How can you choose software design patterns for concurrent and parallel programming?

1 Problem domain

The first criterion is to identify the problem domain of your application, such as data processing, computation, interaction, or coordination. Different problem domains require different approaches to concurrency and parallelism, and therefore different design patterns. For example, if your application involves data processing, you might use the pipeline pattern, which divides the data into chunks and passes them through a series of stages that perform operations on them concurrently. If your application involves computation, you might use the fork-join pattern, which splits a large task into smaller subtasks that can be executed in parallel and then combines the results.

2 Task granularity

The second criterion is to determine the task granularity of your application, which is the size and complexity of the tasks that you want to execute concurrently or in parallel. Task granularity affects the trade-off between overhead and speedup. Overhead is the extra cost of creating, managing, and communicating among the tasks. Speedup is the improvement in performance due to parallelism. Generally, finer-grained tasks have higher overhead and lower speedup, while coarser-grained tasks have lower overhead and higher speedup. Therefore, you should choose a design pattern that matches the task granularity of your application. For example, if your application has fine-grained tasks, you might use the thread pool pattern, which creates a fixed number of threads that can execute tasks from a queue. If your application has coarse-grained tasks, you might use the master-worker pattern, which assigns tasks to multiple workers that can run on different processors or machines.

3 Task dependency

The third criterion is to analyze the task dependency of your application, which is the degree of coupling and communication among the tasks. Task dependency affects the complexity and correctness of concurrent and parallel programming, as it may introduce issues such as deadlock, race condition, and inconsistency. Therefore, you should choose a design pattern that minimizes or manages the task dependency of your application. For example, if your application has independent tasks, you might use the map-reduce pattern, which applies a function to each element of a collection in parallel and then aggregates the results. If your application has dependent tasks, you might use the monitor-object pattern, which encapsulates the shared data and provides synchronized methods for accessing and modifying it.

4 Task variability

The fourth criterion is to consider the task variability of your application, which is the variation in the workload and behavior of the tasks. Task variability affects the load balancing and adaptability of concurrent and parallel programming, as it may cause uneven distribution of work and unpredictable performance. Therefore, you should choose a design pattern that adapts to the task variability of your application. For example, if your application has static tasks, you might use the divide-and-conquer pattern, which recursively breaks down a problem into smaller subproblems that can be solved in parallel. If your application has dynamic tasks, you might use the reactor pattern, which handles events from multiple sources by dispatching them to appropriate handlers.

5 Task environment

The fifth criterion is to evaluate the task environment of your application, which is the hardware and software platform that supports the execution of the tasks. Task environment affects the feasibility and efficiency of concurrent and parallel programming, as it may impose constraints and opportunities for parallelism. Therefore, you should choose a design pattern that exploits the task environment of your application. For example, if your application runs on a single-core processor, you might use the event-driven pattern, which uses callbacks to handle asynchronous events without blocking the main thread. If your application runs on a multi-core processor, you might use the shared-memory pattern, which uses locks, semaphores, or atomic operations to coordinate access to shared data among multiple threads.

6 Task quality

The sixth criterion is to ensure the task quality of your application, which is the reliability, maintainability, and readability of the code that implements the tasks. Task quality affects the development and testing of concurrent and parallel programming, as it may require special tools and techniques to debug and verify the code. Therefore, you should choose a design pattern that enhances the task quality of your application. For example, if your application has complex tasks, you might use the abstract factory pattern, which creates objects that implement a common interface without exposing their concrete classes. If your application has simple tasks, you might use the lambda expression pattern, which uses anonymous functions to express tasks concisely and elegantly.

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?