Multi-threading
Julia’s multi-threading provides the ability to schedule Tasks simultaneously on more than one thread or CPU core, sharing memory. This is usually the easiest way to get parallelism on one’s PC or on a single large multi-core server. Julia’s multi-threading is composable. When one multi-threaded function calls another multi-threaded function, Julia will schedule all the threads globally on available resources, without oversubscribing.
Below is the Julia program to implement parallelism using multi-threading:
Julia
# Julia program to implement parallelism # using multi-threading using Base.Threads function my_thread(id::Int) println("hello from thread $id") end for i in 1:4 @spawn my_thread(i) end |
Output
In this example, we create a function my_thread that takes an integer id as an argument and prints a message to the console. We then create four threads using the Thread constructor and schedule them to run asynchronously using the schedule function.
Julia – Concept of Parallelism
Julia is a high-performance programming language designed for numerical and scientific computing. It is designed to be easy to use, yet powerful enough to solve complex problems efficiently. One of the key features of Julia is its support for parallelism, which allows you to take advantage of multiple CPU cores and distributed computing clusters to speed up your computations and scale your applications.
In Julia, parallelism is achieved through a variety of mechanisms, including multi-threading, distributed computing, and GPU computing which are explained below in detail.