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Large supercomputers such as IBM's Blue Gene/P are designed to heavily exploit parallelism. Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. [ 1 ] Large problems can often be divided into smaller ones, which can then be solved at the same time.
Concurrent computing. Concurrent computing is a form of computing in which several computations are executed concurrently —during overlapping time periods—instead of sequentially— with one completing before the next starts. This is a property of a system—whether a program, computer, or a network —where there is a separate execution ...
Distributed computing. Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. [1][2] The components of a distributed system communicate and coordinate their actions by passing messages to one another in ...
Concurrency (computer science) In computer science, concurrency is the ability of different parts or units of a program, algorithm, or problem to be executed out-of-order or in partial order, without affecting the outcome. This allows for parallel execution of the concurrent units, which can significantly improve overall speed of the execution ...
Parallel algorithm. In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine.
Analysis of parallel algorithms. In computer science, the analysis of parallel algorithms is the process of finding the computational complexity of algorithms executed in parallel – the amount of time, storage, or other resources needed to execute them. In many respects, analysis of parallel algorithms is similar to the analysis of sequential ...
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as ...
Amdahl's law. The theoretical speedup of the latency (via a reduction of latency, ie: latency as a metric is elapsed time between an input and output in a system) of the execution of a program as a function of the number of processors executing it, according to Amdahl's law. The speedup is limited by the serial part of the program.