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Nvidia Tesla Supercomputer (960 cores)
- andr 18y agoI'd love me 2 of those. Has anyone had experience with CUDA?
- lutorm 18y agoYes. It's pretty nice. As nice as C can get, that is... The biggest hurdle for me was thinking data-parallel as opposed to sequentially or even task-parallel.
- chasingsparks 18y agoI use CUDA for finance based Monte-Carlo simulations. Unless you are at a place with a huge cluster that you have access to, some problems cannot be solved without CUDA.
- light3 18y agoOne downside to CUDA is its massively powerful for single FP precision, but double FP precision performance is less than 1/10 of single FP performance. When precision is needed CUDA is much less useful, say you're running 10^10 simulations then with single FP precision you will only have a result accurate to 5 significant figures.
- lsb 18y agoAFAIK some experimental versions of GHC can compile Haskell to GPUs, and you only lose a pretty small constant factor.
- manvsmachine 18y agoI haven't yet, but my summer / fall is going to be spent working with one of my profs to set up a lab with CUDA workstations and a Tesla. If you're interested in GPGPU programming, CUDA works with any system with a GeForce 8XXX or higher GPU. So ~$150 for a 9800GTX will get you 128 equivalent cores, which you can easily double at any time by running SLI. What I'm interested in is seeing how OpenCL plays out, as Khronos seems to have the entire industry behind them (with the exception of Microsoft).
- Hates_ 18y agoSo what would be primary use of something like this?
- zandorg 18y agoEither a map-reduce type operation, or a job which can be split into multiple processes, and cobbling together all their data into a result at the end (or periodically). Optical Character Recognition could be done, by for instance, recognising each letter on a different core.
- palish 18y agoAssuming you know where each letter starts and ends. So each word maybe.
- comatose_kid 18y agohttp://www.nvidia.co.uk/page/tesla_testimonials.html http://www.nvidia.co.uk/page/tesla_testimonials.html
- lutorm 18y agoGo to Technologies -> CUDA -> Cuda Applications and you'll find a screenful of applications.
- helveticaman 18y agoFrom the title, I was expecting something about the onboard computer of the acclaimed nerdmobile.
- zandorg 18y agoWow, a link I posted 2 days ago (also .co.uk) got frontpage'd. :-D I'm gonna love one of these babies for my AI work!
- deleted 18y ago[deleted]
- Zak 18y agoFrom what little I know of the subject, I can see this becoming the dominant platform for game servers, especially MMO games within a few years.
- MikeCapone 18y agoNew Folding@home super-cruncher?
- fireteller 18y ago(I can't see any of the 11 comments so forgive me if post is redundant in some way) Visual Effects production often needs lots of computing resources for rendering. This is a task that can be heavily parallelized. Usually this is done on a frame/per core basis, with memory being the limiting factor. Now I've started using EC2 recently which made me realize a different approach to rendering tasks. Normally a facility has a fixed number of CPUs available to run jobs. So for example 100 cpus. Let's say the average number of frames to render is usually around 200. So if each frame takes an hour to render and your's is the only job on the queue then it will take 2 hours to see the complete shot. And it's much worse in the real world with many artists working on many shots. Whereas using a resource like EC2 you can always render your entire shot in 1 hour regardless of the number of frames. You're only limited by the time it takes 1 frame to render, and the cost is the same if you use 1 cpu or 200. In other words one can trade depth for breadth. Now this may seem obvious to anyone familiar with EC2, but for me it means that tools like Tesla which where once in high demand for this type of work are now much less valuable. I would expect that it's price/performance is much better then EC2, but where is the cutoff? How many hours do you have to run that Tesla to come out ahead? And if you're running 1 Tesla for that many hours, might it not be worth a premium to get your answer sooner by running more massively parallel (but for less wall clock time) on an EC2 like service? I suspect these types of tools becoming only relevant to real time applications (due to reduced latency vs. EC2), and or nonstop computing (assuming there's a big price/performance win).
- wmf 18y agoPretty soon someone will rent you GPUs by the hour, at which point you'd have the advantages of elasticity and price/performance.
- FlorinAndrei 18y agoHow many FPS in Quake? (just kidding)
- deleted 18y ago[deleted]
- blahblahblah 18y agoThis is probably very cool for people doing CG work. It's still too limited for most scientific work though because only single precision floating point is supported. If they'd make a version of this that supports double precision arithmetic and support it in the BLAS library so that parallelization of matrix operations occurs automatically without having to change existing MATLAB code, these things will sell like crazy in the scientific community.
- wmf 18y agoCUDA now supports double precision and BLAS.
- rys 18y agoIt's not quite 960 cores. There are 960 main ALUs across the system, but they're 8-way per item you'd maybe call a core (the stream multiprocessor) and run the same program instruction per clock. There are multiple partitions to execution on top of that, too: 3 cores share a memory space, 10 to a chip, each chip with access to board memory, three chips in the complete Tesla system. Those 960 ALUs are just the main FP32 and INT32 hardware, too. There are others (FP32 MUL and special function units, and an FP64 ALU too, per core).