TopoNets: High performing vision and language models with brain-like topography

Re:TopoNets: High performing vision and language models with... by whynotminot on Thursday January 30, 2025 @10:55PM (#42884570)

This makes me remember game developers back when games could still be played directly from the physical disc. They would often duplicate data to different parts of the disc, knowing that certain data would often be streamed from disc together, so that seek times were minimized.

But those game devs knew where everything was spatially on the disc, and how the data would generally be used during gameplay. It was consistent.

Do engineers have a lot of insight into how models get loaded spatially onto a given GPU at run time? Is this constant? Is it variable on a per GPU basis? I would think it would have to be.

Hard to optimize for this.

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Re:TopoNets: High performing vision and language models with... by jaek on Thursday January 30, 2025 @11:26PM (#42884685)

This brings to mind The Story of Mel from programming folklore.

http://beza1e1.tuxen.de/lore/story_of_mel.html

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Re:TopoNets: High performing vision and language models with... by abrookewood on Friday January 31, 2025 @12:28AM (#42884946)

Such a good read - some people really are on another level in their chosen field.

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Re:TopoNets: High performing vision and language models with... by vlovich123 on Friday January 31, 2025 @10:55AM (#42888817)

Right now models have no structure so that access is random but you definitely know where the data is located in memory since you put it there. It doesn’t matter about the physical location - it’s all through a TLB but if you ask the GPU for a contiguos memory allocation it gives it to you. This is probable the absolute easiest thing to optimize for if your data access pattern is amenable to it.

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