Compilers loop parallelization

How do you parallelize a loop?
DISTRIBUTED loop
When a loop has a loop-carried dependence, one way to parallelize it is to distribute the loop into several different loops.
Statements that are not dependent on each other are separated so that these distributed loops can be executed in parallel..
How does parallelization work?
Parallelization is the technique of dividing a large computational task into smaller sub-tasks that can be executed concurrently on multiple processors or cores, with the goal of reducing overall computation time..
What is parallelization of a for loop?
To parallelize the loop the multiprocessing package provides a process pool with helpful functions to automatically manage a pool of worker processes.
By default, the created Pool class instance uses all available CPU cores.
The parallel version of the built-in map() function takes a single argument..
What is parallelizing compilers?
A “parallelizing compiler” is typically a compiler that finds parallelism in a se- quential program and generates appro- priate code for a parallel computer..
When can a loop be parallelized?
When a loop has a loop-carried dependence, one way to parallelize it is to distribute the loop into several different loops.
Statements that are not dependent on each other are separated so that these distributed loops can be executed in parallel..
Which loops can be run in parallel?
Doacross Loops
A Doacross loop is a parallel loop, where individual iterations are scheduled to the processors in a round-robin fashion, either dynamically or statically.
The iterations are started in the same order as in a sequential loop and then execute overlappingly in a pipelined fashion..
Types of Parallel Processing
Single Instruction, Single Data (SISD) Multiple Instruction, Single Data (MISD) Single Instruction, Multiple Data (SIMD) Multiple Instruction, Multiple Data (MIMD) Single Program, Multiple Data (SPMD) Massively Parallel Processing (MPP)
Compiler techniques for exploiting parallelism and locality
To exploit parallelism and locality in a compiler, you should consider the following: Using affine transform theory to partition data.
Using affine transform theory to partition loops.
Using affine transform theory to partition the iteration space.
Parallel programs can divide complex problems down into smaller tasks and process these individual tasks simultaneously.
By separating larger computational problems into smaller tasks and processing them at the same time, parallel processing allows computers to run faster.
With parallel programming, a developer writes code with specialized software to make it easy for them to run their program across on multiple nodes or processors.
A simple example of where parallel programming could be used to speed up processing is recoloring an image.

The compiler's dependency analysis transforms a DO loop into a parallelizable task. The compiler may restructure the loop to split out unparallelizable sections that will run serially. It then distributes the work evenly over the available processors. Each processor executes a different chunk of iterations.

Definition. Speculative loop (thread) level parallelization is a compiler run-time technique that executes optimistically parallelized loops, verifies the correctness of their execution and, when necessary, backtracks to a safe state for possible re-execution.

Parallelizing (or multithreading) an application compiles the program to run on a multiprocessor system or in a multithreaded environment. Parallelization enables a single task, such as a DO loop, to run over multiple processors (or threads) with a potentially significant execution speedup.

The Fortran compilers focus on DO loops as the primary language element supporting parallelism. Parallelization distributes the computational work of a loop over several processors without requiring modifications to the Fortran source program.

Under automatic parallelization, the compilers start their loop analysis from the outermost loop in a nest and work inward until a parallelizable loop is found.

Compiler optimization for conditionals in loops

Loop unswitching is a compiler optimization.
It moves a conditional inside a loop outside of it by duplicating the loop's body, and placing a version of it inside each of the if and else clauses of the conditional.
This can improve the parallelization of the loop.
Since modern processors can operate quickly on vectors, this improvement increases the speed of the program.

Compilers loop parallelization

How do you parallelize a loop?

How does parallelization work?

What is parallelization of a for loop?

What is parallelizing compilers?

When can a loop be parallelized?

Which loops can be run in parallel?

Types of Parallel Processing