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Parallel task scheduling

Parallel task scheduling is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Parallel task scheduling rather than just read about it. In short: Parallel task scheduling (also called parallel job scheduling or parallel processing scheduling) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling.

Key takeaways

  • Parallel task scheduling belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Parallel task scheduling to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Parallel task scheduling from memory before moving on to harder problems.

Reference excerpt

Parallel task scheduling (also called parallel job scheduling or parallel processing scheduling) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. In a general job scheduling problem, we are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on m machines while trying to minimize the makespan - the total length of the schedule (that is, when all the jobs have finished processing). In the specific variant known as parallel-task scheduling, all machines are identical. Each job j has a length parameter pj and a size parameter qj, and it must run for exactly pj time-steps on exactly qj machines in parallel. Veltman et al. and Drozdowski denote this problem by P | s i z e j | C max {\displaystyle P|size_{j}|C_{\max }} in the three-field notation introduced by Graham et al. P means that there are several identical machines running in parallel; sizej means that each job has a size parameter; Cmax means that the goal is to minimize the maximum completion time. Some authors use P | m j | C max {\displaystyle P|m_{j}|C_{\max }} instead. Note that the problem of parallel-machines scheduling is a special case of parallel-task scheduling where s i z e j = 1 {\displaystyle size_{j}=1} for all j, that is, each job should run on a single machine. The origins of this problem formulation can be traced back to 1960. For this problem, there exists no polynomial time approximation algorithm with a ratio smaller than 3 / 2 {\displaystyle 3/2} unless P = N P {\displaystyle P=NP} .

Definition There is a set J {\displaystyle {\mathcal {J}}} of n {\displaystyle n} jobs, and m {\displaystyle m} identical machines. Each job j ∈ J {\displaystyle j\in {\mathcal {J}}} has a processing time p j ∈ N {\displaystyle p_{j}\in \mathbb {N} } (also called the length of j), and requires the simultaneous use of q j ∈ N {\displaystyle q_{j}\in \mathbb {N} } machines during its execution (also called the size or the width of j). A schedule assigns each job j ∈ J {\displaystyle j\in {\mathcal {J}}} to a starting time s j ∈ N 0 {\displaystyle s_{j}\in \mathbb {N} _{0}} and a set m j ⊆ { 1 , … , m } {\displaystyle m_{j}\subseteq \{1,\dots ,m\}} of | m j | = q j {\displaystyle |m_{j}|=q_{j}} machines to be processed on. A schedule is feasible if each processor executes at most one job at any given time. The objective of the problem denoted by P | s i z e j | C max {\displaystyle P|size_{j}|C_{\max }} is to find a schedule with minimum length C max = max j ∈ J ( s j + p j ) {\displaystyle C_{\max }=\max _{j\in {\mathcal {J}}}(s_{j}+p_{j})} , also called the makespan of the schedule. A sufficient condition for the feasibility of a schedule is the following

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Parallel task scheduling

Start with the simplest possible case. Write down what Parallel task scheduling claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Parallel task scheduling before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Parallel task scheduling ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Parallel task scheduling

In research
Parallel task scheduling appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Parallel task scheduling in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Parallel task scheduling is common in secondary-school and first-year university syllabi. It links to neighbouring topics Optimal scheduling, Packing problems, so understanding it makes those chapters shorter.
In everyday life
Look for Parallel task scheduling outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Parallel task scheduling in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Parallel task scheduling means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Parallel task scheduling out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Parallel task scheduling in simple terms?

Parallel task scheduling (also called parallel job scheduling or parallel processing scheduling) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling.

Why does Parallel task scheduling matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Parallel task scheduling?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Parallel task scheduling.

Tags

  • Optimal scheduling
  • Packing problems

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