Parallel Processing System Analysis

System Architecture

1. Input Stage – Independent Processing

• Multiple tasks are simultaneously input into the system in parallel
• Each task can be processed independently of others

2. Central Processing Network

Blue Nodes (Modification Work)

• Processing units that perform actual data modifications or computations
• Handle parallel incoming tasks simultaneously

Yellow Nodes (Propagation Work)

• Responsible for propagating changes to other nodes
• Handle system-wide state synchronization

3. Synchronization Stage

• Objective: “Work & Wait To Make Same State”
• Wait until all nodes reach identical state
• Essential process for ensuring data consistency

Performance Characteristics

Advantage: Massive Parallel

• Increased throughput through large-scale parallel processing
• Reduced overall processing time by executing multiple tasks simultaneously

Disadvantage: Massive Wait Cost

• Wait time overhead for synchronization
• Entire system must wait for the slowest node
• Performance degradation due to synchronization overhead

Key Trade-off

Parallel processing systems must balance performance enhancement with data consistency:

• More parallelism = Higher performance, but more complex synchronization
• Strong consistency guarantee = Longer wait times, but stable data state

This concept is directly related to the CAP Theorem (Consistency, Availability, Partition tolerance), which is a fundamental consideration in distributed system design.

With Claude