Forum > Linux
SETI MB CUDA for Linux
Richard Haselgrove:
The credit difference is universal to all SETI CUDA applications - stock Windows as well. That goes back to the developers in Berkeley / nVidia - nothing to do with optimisations in general, or Linux in particular.
dtiger:
The credits don't matter for me. I'm interesting in technology only.
As I see from my experience, small units with working time about 16 mins store correct log info in stderr_txt. The longer units are full of "Cuda error 'GaussFit_kernel' in file './cudaAcc_gaussfit.cu' in line 506 : invalid configuration argument."
Also, small units can start one after one on GPU, while longer units are fall back to CPU after completing one on GPU. Seems to memory issue problem with current Crunch3r's SETI-CUDA release (setiathome-CUDA-6.08.i686.tar.bz2).
Also, as BOINC starts 2 normal CPU crunchers on my C2D E4400 and additionally SETI-CUDA grabs one of CPU for 100%, the crunchers start fighting for second CPU and all thing goes very slowly including X-server response time.
sunu:
256MB seem borderline or not enough for the linux cuda app.
If the cpu app you run is astropulse you can force boinc to run only one instance. In your app_info.xml, in the astropulse section, add
<avg_ncpus>2.0000</avg_ncpus>
<max_ncpus>2.0000</max_ncpus>
immediately after
<version_num>500</version_num>
dtiger:
Seems like 256 MB is enough for Win's version of SETI-CUDA. They run fine.
Also, as I see from workunits page, Windows clients crunch units for 100-200 seconds on 8800 GTS 256MB, while my 8600 GT 256MB run about 1000-2000 seconds for the same unit, it's a huge abnormal difference for similar hardware.
sunu:
--- Quote from: dtiger on 03 Mar 2009, 04:45:33 am ---Also, as I see from workunits page, Windows clients crunch units for 100-200 seconds on 8800 GTS 256MB, while my 8600 GT 256MB run about 1000-2000 seconds for the same unit, it's a huge abnormal difference for similar hardware.
--- End quote ---
Two things:
1. Some users with 256 MB graphics cards see some WUs fall back to CPU computation because of not enough memory. Maybe that explains the increased time.
2. The linux CUDA app uses a full core so the time reported is the "real" computation time. The windows CUDA app uses a small percentage of a single core and records only that time. The "real" computation time for windows machines is much larger, possibly equivalent to that of linux PCs.
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