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▲ nickysielicki 14 days ago

If you care enough to time it, you should care enough to time it correctly.

▲bee_rider 14 days ago | parent | next [-]

I described the correct way to time it when using the card as a black-box accelerator.

▲nickysielicki 14 days ago | parent [-]

You can create metrics for whatever you want! Go ahead!

But cuda is not a black box math accelerator. You can stupidly treat it as such, but that doesn’t make it that. It’s an entire ecosystem with drivers and contexts and lifecycles. If everything you’re doing is synchronous and/or you don’t mind if your metrics include totally unrelated costs, then time.time() is fine, sure. But if that’s the case, you’ve got bigger problems.

▲bee_rider 14 days ago | parent | next [-]

Sure, it’s easy to say “there are bigger problems.” There are always bigger problems.

But, there are like 50 years worth of Fortran numerical codes out there, lots of them just use RCIs… if I want to try CUDA in some existing library, I guess I will need the vector back before I can go back into the RCI.

▲doctorpangloss 14 days ago | parent | prev [-]

You're arguing with people who have no idea what they're talking about on a forum that is a circular "increase in acceleration" of a personality trait that gets co-opted into arguing incorrectly about everything - a trait that everyone else knows is defective.

▲gavinray 14 days ago | parent | prev [-]

One of the wisest things I've read all week.

I authored one of the primary tools for GraphQL server benchmarks.

I learned about the Coordinated Omission problem and formats like HDR Histograms during the implementation.

My takeaway from that project is that not only is benchmarking anything correctly difficult, but they all ought to come with disclaimers of:

"These are the results obtained on X machine, running at Y time, with Z resources."