Recent blog entries for gilbou

she kept interrupting the guy all the time (the crowd started to talk about it and they could not believe it). she asked no interesting question, none. she kept making some bold statements, talking to the crowd in what i have to call a vulgar way.

worst interview ever. just watch the mess and judge by yourself.

got a new video card, a nvidia 8600 gt. the overclocked model from club3d (it's in sweden i think). it's not really to get a better world of warcraft from linux (works like a charm by the way, on debian etch 4.0 32 bits + cedega 6.0.5). i had a 7900 gt previously and could not try to play with Cuda. at work (cnrs) we got a cluster. and its admin bought two tesla cards from nvidia which are dedicated to mathematics (128 threads par cpu, two per card). the good thing about it and compared to the ati solution (this is gonna change very soon i think under's amd guidance) is we use nvcc which are wrappers (not very clean if you dive your nose in) around gcc. it's moving fast. first, each card offers us 500 Gflops so we have a theorical 1 Tflop with just two cards loaded on two machines. it's not very portable and you really need to know the architecture behind it to have it work properly. the first week we got the cards was funny. once the card was plugged the machine was not even booting. it only worked on a very very few specific dell machines. after a nvidia developer gave us some daily code, it finally worked. well the machine was booting but once the nvidia's cuda driver was installed it did not see the card. it took over two weeks to have the cards available from debian on the cluster ;) and of course all pre-packaged stuff is only for redhat, suse.. well. i am a bsd monk so i really dont like linux. but god i do love debian (i never thought i would ever say that). the nice thing about the whole thing is you buy a cheap video card, and you can from linux write cuda code. then, we just move that code to the cluster where the tesla cards run it with their raw computing power. that's cool to be able to compile and run it on small scales and then just load it on the cluster for full range calculations. the only thing that really annoys me if that most Cuda functions are UPPERCASE like this and i got a happy hacking keyboard without capslock key... :/

well tonight i'm going to install and try at home for the first time to use cuda on my 8600 and i'd like to implement something easy like a md5 hash or so. and then upload it to the cluster but it's not really something you can get scalar execution from (well, it's a stream you work upon so it's gonna be disk-bound in performance).

having the SLI to work was very easy. edited the xorg.conf and forced the MultiGPU option to yes and selected AFR (alternate frame rendering) as method rather than Auto because in this mode the SLI/Dual GPU rendering will only work if the application being run is recognized.