The most important thing is having a CUDA-compatible hardware. Most of current graphics cards coming from NVIDIA (R) are programmable using the CUDA language. In the referred paper you can find an example of algorithm acceleration using multicore GPUs. There are lots of samples at internet for programming CUDA cards in order to exploit the inherent parallelism of some algorithms / functions.
Article Efficient tool path computation using multi-core GPUs
I forgot to tell you that some multiplatform libraries as OpenCV integrate CUDA extensions for accelerating image processing or vision algorithms.
Dear Mohamed Yehia Dahab,
The first very important thing is to have your graphics card (best from nVidia). The simplest solution is to use geforce card (the same that we use in games) or a more efficient Tesla cards (see links below). Another important thing is to have a proper:
If you already have a PC you can have the following software configurations:
Both the first and the second option can be used on the operating system Windows or Linux. Easier configuration is using Windows but stable work done under Linux.
Best regards,
Konrad
http://www.coolermaster.com/case/mid-tower/haf-xm/
http://www.nvidia.com/object/tesla-workstations.html
http://www.mathworks.com/products/parallel-computing/
http://www.wolfram.com/mathematica/new-in-8/cuda-and-opencl-support/
You just need a computer with a CUDA compatible GPU (see below), then of course install the drivers and software for your OS. Power required from your computer depends on the GPU, so you need to check the number of extra connectors that your power supply has (for example, 2x6pin like the GTX 970).
Tesla
Tesla K80
Tesla K40
Tesla K20
Tesla C2075
Tesla C2050/C2070
Quadro Desktop
Quadro K6000
Quadro K5200
Quadro K5000
Quadro K4200
Quadro K4000
Quadro K2200
Quadro K2000
Quadro K2000D
Quadro K620
Quadro K600
Quadro K420
Quadro 410
Quadro Plex 7000
Quadro Mobile
Quadro K5100M
Quadro K500M
Quadro K4100M
Quadro K3100M
Quadro K2200M
Quadro K2100M
Quadro K1100M
Quadro K620M
Quadro K610M
Quadro K510M
Desktop Geforce:
GeForce GTX TITAN X
GeForce GTX TITAN Z
GeForce GTX TITAN Black
GeForce GTX TITAN
GeForce GTX 980
GeForce GTX 970
GeForce GTX 960
GeForce GTX 780 Ti
GeForce GTX 780
GeForce GTX 770
GeForce GTX 760
GeForce GTX 750 Ti
GeForce GTX 750
GeForce GTX 690
GeForce GTX 680
GeForce GTX 670
GeForce GTX 660 Ti
GeForce GTX 660
GeForce GTX 650 Ti BOOST
GeForce GTX 650 Ti
GeForce GTX 650
GeForce GTX 560 Ti
GeForce GTX 550 Ti
GeForce GTX 460
GeForce GTS 450
GeForce GTX 590
GeForce GTX 580
GeForce GTX 570
GeForce GTX 480
GeForce GTX 470
GeForce GTX 465
GeForce GT 740
GeForce GT 730
GeForce GT 720
GeForce GT 705*
GeForce GT 640
GeForce GT 630
GeForce GT 620
GeForce GT 610
GeForce GT 520
GeForce GT 440
GeForce GT 430
Notebook Geforce
GeForce GTX 980M
GeForce GTX 970M
GeForce GTX 965M
GeForce GTX 960M
GeForce GTX 950M
GeForce GTX 880M
GeForce GTX 870M
GeForce GTX 860M
GeForce GTX 850M
GeForce 840M
GeForce 830M
GeForce 820M
GeForce GTX 780M
GeForce GTX 770M
GeForce GTX 765M
GeForce GTX 760M
GeForce GTX 680MX
GeForce GTX 680M
GeForce GTX 675MX
GeForce GTX 675M
GeForce GTX 670MX
GeForce GTX 670M
GeForce GTX 660M
GeForce GT 750M
GeForce GT 650M
GeForce GT 745M
GeForce GT 645M
GeForce GT 740M
GeForce GT 730M
GeForce GT 640M
GeForce GT 735M
GeForce GT 635M
GeForce GT 730M
GeForce GT 630M
GeForce GT 625M
GeForce GT 720M
GeForce GT 620M
GeForce 710M
GeForce 610M
GeForce GTX 580M
GeForce GTX 570M
GeForce GTX 560M
GeForce GT 555M
GeForce GT 550M
GeForce GT 540M
GeForce GT 525M
GeForce GT 520MX
GeForce GT 520M
GeForce GTX 485M
GeForce GTX 470M
GeForce GTX 460M
GeForce GT 445M
GeForce GT 435M
GeForce GT 420M
GeForce GT 415M
GeForce GTX 480M
GeForce 710M
GeForce 410M
https://developer.nvidia.com/cuda-gpus
Just a quick heads up that if you go for the K40 you'll also need to K600 or K2000 as your 1st graphics card because it does not have outputs
You need programming capable GPU, Programming SDK and IDE.
Dear Mohamed,
Your question is to general to allow a short or an specific answer. If you find this to long you can contact me.
There are some aspects you are confronted with:
The first and the most important is finances. It will severely limit your expectation horizon and will partly decide over hardare and software options.
For the next aspect. hardware, visit first two wikipedia pages:
- CUDA, and find what "compute capability" is, which is suitable for your application, which Nvidia chip provides it (gt, gk, gm, gp / 1xx, 2xx) and a small list of graphic cards that are based on it
- List of Nvidia graphic cards, where you ill find the complete list of Nvidia graphics cards and look in the lasts columns at processing power (Tflop single AND double precision), power consumption (TDP), and eventualy price and remember the first (card) and especially the second (cip) columns.
For learning, writing, debuging. profiling and testing any card will do. The newest generation ("compute capability") the better. Avoid the firsts couple of entry level as crippled or from older generations (Nvidia marketing policy)
If your application(s) need double precision as CFD, MHD, CAD/CAE (there are some that do not as N-body) and you are from the "enthusiast" class look for dual cards. I personally have three gtx295 (old but decent, double precision and extremely cheap on the second hand market) and looking for a gtx Titan Z (not X. crippled DP), the only professional card which arrived on the consumer maket (NVIDIA mistake?)
As for platform you have Intel (expensive) or AMD (cheaper). Look for a motherboard ith as many accesories and features you can find. You will never know when you will need them.
Be shure to have: as maany processor cores as CUDA cips plus one for the integrated video wwhich drives your display (you may dispense of it but there is no place here).
Be shure to have at least the amount of memory the nividia cards have combined (it is I/O mirrored) plus the memory required for the operating system and the softare applications in use (3-5GB)
As for the PSU you may find some "Online PSU calculator" on the web. Start with about (a reliable) 350W and add the videos. I personally use a 300W Fortron PSP Boster (slides into a CDROM slot) for each 300W hungry gtx295 so they can easely migrate and the PSU takes care of the motherboard only
The last aspect is software begining with the operating system: Windos(which gen)/Linux(wich distro). Prefer mature against new. Next comes the "CUDA Toolkit" and its exhaustive documentation (a must to read). Be aware that new toolkits deprecatred old hardare. And last your specific software: Matlab/octave. IDL/ gdl, python/python (hihi) but these are purpose specific and only you know.
Best wishes, Adrian, [email protected]