deviceQuery) seem to think there are no CUDA devices available when I’m not forcing the use of the nvidia card. I too would be interested to know more about why some applications (e.g., the ones I tried in the SDK) can find the 330M just fine, while others (e.g. This brief guide will give you some important tips to help alleviate any confusion you might have when using gfxCardStatus. Your gfxCardStatus tip fixed it for me as well. I had similar issues with a fresh CUDA 3.1 driver/toolkit/sdk install on my i7 MBP with 10.6.4. This issue also raises the question: Is it the responsibility of an application programmer to activate the nVidia card on the MBP, or should it be the responsibility of the CUDA SDK? In my judgement, the case could be made for both… So the issue boils down to: How do I programmatically force OS X to activate the nVidia card? When I force OS X to use the nVidia card, everything works nicely. When I force OS X to use the Intel card, I get the behaviour described in my first post. Here’s an update: I installed the application gfxCardStatus which shows which card is active, and also allows for forcing OS X to use a specific graphics card. Gfxcardstatus-2.3.zip and gfxCardStatus-1.7.5. ![]() Running a video in the background (hoping that would trigger a switch to the CUDA card) did not help.ĭoes anybody here on this forum have any idea what I’m doing wrong here?! The most popular versions among gfxCardStatus for Mac users are 2.3 and 1.7. Reinstalling the CUDA driver, or the SDK does not help. deviceQueryĬUDA Device Query (Runtime API) version (CUDART static linking)ĭeviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 55683, CUDA Runtime Version = 0.0, NumDevs = 0 deviceQuery:Ĭarsten-Kuckuks-MacBook-Pro:release carsten$. gfxCardStatus is an unobtrusive menu bar app for OS X that allows MacBook Pro users to see which apps are affecting their battery life by using the more power-hungry graphics. When I finally felt ready to try out a few things, I was quite surprised that the installed software did not find my CUDA card! Here’s the output of. I then started reading a book about CUDA, which took me a few days. If gpg is not available, see the GnuPG homepage for installation instructions.). (Most operating systems include the gpg command by default. ![]() I could execute the compiled programs, and it was nice to see the output of. Verify the authenticity of downloaded binary (optional) For added security, follow these steps to verify the signature of your InfluxDB download with gpg. Immediately after installing the software, I did a “make” on the C programs, and everything went well. About a week ago, I downloaded and installed the CUDA 3.0 SDK and driver on my new MacBookPro (MacBookPro6,1, 2.53 GHz Intel Core i5, 8GB) which comes with a built-in Intel HD Graphics adapter, and an NVIDIA GeForce GT 330M.
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