kmbriedis

I believe the ML community will strongly disagree. CUDA is everything

The most surprising aspect of this is that it's a solo paper accepted for a major conference!

There's another case of reverting the codebase to an earlier version just to run it

My guess that it's happening mostly due cache conflicts. With 1024 for a simplified L1 with 32kb you can fit exactly 8 lines of the inner dimmension in the cache, which means that (0,8,0) would have the same cache location as (0, 0, 0), which is bad for tiling

As a side note - loop unrolling has almost no effect, it just allows scalar replacements for latency bound instructions and some other stuff

But it can be 20x

So no custom CUDA kernels, just CuPy? Isn't that a performance issue? (Based on installation notes)

People would probably find out what hardware they use for benchmarks and optimize for that, leading to performance decrease for many othes

One could argue if those can be called "technically knowledgeable"

Was it a university like ETH Zürich, where anyone can get in but there is a limited number of spots from the second year onwards? Then it really made sense to suggest that

Same here. I have taken many Calculus and Algebra classes, SAT Math level 2 and have no clue why these graphing calculators are necessarily...

Are you a software developer? No judging, just curious

Interesting is very subjective term. E.g. some find theory much more interesting than games

2$ for a bag is expensive (in Zurich)?