parallelize

Parallelize is a term used in computing to describe the process of converting a computation, program, or task to run in parallel, utilizing multiple processing elements to execute work simultaneously. The word is formed from parallel and the suffix -ize, indicating an action that makes something operate in parallel. In software development, parallelization involves dividing work into independent pieces that can be processed concurrently, typically to improve performance on multi-core CPUs, GPUs, or distributed systems.

There are two broad forms of parallelization. Data parallelism applies the same operation to many data items in parallel, such as applying a function to elements of an array simultaneously. Task parallelism assigns different tasks or stages of a computation to run concurrently, which can be beneficial when tasks have different workloads or dependencies. Common strategies include multithreading, multiprocessing, and distributed computing.

Practical approaches use various tools and paradigms. OpenMP and MPI are prominent for parallel programming on multi-core and distributed memory systems, respectively. CUDA and other GPU frameworks enable parallel execution of kernels on graphics processing units. Higher-level frameworks for big data, such as MapReduce and Apache Spark, support data-parallel workloads across clusters.

Parallelization requires identifying independent tasks, managing synchronization, and handling shared state to avoid race conditions, deadlocks, and contention. Overheads from thread or process creation, communication, and synchronization can limit benefits, making granularity and load balancing important considerations. Theoretical limits, such as Amdahl’s law, constrain achievable speedups based on the serial portion of a task. Parallelization is widely used in scientific computing, image processing, machine learning, and data analytics, delivering performance gains when implemented carefully.