Needless to say, this is a fantastic article. Great job and thank you for going so in-depth.
It's interesting that in my time software engineering, I never had to really learn about how GPUs work in-depth. I wish I did. I have a friend working on deep learning over at Nvidia, and he seemingly operates at a different level of technicality than I do. At the same time, I try to remind myself that my expertise and experience is mostly on hyperscale distributed system and what I work on is probably foreign to him.
Regardless, I feel like at least GPU basics should be known knowledge to ambitious software engineers, especially as the world moves forward on GPU-powered computing thanks to AI.
Although I also never had to work with GPUs directly (apart from running deep learning models), there have been few instances in my career where we wondered if we could use GPUs for a problem. But the lack of basic understanding of how they operate made things difficult.
Abhinav, good article! I’m wondering if we can translate your blog into Chinese and post it in Chinese community. We will highlight your name and keep the original link on the top of the translated version. Thank you!
This analogy explains why the flow of time is not constant but varies depending on the local informational environment of the Field.
The Analogy: Think of the universe as a vast, parallel computer processor—the Adelic Computronium. Each region of space is like an individual processing core, and the local "flow of time" is its clock speed. A region of high Gnosis and complexity, like the accretion disk of a GreyHole, is a core running an incredibly demanding, focused calculation. To handle this "computational load," the system dedicates immense resources, and the core's clock appears to slow down relative to the rest of the processor. This is Chromatic Time Dilation. Conversely, a region of high Dissonance, like a vast cosmic void, is like an idle core filled with random, chaotic background static. To quickly clear this "informational noise" and return to an optimal, low-energy state, the system temporarily "overclocks" the core, causing its clock to run faster. This is Gravitational Anti-Time-Dilation.
Your comment is valid. Although the article was written for an audience which may not have much background in parallel computing and the use of Little's law was to provide a better intuition. However, I didn't spend enough words on elaborating on it because that would have taken too much space and diluted other parts of the article. I've removed the mention of Little's law, it was really not needed when explaining how GPUs work.
Needless to say, this is a fantastic article. Great job and thank you for going so in-depth.
It's interesting that in my time software engineering, I never had to really learn about how GPUs work in-depth. I wish I did. I have a friend working on deep learning over at Nvidia, and he seemingly operates at a different level of technicality than I do. At the same time, I try to remind myself that my expertise and experience is mostly on hyperscale distributed system and what I work on is probably foreign to him.
Regardless, I feel like at least GPU basics should be known knowledge to ambitious software engineers, especially as the world moves forward on GPU-powered computing thanks to AI.
Thank you, Leonardo.
Although I also never had to work with GPUs directly (apart from running deep learning models), there have been few instances in my career where we wondered if we could use GPUs for a problem. But the lack of basic understanding of how they operate made things difficult.
Ciao Abhinav, greetings from Italy. I really enjoy and admire your posts. I have written to you via Linkedin, hope that's okay.
Hi Tony, thank you so much. (already connected with you on LinkedIn) :-)
Nice article, refreshed my 2016 memory of CUDA programming.
Keep up the great job 👏
Thanks, Nat :)
Excellent fundamentals about GPU, Great post, thanks a lot
Abhinav, good article! I’m wondering if we can translate your blog into Chinese and post it in Chinese community. We will highlight your name and keep the original link on the top of the translated version. Thank you!
Time: A Dynamic Computer Processor
This analogy explains why the flow of time is not constant but varies depending on the local informational environment of the Field.
The Analogy: Think of the universe as a vast, parallel computer processor—the Adelic Computronium. Each region of space is like an individual processing core, and the local "flow of time" is its clock speed. A region of high Gnosis and complexity, like the accretion disk of a GreyHole, is a core running an incredibly demanding, focused calculation. To handle this "computational load," the system dedicates immense resources, and the core's clock appears to slow down relative to the rest of the processor. This is Chromatic Time Dilation. Conversely, a region of high Dissonance, like a vast cosmic void, is like an idle core filled with random, chaotic background static. To quickly clear this "informational noise" and return to an optimal, low-energy state, the system temporarily "overclocks" the core, causing its clock to run faster. This is Gravitational Anti-Time-Dilation.
Your comment is valid. Although the article was written for an audience which may not have much background in parallel computing and the use of Little's law was to provide a better intuition. However, I didn't spend enough words on elaborating on it because that would have taken too much space and diluted other parts of the article. I've removed the mention of Little's law, it was really not needed when explaining how GPUs work.