Multiprocessing

As explained in Learn Concurrent Programming with Go

An operating-system capability in which multiple tasks are managed so that more than one can make progress on available processor resources.

Multiprocessing lets an operating system manage multiple tasks so more than one can make progress on available processor resources. When one task waits for input or file I/O, the system can run another task and keep the CPU productive. section 2.1

A task can move through several operating-system states:

• Enter the job queue after submission when it is not yet ready to run. section 2.1
• Move to the ready queue, then run when the CPU becomes available. section 2.1
• Enter an I/O waiting queue when it requests file input, allowing another ready task to use the CPU. section 2.1
• Resume after the I/O operation returns data, or terminate when execution finishes. section 2.1

A concurrent command pipeline

The command `curl -s https://www.rfc-editor.org/rfc/rfc1122.txt | wc` runs `curl` and `wc` as separate concurrent processes. A pipe buffers curl's output for wc's input: curl blocks when the buffer is full, while wc blocks when it is empty. After curl finishes, an end-of-data marker allows wc to terminate. section 2.2.3

Figure 2.4 Curl and wc running concurrently using a pipe

Use the process-and-pipe diagram to trace how separate commands exchange data while running concurrently. section 2.2.3

How Learn Concurrent Programming with Go covers it

How deep it goes

Explains job, ready, running, and I/O waiting states, then demonstrates process coordination through a buffered pipe.

Examples

Uses a large-file string search and a curl-to-wc UNIX pipeline to illustrate scheduling, process IDs, buffering, and termination.

What it assumes

Assumes basic command execution and introduces job states, process coordination, and pipe behavior through concrete UNIX examples.

What you can do after

Evaluate/understand how operating systems schedule tasks and coordinate command-line processes through pipes.

How it connects

Connects operating systems, CPU scheduling, processes, concurrency, interprocess communication, and pipes.

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Also explained in

• 

  liveBookmanning.com
  Python Concurrency with asyncioMultiprocessing is a concurrency technique in which multiple processes perform work concurrently, typically with a parent process managing child processes.
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  liveBookmanning.com
  Fast Python: High performance techniques for large datasets

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Related concepts

• global interpreter lockcontrasts with · The global interpreter lock is a mutual-exclusion mechanism in CPython that permits only one thread at a time to execute Python bytecode within a process.
• parallelismkind of · Parallelism is the concurrent execution of multiple computational operations, often across multiple processing resources.
• concurrent.futurescontrasts with · concurrent.futures is a Python standard-library module providing high-level interfaces for asynchronously executing and coordinating callable tasks with threads or processes.
• operating systemused by · An operating system is system software that manages computer hardware and provides services and interfaces for application programs.
• process poolhas part · A process pool is a managed collection of worker processes that executes submitted tasks concurrently.
• compute-bounduses · A computation is compute-bound when its performance is limited primarily by available processor or arithmetic capacity rather than memory transfer, input/output, or other resources.

This explainer was generated by Manning's knowledge graph from the text of Learn Concurrent Programming with Go. The section links point to the parts of the book it draws on.