CompuTruthing

In the last post we shown how important data manipulation can be in the efficiency of programs. More specifically, we shown that in some cases, the data access is much slower than the instructions we run on the CPU side. The last post was a show-case for data-oriented design (DOD).

But we can turn the problem upside down, and ask ourselves: assuming data layout is optimal, can we add more CPU instructions to be used while waiting for memory? The answer appears to be yes, within some limits.

This is not a show-case of data-oriented design; rather it’s an exploration inside the realm of DOD, to investigate some finer points of DOD. And, compared to last post, we venture outside of game industry to emphasize the point that DOD techniques can be applied in other industries as well.

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Data-Oriented Design (DOD) is an approach of solving computing problems that require efficiency with an emphasis on the data. It was made popular by Mike Acton with his highly acclaimed talk Data-Oriented Design and C++ at CppCon2014. If you are a programmer and you haven’t seen the video yet, please do yourself a service and watch it (embedded below).

Over the course of time, I started to believe more and more in this approach. But a belief is just a belief, so in accordance with the goals of this blog, I need to move it towards true-belief and then to truth. This post is about adding evidence to support Data-Oriented Design.

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This post is inspired by the blog post Why physicists need philosophy. Richard Healey, the author of the post, rightfully argues against the mainstream belief among top physicists that physics has an answer for everything. He argues that philosophy is still needed to answer some of the aspects that physicists are still unable to answer. Stephen Hawking is so wrong when he considers philosophy to be dead.

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The first time I’ve met a computer I was 10. It wasn’t love at first sight, but very soon the relationship turned into a steady long-lasting one. While other kids of my age were playing games, I was trying to program small games. I’ve been a programmer since then; I’ve got my first programming full-time job while I was still 18 – that was over 15 years ago. I’ve later become a proper software engineer, and then a computer scientist (with a PhD in programming languages).

Over the years, I’ve gathered some knowledge about programming and computer science. At the same time, I’ve come to know much better the things that I don’t know, even in fields in which I’m supposed to be an expert (the so-called Socratic paradox).

This post is about what I know, what I don’t know, and about how to know if I know a subject. And above all, about the process of knowing. Mostly related to computing.

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