What is parallelism in algorithm design?

Thread-level parallelism: Thread-level parallelism involves executing multiple threads simultaneously by dividing the workload among multiple processing units or cores. This can be achieved through multi-threading, where each thread performs a specific task or functions concurrently.

Parallelism in algorithm design enhances computational efficiency by simultaneous processing, primarily using two types: data parallelism and task parallelism. Data parallelism involves executing the same operation on different data segments concurrently. An example is element-wise addition in large arrays, where each operation is independent and co-occurs across array elements. This method optimizes tasks that can be uniformly distributed across data sets. Task parallelism, however, focuses on performing different operations on separate data segments in parallel. It's like sorting one part of a list while searching another part simultaneously. Each task operates independently, maximizing performance in diverse operations.

Designing parallel algorithms involves intricate challenges. The primary task is to break down the problem and data into independent subproblems and subsets that can be processed simultaneously, ensuring they don’t interfere with each other. Balancing the workload across all processors or threads is crucial to avoid idle or overburdened resources. This requires meticulous planning and distribution of tasks. Moreover, synchronization and coordination are vital for accurate and consistent outcomes. Processors or threads must communicate effectively, exchanging data without causing conflicts or inconsistencies. This is essential to maintain the integrity of the results.

Effective parallel algorithm design hinges on adhering to fundamental principles. Firstly, identify parallelism sources and degrees in your problem and data. Assessing the nature and scope of parallelism guides the choice of parallelism type and level, ensuring compatibility with your hardware. Minimizing communication and synchronization between processors or threads is crucial. Reducing the frequency and volume of data exchanges enhances efficiency, as excessive communication can offset the benefits of parallel processing. Performance and scalability optimization is essential. Successful parallel algorithm design involves a strategic approach to identifying parallelism and minimizing inter-processor communication.

When performing parallelism in data or task flow, we need to consider 1. State mutability - algorithm should never overlap in reading/updating the data state (if it does then the resultant will lead to data inconsistencies that can be hard to debug and track) 2. State isolation, boundaries and locks - each parallel data task should execute independently ensuring data consistency on reads and atomicity (establish locks on updates so there will be no race conditions) 3. Fault tolerance - algorithm should be able to fail gracefully rolling back to the prior state on error/exception in the parallel executing data or tasks