How do you manage communication among parallel processes?

1 Why communication matters

Communication among parallel processes is essential for several reasons. First, communication enables data sharing and exchange, which is often required for solving interdependent tasks or subproblems. Second, communication facilitates coordination and synchronization, which is necessary for ensuring consistency, correctness, and efficiency of the parallel execution. Third, communication affects performance and scalability, which depend on how much, how often, and how fast the processes can communicate with each other.

2 Types of communication

There are two main types of communication among parallel processes: message passing and shared memory. Message passing is a method where processes send and receive data through explicit messages, using some communication protocol or library. Message passing is suitable for distributed systems, where processes run on different machines or networks. Shared memory is a method where processes access and modify data through a common memory space, using some synchronization mechanism. Shared memory is suitable for multicore systems, where processes run on the same machine or processor.

3 Communication patterns

Different communication patterns can be used to describe how processes communicate with each other, depending on the problem structure and the communication type. Point-to-point is a simple and efficient pattern, but it can be limited in scalability and flexibility. Broadcast is useful for distributing data or instructions, but it can be costly in bandwidth and synchronization. Collective operations, such as reduction, scan, or barrier involve all or some of the processes, however this pattern can be complex and challenging to implement and optimize. Lastly, pipeline is useful for processing data in stages, but it can be prone to bottlenecks and latency.

4 Communication models

Different communication models can be used to define how processes send and receive data, depending on the communication type and programming language. Blocking is a simple, reliable model where a process waits until the communication is completed before continuing its execution, but it can reduce performance and concurrency. Non-blocking is more complex and error-prone but can improve performance and concurrency by allowing a process to continue its execution while the communication is in progress. Synchronous ensures consistency and correctness, but can cause deadlock and inefficiency, whereas asynchronous increases performance and scalability, though it can cause inconsistency and complexity.

5 Communication challenges

Communication among parallel processes presents several challenges, such as overhead, consistency, and scalability. To minimize these issues, communication should be balanced, optimized, controlled, synchronized, verified, designed, distributed and adapted as much as possible. This will help to reduce the costs associated with communication and ensure the accuracy of the data and execution. Additionally, it will allow the parallel system to scale up and handle more processes, data or complexity.

6 Communication tools

Various tools and libraries can assist in managing communication among parallel processes, depending on the communication type and programming language. MPI (Message Passing Interface) is a standard library for message passing communication in distributed systems, with support for various communication types, patterns, and models, and available for C, C++, Python, and Java. OpenMP (Open Multi-Processing) is a standard library for shared memory communication in multicore systems, supporting synchronization mechanisms such as locks, barriers, and atomic operations, and available for C, C++, and Fortran. CUDA (Compute Unified Device Architecture) is a platform and library for parallel computing on GPUs (graphics processing units), with support for both message passing and shared memory communication using different memory spaces and models, based on C and C++.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?