Task parallelism
This form of parallel computing means that the tasks are broken down into smaller tasks — or subtasks — and then allocated to multiple processors, which execute those components at the same time, using the same information source..
There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism..
Instruction-level Parallelism (ILP) is a family of processor and compiler design techniques that speed up execution by causing individual machine operations, such as memory loads and stores, integer additions and floating point multiplications, to execute in parallel..
Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program.
More specifically ILP refers to the average number of instructions run per step of this parallel execution..
The term Parallelism refers to techniques to make programs faster by performing several computations at the same time.
This requires hardware with multiple processing units.
In many cases the sub-computations are of the same structure, but this is not necessary.
Graphic computations on a GPU are parallelism..
A “parallelizing compiler” is typically a compiler that finds parallelism in a se- quential program and generates appro- priate code for a parallel computer..
Some applications for parallel processing include computational astrophysics, geoprocessing, financial risk management, video color correction and medical imaging..
Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task..
A parallel processing system can process data simultaneously to complete tasks more quickly.
For instance, the system could receive the next instruction from memory as the current instruction is processed by the CPU's arithmetic-logic unit (ALU)..
In parallel programming, tasks are parallelized so that they can be run at the same time by using multiple computers or multiple cores within a CPU.
Parallel programming is critical for large scale projects in which speed and accuracy are needed..
If a compiled function running in parallel has several threads trying to call ordinary Wolfram Language code, only one thread can do this at any time.
Listable compiled functions can execute in parallel:.
Parallelism can be implemented by using parallel computers, i.e. a computer with many processors.
Parallel computers require parallel algorithm, programming languages, compilers and operating system that support multitasking.
In this tutorial, we will discuss only about parallel algorithms.
Adding parallel compiler directives manually.
Adding the -Qparallel (Windows*) or -parallel (Linux* or macOS*) option to the compile command is the only action required of the programmer.
However, successful parallelization is subject to certain conditions that are described in the next section.
A parallelizing compiler generally works in two different ways:
