Bulk synchronous parallel

Definition

Bulk synchronous parallel (BSP) is a parallel computing model that divides computation into a series of supersteps, where each superstep consists of local computations followed by a global synchronization point. This model is designed to simplify the programming of parallel algorithms by providing a structured way to manage communication and synchronization among processes, making it particularly useful for parallel sorting and searching algorithms.

5 Must Know Facts For Your Next Test

1. In the BSP model, each superstep allows processes to perform computations locally, which reduces the need for constant communication and can enhance performance.
2. The synchronization point in BSP serves as a barrier where all processes must wait for each other, ensuring no process proceeds until all have completed their local computations.
3. BSP is particularly effective for problems involving large datasets, as it allows for efficient sorting and searching through its structured approach to process communication.
4. The communication cost in BSP can be analyzed based on the number of messages sent and received during each superstep, which helps in optimizing performance.
5. BSP facilitates load balancing by allowing the programmer to design algorithms that distribute work evenly across available processing resources.

Review Questions

• How does the bulk synchronous parallel model improve efficiency in parallel algorithms?
  • The bulk synchronous parallel model improves efficiency by structuring the computation into supersteps where processes can perform local computations independently before reaching a synchronization point. This reduces the frequency of communication between processes, which can often be a bottleneck in parallel computing. By minimizing communication overhead and allowing processes to work simultaneously during each superstep, BSP enables faster execution of parallel algorithms.
• Discuss the role of synchronization in bulk synchronous parallel computing and its impact on algorithm design.
  • Synchronization in bulk synchronous parallel computing is crucial as it dictates when processes must pause their operations to ensure data consistency. The barrier synchronization at the end of each superstep requires all processes to complete their local tasks before moving forward. This requirement influences algorithm design significantly, as developers must consider how to structure their computations to minimize the time spent waiting at synchronization points while maximizing parallel execution.
• Evaluate the implications of using bulk synchronous parallel for sorting algorithms in terms of scalability and performance.
  • Using bulk synchronous parallel for sorting algorithms has significant implications for scalability and performance. The BSP model allows these algorithms to efficiently manage large datasets by breaking down the sorting process into manageable supersteps. As data sizes grow, this approach can scale effectively across multiple processors while maintaining performance by minimizing communication during local computations. However, the reliance on synchronization points may introduce delays, so careful design is necessary to balance the benefits of parallel execution with potential bottlenecks from synchronization.

Related terms

Superstep: A phase in the BSP model that includes local computations by processes, followed by a barrier synchronization where all processes must reach the same point before moving to the next phase.

Parallel algorithms: Algorithms that are designed to run simultaneously across multiple processors, improving performance and efficiency for large-scale data processing tasks.

Synchronization: The coordination of processes to ensure they operate together effectively, preventing data races and ensuring consistency in parallel computing environments.