Weird rumble scores
← Thread:Talk:BeepBoop/Weird rumble scores/reply (29)
I'm not sure I follow: what you are saying suggests that skipped turns should happen more on Xor_Sily right? I assume the reason for BeepBoop's low scores is it skipping lots of turns.
As an aside, I've also noticed that DrussGT 3.1.7 has also dropped 0.3 APS compared to 3.1.6, maybe it is also getting bad battles with lots of skipped turns?
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I mean if you run battles on computers without turbo boost, you should get identical results as Xor_Sily.
Hmmm I'm still not able to reproduce the low-scoring battles. First of all, turning off turbo boost does change the CPU constant for me (it's ~4e6 with turbo boost on and ~5e6 with turbo boost off, I've recomputed the constant both with/without multiple times and it seems reasonably consistent). But even if I use the 4e6 CPU constant with turbo boost turned off, I am getting essentially the same results as in the image I uploaded. What CPU constant does Xor_Sily use? Maybe as a short-term fix you could add BeepBoop to its roborumble.txt EXCLUDE?
I stopped Xor_Sily. It's some high performance server that costs $40 a week. Maybe we need some test set to verify rumble clients before entry, making it easier to serve a client.
Btw, could you re-submit a version and run rumbles on your machine, and see if we can get the correct result now?
I think the problem is that Xor_Sily is running 7/24, for months. If memory leak happens, GC will get worse and worse. Maybe I should add some auto restart script later and try again.
You can set the max iterations in the client and run it in a bash loop.
What sort of high performance server is this? If it's virtualized rather than true dedicated, it wouldn't surprise me if the exact amount of available CPU varies dramatically from moment-to-moment even if the provider is guaranteeing some number of cores worth of overall performance.
Could be interesting to some time make a tool for measuring the stability of available CPU, based on running rapidly running a series of identical micro-benchmarks and looking at the variations in how long it takes.