Parallelization: Making the most of your Machine - Revision history

Ajshen at 04:30, 26 June 2026

2026-06-26T04:30:40Z

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	[[Category:Beginner's_Guide_Scaling\|01]]		[[Category:Beginner's_Guide_Scaling\|01]]

	{{~~ImplementationSidebar~~}}		{{ScalingSidebar}}

	By default, most programming languages like Python and R execute code sequentially using a single CPU core. For small workloads, this is often sufficient. However, as your statistical research scales—whether you are running massive bootstrap simulations, tuning hyperparameters via cross-validation, or generating large spatial field realizations—sequential processing becomes a severe bottleneck.		By default, most programming languages like Python and R execute code sequentially using a single CPU core. For small workloads, this is often sufficient. However, as your statistical research scales—whether you are running massive bootstrap simulations, tuning hyperparameters via cross-validation, or generating large spatial field realizations—sequential processing becomes a severe bottleneck.

Ajshen at 04:28, 26 June 2026

2026-06-26T04:28:42Z

← Older revision		Revision as of 04:28, 26 June 2026
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	[[Category:Beginner'~~s_Guide_Implementation~~\|04]]		[[Category:Beginner's_Guide_Scaling\|01]]

	{{ImplementationSidebar}}		{{ImplementationSidebar}}

Ajshen: /* 2. Language-Specific Implementations */

2026-06-26T04:25:39Z

2. Language-Specific Implementations

← Older revision		Revision as of 04:25, 26 June 2026
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	<div style="background-color: #f8f9fa; border-left: 4px solid #75AADB; padding: 15px; margin-bottom: 10px;">		<div style="background-color: #f8f9fa; border-left: 4px solid #75AADB; padding: 15px; margin-bottom: 10px;">
	'''[[Parallelization in r \| Parallelization in R]]'''<br>		'''[[Parallelization in r \| Parallelization in R]]'''<br>
	R offers several excellent ways to scale up. This guide covers the built-in <code>parallel</code> package, the <code>foreach</code> paradigm, and the modern ~~<code>future</code>/<code>furrr</code> ecosystem~~ for seamless parallel mapping over lists and data frames.		R offers several excellent ways to scale up. This guide covers the built-in <code>parallel</code> package, the <code>foreach</code> paradigm, and the modern ecosystems for seamless parallel mapping over lists and data frames.
	</div>		</div>

Ajshen at 04:24, 26 June 2026

2026-06-26T04:24:49Z

← Older revision		Revision as of 04:24, 26 June 2026
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	By default, most programming languages like Python and R will only use one CPU core/thread to execute your code. For small workloads, this is often sufficient even on a personal device. However, larger data processing or analysis workflows will often taken far too long to run on a single core or thread.		[[Category:Beginner's_Guide_Implementation\|04]]

	'''Parallelization''' is the process of taking a large computational task and breaking it into smaller tasks that can all be run '''simultaneously''' and '''independently''.''''' For example, most bootstrap methods can be parallelized, since each bootstrap sample is processed independently. This page will introduce several tools and techniques for running parallel workflows.		{{ImplementationSidebar}}

	~~Back~~ to ~~[[Beginner's Guide~~ to ~~Computation in Statistical Research]]~~		By default, most programming languages like Python and R execute code sequentially using a single CPU core. For small workloads, this is often sufficient. However, as your statistical research scales—whether you are running massive bootstrap simulations, tuning hyperparameters via cross-validation, or generating large spatial field realizations—sequential processing becomes a severe bottleneck.
	~~{\| class~~="~~wikitable~~"
	\|+		'''Parallelization''' is the process of breaking a large computational workload into smaller, independent tasks that can be executed '''simultaneously'''. This page serves as a directory of parallelization tools and techniques. You don't need to read every link right now; simply familiarize yourself with the concepts so you know what tools to reach for when your code starts taking hours (or days) to run.
	~~!Title~~
	!~~Summary~~		<div style="background-color: #fff3cd; border-left: 5px solid #ffc107; padding: 15px; margin-bottom: 20px; margin-top: 15px; overflow: hidden;">
	~~!Technical Level~~		'''Important: The Overhead Cost'''<br>
	~~!Relevance~~		Parallelization is not a magic bullet. Spawning new threads or processes introduces computational overhead. If your individual tasks take only a fraction of a second to run, the time spent setting up the parallel workers might actually make your code run ''slower''. Always benchmark your code before and after parallelizing!
	\|-		</div>
	~~\|[[Parallel computing]]~~
	~~\|A primer on~~ the ~~benefits~~ of parallel computing		== 1. Foundational Concepts & Small-Scale Parallelization ==
	~~\|⭐⚫⚫⚫⚫~~		These topics cover the basics of parallel computing and how to manage multiple independent tasks on a single machine or remote server.
	\| style="background-color: #~~44ee88~~;" \|'''~~Almost all~~ computing ~~workflows~~'''
	\|-		<div style="background-color: #f8f9fa; border-left: 4px solid #6c757d; padding: 15px; margin-bottom: 10px;">
	\|[[Terminal multiplexers]]		'''[[Parallel computing \| A Primer on Parallel Computing]]'''<br>
	~~\|Use~~ multiple ~~terminal~~ "windows" to run ~~several workflows simultaneously~~		Start here if you are completely new to parallelization. This guide covers the fundamental concepts, the difference between multithreading and multiprocessing, and how to identify "embarrassingly parallel" tasks in your research.
	~~\|⭐⭐⚫⚫⚫~~		</div>
	\|style="background-color: #~~44ee88~~;" ~~\|'''Small-scale server workflows~~'''
	\|-		<div style="background-color: #f8f9fa; border-left: 4px solid #17a2b8; padding: 15px; margin-bottom: 10px;">
	\|[[Parallelization in python]]		'''[[Terminal multiplexers \| Terminal Multiplexers (tmux / screen)]]'''<br>
	~~\|Introduces several python packages~~ for ~~parallel~~ workflows		If you are running long scripts on a remote server, a dropped SSH connection will kill your job. Terminal multiplexers allow you to open multiple "windows" on a server, start your scripts, and safely detach. Your code keeps running independently even after you close your laptop.
	~~\|⭐⭐⭐⚫⚫~~		</div>
	~~\|Python workflows~~
	\|-		== 2. Language-Specific Implementations ==
	\|[[Parallelization in r]]		Once you understand the concepts, you need to implement them in your language of choice. These guides cover the specific packages required to run parallel loops and apply-functions.
	~~\|Introduces~~ several ~~R packages~~ for parallel ~~workflows~~
	~~\|⭐⭐⭐⚫⚫~~		<div style="background-color: #f8f9fa; border-left: 4px solid #005A9C; padding: 15px; margin-bottom: 10px;">
	~~\|R workflows~~		'''[[Parallelization in python \| Parallelization in Python]]'''<br>
	\|-		Python's Global Interpreter Lock (GIL) makes parallelization slightly more complex. This page introduces the built-in <code>multiprocessing</code> library, as well as higher-level tools like <code>joblib</code> which are excellent for parallelizing iterations in data processing workflows.
	\|[[Slurm#Using SLURM to parallelize workflows\|Using ~~SLURM~~ to ~~parallelize workflows~~]]		</div>
	~~\|How~~ to use ~~the SLURM job scheduler~~ to ~~parallelize workflows~~
	~~\|⭐⭐⭐⭐⚫~~		<div style="background-color: #f8f9fa; border-left: 4px solid #75AADB; padding: 15px; margin-bottom: 10px;">
	~~\|Large-scale server workflows~~		'''[[Parallelization in r \| Parallelization in R]]'''<br>
	\|}		R offers several excellent ways to scale up. This guide covers the built-in <code>parallel</code> package, the <code>foreach</code> paradigm, and the modern <code>future</code>/<code>furrr</code> ecosystem for seamless parallel mapping over lists and data frames.
			</div>

			== 3. Large-Scale Cluster Computing ==
			When a single machine—even a powerful compute node—is no longer enough, you must distribute your work across an entire cluster.

			<div style="background-color: #f8f9fa; border-left: 4px solid #dc3545; padding: 15px; margin-bottom: 10px;">
			'''[[Slurm#Using SLURM to parallelize workflows \| Using Slurm to Parallelize Workflows]]'''<br>
			For massive computational workloads, you will need to utilize the Slurm workload manager. This page explains how to request multiple CPUs, and crucially, how to use '''Slurm Job Arrays''' to launch hundreds of independent simulation replicates with a single submission script.
			</div>

Ajshen at 06:01, 27 March 2026

2026-03-27T06:01:34Z

← Older revision		Revision as of 06:01, 27 March 2026
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	\|Use multiple terminal "windows" to run several workflows simultaneously		\|Use multiple terminal "windows" to run several workflows simultaneously
	\|⭐⭐⚫⚫⚫		\|⭐⭐⚫⚫⚫
	\|'''Small-scale server workflows'''		\|style="background-color: #44ee88;" \|'''Small-scale server workflows'''
	\|-		\|-
	\|[[Parallelization in python]]		\|[[Parallelization in python]]

Ajshen at 06:01, 27 March 2026

2026-03-27T06:01:24Z

← Older revision		Revision as of 06:01, 27 March 2026
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	\|Use multiple terminal "windows" to run several workflows simultaneously		\|Use multiple terminal "windows" to run several workflows simultaneously
	\|⭐⭐⚫⚫⚫		\|⭐⭐⚫⚫⚫
	\|Small-scale server workflows		\|'''Small-scale server workflows'''
	\|-		\|-
	\|[[Parallelization in python]]		\|[[Parallelization in python]]

Ajshen at 05:35, 27 March 2026

2026-03-27T05:35:34Z

← Older revision		Revision as of 05:35, 27 March 2026
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	\|A primer on the benefits of parallel computing		\|A primer on the benefits of parallel computing
	\|⭐⚫⚫⚫⚫		\|⭐⚫⚫⚫⚫
	\| style="background-color: #44ee88;" \|'''Almost all workflows'''		\| style="background-color: #44ee88;" \|'''Almost all computing workflows'''
	\|-		\|-
	\|[[Terminal multiplexers]]		\|[[Terminal multiplexers]]

Ajshen at 05:35, 27 March 2026

2026-03-27T05:35:02Z

← Older revision		Revision as of 05:35, 27 March 2026
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	\|A primer on the benefits of parallel computing		\|A primer on the benefits of parallel computing
	\|⭐⚫⚫⚫⚫		\|⭐⚫⚫⚫⚫
	\| style="background-color: #44ee88;" \|'''Almost all ~~computing tasks~~'''		\| style="background-color: #44ee88;" \|'''Almost all workflows'''
	\|-		\|-
	\|[[Terminal multiplexers]]		\|[[Terminal multiplexers]]

Ajshen at 05:34, 27 March 2026

2026-03-27T05:34:38Z

← Older revision		Revision as of 05:34, 27 March 2026
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	\|Use multiple terminal "windows" to run several workflows simultaneously		\|Use multiple terminal "windows" to run several workflows simultaneously
	\|⭐⭐⚫⚫⚫		\|⭐⭐⚫⚫⚫
	\|Small scale ~~parallelization~~		\|Small-scale server workflows
	\|-		\|-
	\|[[Parallelization in python]]		\|[[Parallelization in python]]
	\|Introduces several python packages for parallel workflows		\|Introduces several python packages for parallel workflows
	\|⭐⭐⭐⚫⚫		\|⭐⭐⭐⚫⚫
	\|Python workflows ~~only~~		\|Python workflows
	\|-		\|-
	\|[[Parallelization in r]]		\|[[Parallelization in r]]
	\|Introduces several R packages for parallel workflows		\|Introduces several R packages for parallel workflows
	\|⭐⭐⭐⚫⚫		\|⭐⭐⭐⚫⚫
	\|R workflows ~~only~~		\|R workflows
	\|-		\|-
	\|[[Slurm#Using SLURM to parallelize workflows\|Using SLURM to parallelize workflows]]		\|[[Slurm#Using SLURM to parallelize workflows\|Using SLURM to parallelize workflows]]
	\|How to use the SLURM job scheduler to parallelize workflows		\|How to use the SLURM job scheduler to parallelize workflows
	\|⭐⭐⭐⭐⚫		\|⭐⭐⭐⭐⚫
	\|~~HPC~~ workflows ~~with SLURM scheduling~~		\|Large-scale server workflows
	\|}		\|}

Ajshen at 05:32, 27 March 2026

2026-03-27T05:32:23Z

← Older revision		Revision as of 05:32, 27 March 2026
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	\|A primer on the benefits of parallel computing		\|A primer on the benefits of parallel computing
	\|⭐⚫⚫⚫⚫		\|⭐⚫⚫⚫⚫
	\| style="background-color: #44ee88;" \|Almost all computing tasks		\| style="background-color: #44ee88;" \|'''Almost all computing tasks'''
	\|-		\|-
	\|[[Terminal multiplexers]]		\|[[Terminal multiplexers]]