# Graph parallel model
In the fields of computer science and parallel computing, BSP (Bulk Synchronous Parallel), AP (Asynchronous Parallel), SSP (Single Program Multiple Data), and AAP (Asynchronous Array of Processors) are four common parallel computing models. These models are used to describe different parallel execution methods in parallel computing.
# BSP（Bulk Synchronous Parallel）model：
The BSP model emphasizes synchronization, dividing parallel computing into a series of supersteps, each of which includes three stages: computation, communication, and synchronization.
In each superstep, all processes independently execute the computation phase, and then perform data exchange and synchronization operations during the synchronization phase.
The advantage of the BSP model is that it is easy to understand and analyze, and can be used for various parallel computing tasks.
# Pregel model：
The Pregel model is a graph computing model based on BSP, specifically designed for distributed processing of large-scale graph data. It divides the graph processing task into a series of iterative supersteps, in which the graph nodes can perform local calculations and message passing. The Pregel model is very suitable for handling iterative algorithms on graphs.
The PageRank algorithm is used to evaluate the importance of different web pages on the internet. This algorithm determines the ranking of each webpage by analyzing the link relationships between them. The Pregel model is very suitable for implementing the PageRank algorithm, as PageRank requires multiple iterative calculations to be performed on the graph.
In the Pregel model, each web page is represented as a graph node, and the links between web pages are represented as graph edges. The implementation of the PageRank algorithm can be carried out as follows:
Initialization stage: The initial PageRank value of each webpage is assigned. This can be done in the first superstep of the Pregel model.
Iterative calculation: The superstep of the Pregel model begins. In each superstep, each webpage (graph node) distributes its PageRank value to its linked webpage and updates its own PageRank value based on the received PageRank value. These calculations are carried out in parallel, so each webpage can be independently calculated on different processing nodes.
Synchronization phase: At the end of each step, a synchronization operation is required to ensure that all nodes have completed the calculation of the current step. This allows the algorithm to continue iterating in the next superstep.
Convergence detection: The algorithm can check the changes in the PageRank value after each superstep to determine whether the convergence condition has been reached (the PageRank value no longer changes significantly).
Final result: Once the algorithm converges, the final PageRank value for each webpage is determined.
The implementation of the PageRank algorithm using the Pregel model can effectively handle large-scale internet graphs, as it can parallelly process a large number of nodes and edges on the graph, thereby accelerating the computational process. This is a typical application example of the Pregel model in graph algorithms.
# AP（Asynchronous Parallel）model：
The AP model emphasizes asynchrony, allowing processes to independently execute computational tasks without waiting for other processes. Communication and synchronization operations between processes are implicit and do not require explicit synchronization steps.
Example: Distributed graph computing frameworks such as Apache Girth and Apache Hama use the AP model.
In this model, each graph node can asynchronously update its state without waiting for other nodes. This is very useful for processing large-scale graph data, as synchronization between nodes may lead to performance degradation.
The AP model is suitable for parallel computing tasks that are not easily synchronized. The AP (Asynchronous Parallel) model has limited application in the field of graph computing compared to the BSP (Bulk Synchronous Parallel) model and the Pregel model, and is less common in processing graph data. The AP model is more suitable for certain asynchronous computing situations, while graph computing usually emphasizes the iterative nature of graph structure, so BSP and Pregel models are more common and classic.
Nevertheless, the AP model still holds certain importance, especially in other parallel computing fields or in some applications of non graph data processing, where the AP model may be more valuable. However, in terms of graph data processing, models such as BSP and Pregel are more widely used and studied, so they are usually more concerned. For graph computing, the most basic and classic models are still BSP and Pregel, which provide a solid foundation for the design and implementation of graph algorithms. If you are interested in the application of AP models or other parallel models in specific contexts, you can certainly delve deeper into and explore.
These parallel models have their own advantages and limitations in different application scenarios and hardware architectures, and the selection of appropriate models depends on specific problems and requirements.