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I'm interested in buying a new workstation for my lab and in the process I encountered a lot of options which some I did not fully understand.

The main function this workstation is for numerical simulations which mainly involve: solving large eigenvalue problems and algebraic equations, large lists manipulations and setting (symbolic and numeric) and analyzing large databases.

I am not sure which hardware is needed for each usage (more memory, more CPU speed, more processing cores. etc.), so I can't really tell which system I need.

Furthermore, I am using parallel-computations in my simulations which cuts of a great deal of time for it to run. Thus, I was offered two alternatives: using coprocessors (such as Intel Xeon Phi) or using GPU processing (nVidia Tesla or Titan). I would like the advantages and disadvantages of each hardware while using Mathematica for my purposes (including the price issues). Moreover, I know that for complicated calculations CUDA programming is necessary for exploiting the most of GPU processing, are there any adjustments I need to make in order to use coprocessors with Mathematica or does it automatically using all available processing-cores?

After my little internet searching I was thinking to get something like the following:

  • Intel Xeon 8 core 2.4GHz processors X2
  • 128Gb RAM memory
  • Intel Xeon Phi coprocessor 51XX or 71XX (depending on price) X2/X3
  • OS SSD drive
  • 2Tb data drive X2

Is it too much? too little?

Thank you all in advance

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    $\begingroup$ Depends really, can't you run a few examples and see with MaxMemoryUsed[] how much you are using? I personally never go beyond 16GB, with what I consider large datasets, but your definition could be different. $\endgroup$
    – Feyre
    Aug 16 '16 at 11:00
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    $\begingroup$ Related or possible duplicates: mathematica.stackexchange.com/questions/69404/…, mathematica.stackexchange.com/questions/59344/…, mathematica.stackexchange.com/questions/28209/…. $\endgroup$
    – Karsten 7.
    Aug 16 '16 at 11:04
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    $\begingroup$ The way you access nvidia graphic cards (probably CUDALink) is different from how you access a Xeon Phi (OpenMP?) and we don't know if you are an experienced CUDA programmer or much more productive and experienced with the software tools available for Intel Xeon Phi processors. $\endgroup$
    – Karsten 7.
    Aug 16 '16 at 11:47
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    $\begingroup$ I think you really should start from a consideration of what you currently do with Mathematica and determine your need from there; else you will end up spending a ton of money on resources you are hardly ever going to use. $\endgroup$
    – Sascha
    Aug 16 '16 at 13:25
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    $\begingroup$ @Karsten7. I contacted them, will update once they give an answer. $\endgroup$
    – Getz
    Aug 16 '16 at 20:15