Parallelizable

Parallelizable is an adjective used to describe a process, algorithm, or problem that can be executed in parallel, i.e., divided into subtasks that can run concurrently on multiple processing units. The defining requirement is that these subtasks either have no dependencies or only limited, well-managed dependencies, so they can proceed simultaneously rather than sequentially. In practice, problems with data parallelism (the same operation applied to many data items) or task parallelism (different tasks performed concurrently) are most easily parallelizable.

Performance of parallelizable workloads depends on how finely they can be decomposed (granularity), the overhead of coordinating subtasks (scheduling, synchronization, communication), and the available hardware such as multicore CPUs, GPUs, or distributed clusters. The theoretical speedup is constrained by serial portions of the work; as more processors are added, the non-parallel parts dominate, yielding diminishing returns. In large systems, scalability also hinges on memory bandwidth, network latency, and contention for shared resources.

Examples of parallelizable problems include vector and matrix operations, image and signal processing with data parallelism, and Monte Carlo simulations. Problems with strong sequential dependencies or tight inter-task communication are poorly parallelizable.

Assessing parallelizability involves dependency graphs and data partitioning strategies, as well as choosing appropriate programming models. Common tools and frameworks include OpenMP and MPI for CPUs, CUDA and OpenCL for accelerators, and distributed data processing frameworks like MapReduce and Apache Spark.