecks is to use multiple pipelines.
    The question then becomes one of selecting these instructions.
  • 7. Super Scalar Execution
    Issue multiple instructions during the same CPU cycle
    How?
    By simultaneously dispatching multiple instructions to redundant units
    not a separate CPU core
    but an execution resource within a single CPU e.g. ALU
  • 8. Difference Between Vector Instruction and Pipelining
  • 9. Parallel Processing
    Computer speed / output is increased through parallel processing
    Increase hardware
    Increase cost
    Tech advancements
    Increase in cost is minimal
  • 10. Types of Parallelism - explained
  • 11. Data Parallelism
    Same code segment runs concurrently on each processor
    Each processor is assigned its own part of the data to work on