How do you choose data structures for parallel algorithms?

1 Parallelism and data structures

Parallelism is the ability to execute multiple tasks or operations simultaneously, using multiple processors, cores, or threads. Parallel algorithms are designed to exploit parallelism and accelerate computations, particularly for large and complicated problems. Nevertheless, parallelism can also present challenges such as synchronization, communication, load balancing, and concurrency control. Data structures can either aid or hinder parallel algorithms depending on how they facilitate or limit parallelism. When selecting data structures for parallel algorithms, some factors to consider are the degree of parallelism (how many tasks or operations can be performed in parallel and how independent or dependent they are), the access pattern (how often and how randomly the data is read or written and how much contention or conflict there is among parallel tasks or operations), the communication cost (how much data needs to be transferred or shared among parallel tasks or operations and how efficient or expensive the communication mechanism is), and the memory usage (how much space the data structure occupies and how well it fits the memory hierarchy of the system).

2 Sequential vs. parallel data structures

A sequential data structure is one that is designed for sequential algorithms, where only one task or operation can access or modify the data at a time. Conversely, a parallel data structure is one that is designed for parallel algorithms, where multiple tasks or operations can access or modify the data concurrently. These data structures often have different properties and trade-offs, such as lock-free or wait-free which may reduce contention and overhead but introduce complexity and overhead in other aspects. Partitioned or distributed data structures can increase parallelism and locality but may also increase communication and coordination costs. Additionally, mutable or immutable data structures offer different levels of flexibility and functionality but may require more synchronization and consistency mechanisms.

3 Common data structures for parallel algorithms

Data structures are commonly used or adapted for parallel algorithms, such as arrays, linked lists, stacks, queues, trees, and graphs. Arrays are simple and efficient for random access and iteration, but may cause contention if multiple tasks access the same elements. Linked lists are flexible and efficient for insertion and deletion, but may cause overhead due to pointer manipulation. Stacks and queues are simple and efficient for adding and removing elements from one end, but may be inefficient for accessing or modifying elements in the middle. Trees and graphs are complex and versatile for representing data and relationships, but may cause overhead due to edge or link manipulation. Each data structure can be used for various parallel algorithms with different access patterns, but can also cause certain issues if not handled properly.

4 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?