What is a parallel data structure, and how does it work?

A parallel data structure is a type of data structure that allows multiple operations to be performed simultaneously.

In more detail, parallel data structures are designed to maximise the efficiency of parallel computing environments. They are a key component in parallel programming, where tasks are divided into subtasks that can be processed simultaneously, often on multiple processors or cores, to improve computational speed and performance.

Parallel data structures work by enabling concurrent access to different parts of the data structure. This is in contrast to sequential data structures, where operations are performed one after the other. In a parallel data structure, multiple threads can read from and write to the data structure at the same time. This concurrent access is managed through various synchronisation techniques to ensure data integrity and prevent race conditions, where the outcome of an operation depends on the sequence or timing of other uncontrollable events.

For example, consider a parallel array, which is a simple type of parallel data structure. In a parallel array, each processor or core has its own local array. When an operation is performed, it is performed on all the arrays simultaneously. This can significantly speed up operations such as searching or sorting, as they can be performed on multiple parts of the data set at the same time.

Another example is a parallel hash table. In a parallel hash table, the table is divided into multiple segments, each of which can be accessed independently. This allows multiple threads to read from and write to the table simultaneously, improving performance.

However, designing and implementing parallel data structures can be complex. It requires careful consideration of factors such as data distribution, load balancing, and synchronisation. Furthermore, not all algorithms can be effectively parallelised. Some problems are inherently sequential and cannot be broken down into independent subtasks. Therefore, while parallel data structures can offer significant performance improvements, they are not a universal solution and their use must be carefully considered.