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NVIDIA Quadro M6000 12GB Maxwell Workstation Graphics Tested Showing Solid Gains

Posted by samzenpus on from the check-it-out dept.

MojoKid writes: NVIDIA's Maxwell GPU architecture has has been well-received in the gaming world, thanks to cards like the GeForce GTX Titan X and the GeForce GTX 980. NVIDIA recently took time to bring that same Maxwell goodness over the workstation market as well and the result is the new Quadro M6000, NVIDIA's new highest-end workstation platform. Like the Titan X, the M6000 is based on the full-fat version of the Maxwell GPU, the G200. Also, like the GeForce GTX Titan X, the Quadro M6000 has 12GB of GDDR5, 3072 GPU cores, 192 texture units (TMUs), and 96 render outputs (ROPs). NVIDIA has said that the M6000 will beat out their previous gen Quadro K6000 in a significant way in pro workstation applications as well as GPGPU or rendering and encoding applications that can be GPU-accelerated. One thing that's changed with the launch of the M6000 is that AMD no longer trades shots with NVIDIA for the top pro graphics performance spot. Last time around, there were some benchmarks that still favored team red. Now, the NVIDIA Quadro M6000 puts up pretty much a clean sweep.

3 of 66 comments (clear)

  1. Too bad by Anonymous Coward · · Score: 5, Interesting

    its too bad there is no double precision performance to speak of on these newer cards lately. good for games, not much else.

    1. Re:Too bad by Anonymous Coward · · Score: 4, Informative

      If your algorithm is unstable at single precision floating point, it's going to be unstable at double precision as well.

      Do you even know what you're talking about? error propagation, does that ring a bell? I'll give you a hint, if your computation requires a large number of operations then the absolute magnitude of machine rounding errors is critical. And, lest you think this is a 'very small niche', any matrix multiplication has O(n) operation per element. Chain a few those (say, in a Markov chain type of random walk) and it's an exponential growth. Using double instead of single is like being able to do periodic (expensive) full recomputations to control stability of a fast-updating chain instead of barely having enough precision when doing the full recomputations. Fast and stable versus extremely slow and perhaps (depending on today's $DEITY's mood) barely stable.

      To put it differently, by the time error accumulation in double precision leaves you with a single-precision-worth of valid digits, single precision error accumulation has long ago made the computed value completely meaningless.

    2. Re:Too bad by Anonymous Coward · · Score: 3, Informative

      Yea, they do go Nvidia is the unfortunate reality but it's somewhat understood if you look at all the extra capabilities Nvidia's architecture exposes - this becomes clear once you've really soaked in on AMD and OpenCL. Also AMD flubbed up on double precision after the 7990s. No one gives a crap about double precision for the foreseeable future except all the researchers and engineers programming the darn things. Haha. Wait, why is no one laughing?