Dave Ackley challenges conventional software engineering by advocating for a balance between correctness and efficiency. The episode explores robustness in computing, prioritizing resilient solutions over peak performance. Examples range from RAID to Byzantine fault tolerance, highlighting the importance of handling unpredictable situations in software development.
Efficiency and robustness trade-offs challenge standard computing models.
Robustness in distributed systems mitigates potential failure points for reliability.
Exploring fault-tolerant systems embraces inherent unreliability for adaptability.
Balancing robustness with efficiency is crucial in everyday coding scenarios.
Deep dives
Trade-off Between Efficiency and Robustness in Software Design
Efficiency and robustness often present a trade-off in software development, where optimizing for one may compromise the other. This trade-off can be seen in examples like implementing lazy evaluation in Haskell to enforce a level of determinism or consistently parsing code frames for efficient syntax highlighting. While efficiency is essential, sacrificing robustness can lead to bugs that may be challenging to track down and fix.
Distributed Computing and Robustness
Robustness in distributed computing systems is crucial for handling errors that can arise from various sources such as network failures, hardware faults, or software bugs. Designs like multiple control computers in space shuttles or redundancy strategies in quorum-based computing exhibit how robustness is integrated into systems beyond just correctness and efficiency. The necessity for redundancy in distributed systems reflects the need to mitigate potential failure points for a reliable operation.
Forcing Robustness Through Insufficient Hardware Reliability
Challenging the standard model of computing that assumes deterministic correctness, there is an interest in exploring hardware and software systems that accommodate inherent unreliability. From creating virtual machines with built-in faults to designing programs that account for random errors, embracing a robust-first approach can lead to more adaptable and fault-tolerant systems that react effectively to unexpected situations.
Practical Examples of Robustness Trade-offs
Practical instances of considering robustness trade-offs can be found in everyday coding scenarios such as optimizing game performance by introducing varying levels of redundancy or re-evaluating data parsing strategies for accurate and responsive outputs. These examples underscore the significance of balancing robustness with accuracy and efficiency to ensure reliable and stable software solutions.
Utilizing Gradient Descent for Approximation
Using gradient descent allows for approximating solutions in cases where closed-form calculations are not feasible. It enables finding solutions close enough to local minima of error, offering practicality over exact correctness.
Trade-offs Between Efficiency and Robustness
The discussion explores examples like bubble sort encountering failure cases that lead to inefficient or non-terminating outcomes in the quest for robustness. Different sorting algorithms exhibit varying robustness levels when faced with edge cases, highlighting the dynamic between efficiency and robustness.
Ideological Perspectives on Computing Paradigms
The paper delves into a philosophical stance emphasizing sustainability, robustness, correctness, and efficiency in computing paradigms. It challenges traditional viewpoints and advocates for prioritizing sustainability and robustness, suggesting a shift in the current computing landscape for better outcomes.
Dave Ackley's paper Beyond Efficiency is three pages long. With just these three pages, he mounts a compelling argument against the conventional way we engineer software. Instead of inflexibly insisting upon correctness, maybe allow a lil slop? Instead of chasing peak performance with cache and clever tricks, maybe measure many times before you cut. So in this episode, we're putting every CEO in the guillotine… (oh, that stands for "correctness and efficiency only", don't put us on a list)… and considering when, where, and how to do the robust thing.
Links
$ patreon.com/futureofcoding — The most recent bonus episode is a discussion with Stefan Lesser about new "laws of physics" we can invent inside the computer.
Don't destroy the earth, then make sure your thing can't be destroyed, then don't destroy your data, and finally, do your damn job, AAAAAAAAAAAAAAAAAAAAAAAAAAA.
Jepsen is a rough, rugged, deeply thoughtful and fantastically cool approach to distributed systems testing, by Kyle Kingsbury. Also, we didn't talk about it, but his reversing / hexing / typing / rewriting / unifying technical interview series is essential reading.
Datamosh is a creative misuse of the lack of robustness that comes from storing diffs instead of full state snapshots. Here's a lovely gallery of examples.
"It is difficult", says Upton Sinclair when asked about Jimmy Miller being Jimmy Miller, and how we all ought to approach our own sense of Jimmy Miller.