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arxiv: 2604.08938 · v1 · submitted 2026-04-10 · 💻 cs.DS

Speed Thrills: Visceral Demonstrations That Get Students Excited About Efficient Algorithms

Alistair Moffat , David Hawking This is my paper

Pith reviewed 2026-05-10 17:49 UTC · model grok-4.3

classification 💻 cs.DS
keywords algorithm efficiencystudent motivationdata structureslive demonstrationsruntime analysisalgorithmic improvementsthrills of algorithms
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The pith

Live demonstrations of algorithmic improvements that cut runtimes from days or years to seconds convey the value of efficiency to students in a memorable way.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes using sequences of successive improvements to basic algorithms for two simple problems, each producing large drops in execution time that can be shown live on a lecturer's laptop. These sequences, termed thrills of algorithms, turn theoretical complexity discussions into concrete experiences where slow versions take far too long to finish while fast ones complete instantly. The authors supply pseudocode and analyses so instructors can replicate the demos in data structures and algorithms classes. The goal is to create an impact that helps students retain why efficient algorithm design matters in practice.

Core claim

Sequences of algorithmic improvements for two simple problems produce spectacular runtime reductions that can be demonstrated live, turning estimates of days or years into a few seconds and thereby conveying the importance of efficient algorithms in a way unlikely to be forgotten.

What carries the argument

Thrills of algorithms: successive improvements to a basic algorithm for a given problem that yield large, observable decreases in runtime, executed live in class.

If this is right

  • Instructors gain ready-to-use examples with pseudocode and complexity analysis to replace or supplement abstract discussions of big-O notation.
  • Students encounter the practical gap between slow and fast solutions in real time rather than through theoretical estimates alone.
  • The approach can be applied to any problem that admits a series of increasingly efficient solutions with measurable runtime differences.
  • Course materials become more engaging without requiring specialized hardware beyond a standard laptop.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar live demos could be developed for other core topics such as sorting or graph algorithms where progressive improvements are easy to stage.
  • Recording the demos in advance would allow students to review the runtime transitions outside class and compare them to their own implementations.
  • The method might extend to online courses if the runtime measurements are presented in short video segments with on-screen timers.
  • Instructors could collect informal feedback immediately after the demo to gauge immediate student reactions before relying on long-term retention.

Load-bearing premise

Performing these live runtime demonstrations will produce a lasting positive effect on student motivation and understanding.

What would settle it

A controlled comparison of students who saw the live demos versus those taught only through standard lectures and analysis, measuring retention of the practical importance of efficiency six months later.

Figures

Figures reproduced from arXiv: 2604.08938 by Alistair Moffat, David Hawking.

Figure 1
Figure 1. Figure 1: One of the authors reaches the visceral “tadaa” moment in a lecture when a “flight from Melbourne to Shanghai” (Repeats Method 1, 𝑛 = 107 , 𝑚 = 100, extrapolated to take nine hours based on an execution of 𝑛 = 106 needing 320 seconds, see Section 3) is accomplished in just three seconds using a better algorithm (Repeats Method 2). you think this new program will be able to solve an 𝑛 = 10,000,000 instance … view at source ↗
Figure 2
Figure 2. Figure 2: Number of primes less than 1012 (one trillion) computed using Method 5 and segments of size 2 23, using less than 10 MiB of memory, and requiring around 30 minutes of computation on a 2024 MacBook Pro M3. 1e+04 1e+05 1e+06 1e+07 1e+08 1e+09 1e+10 1e+11 n 1e-01 1e+00 1e+01 1e+02 time (seconds) primes 0 primes 1 primes 2 primes 3 primes 4 primes 5 [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Execution times for the prime counting task, six different approaches measured on a 2024 MacBook with an M3 Pro processor and 36 GiB memory. Method 5 was executed with a segmented sieve of size 2 23 ≈ 8 × 106 one-byte entries. for a prime 𝑣 is initially 𝑣 2 (step 5), as noted in connection with Algorithm 4. To prevent overhead costs from dominating, each segment of the sieve (the constant segsize in Algori… view at source ↗
Figure 5
Figure 5. Figure 5: Execution times for the prime counting task for 𝑛 = 1012 when multiple cores are available and the parallel version of Algorithm 5 is employed. longer experiments, and sought to compute the primes up to one quadrillion, 𝑛 = 1015. But extrapolating from the results shown in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: shows the output of an implementation of Method 3, cor￾responding to the rightmost purple “x” in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

We address the problem of motivating students in Data Structures and Algorithms courses by presenting two simple problems that each have a series of improvements to a basic algorithm, leading to spectacular decreases in runtimes. Coining a new term, we refer to such sequences as being "thrills of algorithms". Seeing runtimes drop from an estimate of days (or even years) to just a few seconds has a visceral impact which conveys the importance of efficient algorithms in a way unlikely to be forgotten. The demonstrations are particularly compelling because they can be performed live in class on the lecturer's laptop. To assist staff teaching such courses we provide detailed pseudocode descriptions and complexity analyses for the various methods, and can supply implementations on request.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript describes two sequences of algorithmic improvements for simple problems that achieve dramatic runtime reductions, from estimated days or years down to seconds. It coins the term 'thrills of algorithms' for such sequences and provides pseudocode, complexity analyses, and notes that these can be demonstrated live on a laptop to motivate students in DSA courses by conveying the importance of efficiency viscerally and memorably. Implementations are available upon request.

Significance. If the demonstrations do produce the claimed lasting motivational effect, the paper would offer a valuable teaching resource for illustrating the practical benefits of efficient algorithms. The algorithmic descriptions and analyses are standard and correct, providing clear material for instructors. However, the absence of any evaluation of the student impact limits the significance, as the core assertion remains unverified.

major comments (1)
  1. Abstract: The assertion that 'Seeing runtimes drop from an estimate of days (or even years) to just a few seconds has a visceral impact which conveys the importance of efficient algorithms in a way unlikely to be forgotten' is presented as a central benefit but is unsupported by any classroom data, surveys, pre/post assessments, or even anecdotal observations. This claim is load-bearing for the paper's contribution to student motivation.
minor comments (2)
  1. The abstract could more explicitly name the two simple problems to give readers a clearer sense of the content.
  2. Consider adding a brief discussion of potential limitations or variations in how these demonstrations might be received by different student cohorts.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and address the major comment below.

read point-by-point responses

  1. Referee: Abstract: The assertion that 'Seeing runtimes drop from an estimate of days (or even years) to just a few seconds has a visceral impact which conveys the importance of efficient algorithms in a way unlikely to be forgotten' is presented as a central benefit but is unsupported by any classroom data, surveys, pre/post assessments, or even anecdotal observations. This claim is load-bearing for the paper's contribution to student motivation.

    Authors: We agree that the manuscript provides no empirical data, surveys, or assessments to support the motivational claim. The assertion is based solely on the authors' experience using similar live demonstrations in DSA courses, but this is unverified and not documented in the paper. Since no evaluation was performed, we cannot supply supporting evidence. In revision we will qualify the language in the abstract and introduction to present the 'visceral impact' as the intended pedagogical rationale for these demonstrations rather than a demonstrated outcome, and we will add a brief limitations paragraph noting the lack of formal student-impact evaluation. This is a partial revision that preserves the core contribution of the algorithmic examples and materials while addressing the unsupported claim. revision: partial

Circularity Check

0 steps flagged

No circularity: purely descriptive teaching proposal with no derivations or load-bearing chains

full rationale

The manuscript offers pseudocode, complexity analyses, and classroom demonstration ideas for known algorithms (e.g., successive improvements to basic search or sorting routines). No equations, fitted parameters, predictions, or self-citations appear. The central assertion that runtime drops produce a 'visceral' and 'unforgettable' motivational effect is an untested premise rather than a derived result; it does not reduce to any input by construction. The paper is self-contained as a descriptive resource and contains no derivation chain to inspect.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces no free parameters, mathematical axioms, or invented entities. It relies entirely on standard algorithmic knowledge and proposes pedagogical applications of known methods.

pith-pipeline@v0.9.0 · 5412 in / 978 out tokens · 28180 ms · 2026-05-10T17:49:19.784983+00:00 · methodology

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Reference graph

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