Data parallelism in the context of OpenCL

👉 Data parallelism in the context of OpenCL

OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. OpenCL provides a standard interface for parallel computing using task- and data-based parallelism.

OpenCL is an open standard maintained by the Khronos Group, a non-profit, open standards organisation. Conformant implementations (passed the Conformance Test Suite) are available from a range of companies including AMD, Arm, Cadence, Google, Imagination, Intel, Nvidia, Qualcomm, Samsung, SPI and Verisilicon.

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Data parallelism in the context of Parallel computing

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

In computer science, parallelism and concurrency are two different things: a parallel program uses multiple CPU cores, each core performing a task independently. On the other hand, concurrency enables a program to deal with multiple tasks even on a single CPU core; the core switches between tasks (i.e. threads) without necessarily completing each one. A program can have both, neither or a combination of parallelism and concurrency characteristics.

View the full Wikipedia page for Parallel computing

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Data parallelism in the context of OpenCL

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👉 Data parallelism in the context of OpenCL

Data parallelism in the context of Parallel computing