Well it's not as if a fileserver would ever profit from CUDA. Same goes for WSUS or messaging protocols. It's not as if the CPU was the bottleneck for those things (and I have no idea how you'd even write an efficient CUDA XMPP implementation for example - I don't like saying it's impossible but I don't see how)Unless you have a full-time programmer working for your office that can write the opencl software you need, then it's pointless. Some of the big companies do ...
Well it's not as if a fileserver would ever profit from CUDA. Same goes for WSUS or messaging protocols. It's not as if the CPU was the bottleneck for those things (and I have no idea how you'd even write an efficient CUDA XMPP implementation for example - I don't like saying it's impossible but I don't see how)
Databases usually also fall into that category, although the term is nonspecific enough so if we include data warehouses - well possible although the bottleneck should still be the memory..
Sorry, as I understood your post you meant that if someone had the programmers for the task they could profit from using tesla. Seems like I misunderstood you, but no harm done I hope.I think you quoted me because you misunderstood my position.
I am not suggesting the op purchase tesla for any of the needs he listed.
I should be more clear
i think these could be of benefit to databases. but as database costs are essentially a high software $$$. the benefits would be very questionable.
How would it help databases? Databases are all about memory access, and while GPU's have a lot of bandwidth, the memory latency is something truly appalling.
If you're talking about a normal db (and not datawarehousing or something where you do some computational expensive things with the data) then I don't see what you're getting at. The current memory of a GPU together with how their caches work limit the usefulness of a GPU for memory limited things quite a lot.lol - you just answered the question then tried to deny it for some reason. Having memory several times as fast matters a lot more then a little latency. Not that I am convinced that databases are "all about memory access" anyway - I am sure there's a whole pile of hardware bottlenecks - fast processing for all the server side work, v fast network access, etc.
But, why? They offer proprietary software, and keep customers on an upgrade treadmill. The T-series gives them the ability to make that proprietary hardware, too. It is not just unlikely for awhile, it is unlikely, period, unless oracle buys a company that currently produces GPUs, to make a new processor for Oracle software to run on. Ellison makes Jobs look humble and selfless.Anyway I am sure a gpu could speed up servers but that requires software that runs on the gpu which is pretty unlikely for a while. Perhaps one day oracle will bring out a gpu accelerated version, but lots complexity as these things have to be 100% reliable and secure.
If that were plausible, maybe. As it stands, that kind of speedup is unreasonable, and will remain so, until memory operations are fast, or until a realiable OS runs on Teslas, DB software gets made for it, and nVidia starts supporting decent amounts of memory. The T-series is still pretty much untouchable, for the next several years.Oracle will react pretty quickly if a competitor comes out that uses the gpu and runs at 10 times the speed.
The GPU would not be able to do that more efficiently. A GPU is slow at doing what your CPU generally does. Fermi can do some pointer chasing, but not well. Compression is not something a GPU will be able to do well. Decompression, with the right compression algorithm, maybe. If the network is the bottleneck, a programmable GPU won't fix that.Also obviously it depends on the database but there's more to most servers then the actual oracle query. For example databases I work with would with do a query then return a load of data (i.e. a bunch of files). For the server the most efficient way is to return that data as it is but that is very heavy on network traffic. Often the most out of date bit of most companies IT infrastructures is their networking.
It would be much more efficient to zip all those files up into one big compressed file and send that, but that's a very computationally expensive operation. Often you can go even further and extract the sections of those files really required and just send them zipped up - even more computationally heavy.
That is the sort of thing a gpu would be able to do quite efficiently. I am sure there's a load of other examples that would also work well on a gpu - given new hardware you can always make use of it, just takes a company with a bit of initiative to work out how.
lol - you just answered the question then tried to deny it for some reason. Having memory several times as fast matters a lot more then a little latency.
Compute is the key word. Most of the space and power of your CPU is dedicated to doing one thing, through several different mechanisms: accurately predict what addresses in memory you will need over the next few hundred cycles, and try to make sure that the right one is on the CPU. The rest is useful, but no amount of ALUs, FPUs, dedicated vector engines, or any of that, will make up for a branch predictor that isn't up to snuff, or prefetchers simultaneously fetching with several different algorithms.Thanks for the replies, interesting discussion.
http://www.tomshardware.com/news/Windows-Leopard-Cuda-Stream,7648.html
"Consider the GPU as a 'helper' now, offering up its higher-end processing areas to compute a portion of a task carried out by the CPU..."
...Makes it sound like it helps out with nearly anything the CPU happens to be doing.
No that's not what it means (or it's what it meant - haven't read the article, but it's factually wrong). In reality there are some libraries for Java, Python - pretty much the same as for C/C++. And believe me the last time I checked the Python CUDA lib was.. not pretty. That means writing Kernels in the usual C++ variant as strings. numpy could use CUDA for some stuff which was as nice as usual, but writing cuda programs yourself in python? Horrible. Or at least not nicer than writing them in C++ to start with. And writing CUDA kernels efficiently is a topic for itself - the compiler is just plain incapable (when was the last time you had to unroll a loop yourself )."Nvidia's CUDA is compatible with many computational interfaces including PhysX, Java, Python, OpenCL, and DirectX Compute..."
...Python and Java might be of help on a server, assuming this means CUDA helps run those no matter how they're written.