Introduction to Parallel Processing
The chapter covers key foundational aspects of parallel processing, highlighting its necessity due to limitations in single-processor performance and exploring the architectures that facilitate parallel computation. It delves into the intricacies of pipelining, outlining its operational mechanisms and the associated challenges such as hazards, while providing an overview of different parallel architectures classified through Flynn's Taxonomy. The critical role of interconnection networks in achieving effective parallelism is also discussed, emphasizing their impact on performance and scalability.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Parallel processing enhances computational power by enabling multiple tasks to be executed concurrently.
- Pipelining increases CPU throughput by overlapping instruction execution but introduces complexities like structural, data, and control hazards.
- Interconnection networks are vital for effective communication in parallel systems, influencing their performance and scalability.
Key Concepts
- -- Parallel Processing
- A computing paradigm that breaks down large problems into smaller tasks, executing them simultaneously on multiple processing units.
- -- Pipelining
- An architectural optimization that allows multiple instruction phases to overlap, increasing instruction throughput.
- -- Flynn's Taxonomy
- A classification system for parallel computing architectures based on the number of instruction and data streams.
- -- Interconnection Networks
- Networks that facilitate communication between processing elements in parallel systems, critical for performance.
Additional Learning Materials
Supplementary resources to enhance your learning experience.