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HPC Tradecraft Apprenticeship, Volume 1
What was so unusual and special about Cray Research? We accomplished, repeatedly, what nobody else on the planet was able to match. High-Performance Computing is not merely parallelism and large-scale systems. That is the public-facing decoy version.
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About the Course
Around 1995, we hid the messy details behind abstractions. This freed software development to advance rapidly. But it also hid the constraints that shape high-performance systems. The tradecraft of reasoning about systems as a whole, the skill that built the world's fastest supercomputers, became tacit knowledge passed from person to person and never written down.
That knowledge is now retiring out of the workforce.
This book exists to transmit the method of transmission. If you want to teach this material, you need to know how it was taught. If you want to learn it, you need to know what you are committing to.
The book performs what it teaches. Each chapter demonstrates a technique while explaining it. By the end, you will have experienced the formation process that the larger books in this series employ throughout.
You will learn to model your own thinking and articulate it to others. You will learn to spot transcendent patterns, solutions that appear independently across different contexts because similar constraints shape similar solutions. You will see how the attention mechanism in modern AI maps to the same cognitive processes you already use.
None of this requires mathematics. The concepts are separated from their implementations.
This is not an introduction to high-performance computing in the conventional sense. There is no parallel programming tutorial, no cluster architecture overview, no benchmark analysis.
This is the cognitive foundation. The skills that made Cray Research engineers able to accomplish what nobody else on the planet could match. The orientation that treats "it has never been done before" as a starting point rather than a barrier.
If you skim, you will get nothing. The material is designed to be experienced, not surveyed. That is not a sales pitch. It is a structural fact about how the teaching works.
The book serves as qualification for the two large books that follow: Nobody but Us (how Cray Research built the world's fastest supercomputers, from the software-engineering point of view) and The Wizard's Lens (how to observe and characterize AI systems using the same tradecraft). Those books require significant commitment. This one lets you make an informed choice.
If you are a professor considering whether to teach this material, the book shows you exactly how the pedagogy works. If you are a practitioner wondering whether Cold War-era engineering tradition has anything to offer modern AI work, the book demonstrates the connection directly.
Two declassified NSA documents from 1952-1953 show the distinction between labor-saving machines and revolutionizers: computing systems that enable work previously impossible to consider. This is not historical decoration. It is the operational definition of high-performance computing that this series transmits.
The final appendix is the road map for all six books in the two series, showing where this book fits and what lies beyond.
Approximately 20,000 words. Readable in about three hours if you engage fully. The free sample includes the opening chapter and the road map appendix, enough to know whether you belong here.
About the Instructor
What was it like to stand in the breach, with nobody else to take the decisions, and do-overs are too late? Margaret Hamilton, the first programmer hired for the Apollo project at MIT, explained:
Because software was a mystery, a black box, upper management gave us total freedom and trust. We had to find a way and we did. Looking back, we were the luckiest people in the world; there was no choice but to be pioneers; no time to be beginners.
During the Cold War when it was "nobody but us," our decisions and solutions were shaped by constraints. At Cray Research constraints and barriers pointed us to the best point of leverage. To remain the best in the world, we had no other option. But before considering leverage, we carefully identified and proved relevant capabilities. Those capabilities showed us what solutions might be plausible. We also found that if it wasn't fun, it probably was not worth doing.
This forced way of working, where responsibility could not be abstracted away, has been mostly lost to time.
I am bringing you those skills because they were never passed to the next generation. I created a primary source document showing what it was like: Nobody but Us: A History of Cray Research's Software and the Building of the World's Fastest Supercomputer. But I wrote a second primary source, reproducing the Cray Research skills for you right now, in 2026. The Wizard's Lens: Learn to Think Like AI is an apprenticeship drawing you in to experience, not merely read about, how we continuously "achieved the impossible" at Cray Research.
Those Cray Research skills did not begin with software, or even hardware. They began outdoors. Experiential education, with real risks and real consequences, has also been abstracted away. That is where judgement is formed. For this I wrote Surviving Spring Break on the Mountain: The Power of Experiential Education.
If it isn't fun, it probably isn't worth doing. I continued practicing the most important debugging skill I know: spotting patterns and connections that others miss. I wrote Unexpected Histories to show you shifted perspectives, purely for entertainment, but showing real history that matters today. In each case, once you see it, you cannot "un-see" it.
Каково это — стоять на переднем крае, когда больше некому принимать решения и на повторные попытки уже нет времени? Маргарет Хэмилтон, первый программист, нанятый для проекта Apollo в MIT, объясняла это так:
Поскольку программное обеспечение было загадкой, «чёрным ящиком», высшее руководство предоставило нам полную свободу и доверие. Мы должны были найти выход — и мы его нашли. Оглядываясь назад, можно сказать, что мы были самыми везучими людьми в мире: у нас не было выбора, кроме как быть первопроходцами; не было времени на ученичество.
Во времена холодной войны, когда всё сводилось к принципу «никто, кроме нас», наши решения и подходы формировались под давлением жёстких ограничений. В Cray Research именно ограничения и барьеры указывали нам на наиболее эффективную точку приложения усилий. У нас просто не было иного пути, кроме как стать лучшими в мире. Но прежде чем прилагать усилия, мы тщательно искали и проверяли соответствующие компетенции. Именно они показывали, какие решения вообще могут быть осуществимы. Мы также поняли: если дело не приносит удовольствия — вероятно, не стоит им заниматься.
Этот вынужденный стиль работы, при котором ответственность нельзя переложить на других, почти утрачен со временем.
Я передаю вам эти навыки, потому что они так и не были переданы следующему поколению. Я написал книгу воспоминаний о том, как это было на самом деле: Nobody but Us: A History of Cray Research's Software and the Building of the World's Fastest Supercomputer. («Только мы: история программного обеспечения Cray Research и создания самого быстрого суперкомпьютера в мире»). Но я написал и вторую книгу, возрождающую стиль мышления Cray Research для вас прямо сейчас, в 2026 году. The Wizard's Lens: Learn to Think Like AI («Линза волшебника: научитесь думать как ИИ») — это учебник, который погружает вас в атмосферу и дает опыт, а не просто рассказывает о том, как мы постоянно «достигали невозможного» в Cray Research.
Истоки подхода Cray Research лежат не в программном обеспечении и даже не в железе, а в холодной реальности жизни. Обучение через опыт, с реальными рисками и реальными последствиями, подвергнутое переосмыслению. Именно так формируется суждение. Об этом я написал книгу Surviving Spring Break on the Mountain: The Power of Experiential Education («Выжить на весенних каникулах в горах: сила обучения через опыт»).
Если это не приносит удовольствия — вероятно, этим не стоит заниматься. Я продолжал практиковать самый важный навык профессионального отладчика, который знаю: замечать закономерности и связи, которые другие упускают. Я написал Unexpected Histories («Неожиданные истории»), чтобы показать вам смещенные перспективы — исключительно ради развлечения, но опираясь на реальную историю, которая имеет значение и сегодня. В любом случае, увидев это однажды, вы уже не сможете «развидеть» увиденное.
Episode 317
An Interview with Edward W. Barnard
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