What are the best practices for optimizing an algorithm for parallel processing?

1 Choose the right level of granularity

Granularity refers to the size and frequency of the tasks that you assign to each processor or core. If the tasks are too large or too infrequent, you will not benefit from parallel processing, as some processors will be idle while others are busy. If the tasks are too small or too frequent, you will incur a lot of overhead from creating, scheduling, and coordinating the tasks. The optimal granularity depends on the nature of your algorithm, the number and type of processors, and the available memory and bandwidth. You can use analytical or empirical methods to estimate the best granularity for your algorithm.

2 Minimize communication costs

Communication costs are the time and resources required to exchange data and messages between processors or cores. Communication costs can significantly affect the performance and scalability of your parallel algorithm, especially if the data is large, complex, or distributed. To minimize communication costs, you should try to reduce the amount and frequency of data transfer, use efficient communication protocols and libraries, and exploit locality and caching. You should also design your algorithm to avoid unnecessary or redundant communication, such as by using collective operations, pipelining, or data aggregation.

3 Use appropriate data structures

Data structures are the way you organize and store your data in memory. Data structures can have a big impact on the performance and complexity of your parallel algorithm, as they determine how easy or hard it is to access, manipulate, and share the data. To use appropriate data structures, you should consider the following factors: the size and shape of the data, the type and pattern of the operations, the degree and direction of the data dependencies, and the availability and consistency of the data. You should choose data structures that are suitable for your algorithm, such as arrays, matrices, lists, trees, graphs, or hashes.

4 Avoid race conditions and deadlocks

Race conditions and deadlocks are two common problems that can occur in parallel algorithms, causing incorrect results or system failures. A race condition happens when two or more processors or cores try to access or modify the same shared data at the same time, leading to inconsistent or unpredictable outcomes. A deadlock happens when two or more processors or cores wait for each other to release a resource that they need, resulting in a circular dependency that prevents them from progressing. To avoid race conditions and deadlocks, you should use synchronization mechanisms, such as locks, semaphores, barriers, or atomic operations, to coordinate and control the access and modification of the shared data.

5 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?