Put me in front of a dual processor motherboard and a pair of eight core Xeons with HyperThreading and I will squeal with delight.  Then I will take it to the cleaners with multithreaded testing to actually see how good it is.  Watching a score go up or the time taken to do a test going down is part of the parcel as a product reviewer, so watching the score go higher or the time taken going down is almost as good as product innovation.

Back in research, two things can drive the system: publication of results and future relevance for those results.  Understanding the system to get results is priority number one, and then being able to obtain results could be priority number two.  In theoretical fields, where a set of simulations can take from seconds to months and even years, having the hardware to deal with many simulations (or threads within a simulation) and the single threaded speed means more results per unit time.  Extremely useful when you get a weeks worth of results back and you missed a negative sign in the code (happens more often than you think).  Some research groups, with well-developed code, take it to clusters.  Modern takes on the code point towards GPUs, if the algorithm allows, but that is not always the case.

So when it comes to my perspective on the GA-7PESH1, I unfortunately do have not much of a comparison to point at.  As an overclocking enthusiast, I would have loved to see some overclock, but the only thing a Sandy Bridge-E processor with an overclock will do is increase single threaded speed – the overall multithreaded performance on most benchmarks is still below an i7-3960X at 5 GHz (from personal testing).  For simulation performance, it really depends on the simulation itself if it will blaze though the code while using ~410 watts.

Having an onboard 2D chip negates needing a dedicated display GPU, and the network interfaces allow users to remotely check up on system temperatures and fan speeds to reduce overheating or lockups due to thermals.  There are plenty of connections on board for mini-SAS cabling and devices, combined with an LSI SAS chip if RAID is a priority.  The big plus point over consumer oriented double processor boards is the DIMM slot count, with the GA-7PESH supporting up to 512 GB.

Compared to the consumer oriented dual processor motherboards available, one can criticize the GA-7PESH1 for not being forthcoming in terms of functionality.  I would have assumed that being a B2B product that it would be highly optimized for efficiency and a well-developed platform, but the lack of discussion and communication between the server team and the mainstream motherboard team is a missed opportunity when it comes to components and user experience.

This motherboard has been reviewed in a few other places around the internet with different foci with respect to the reviewer experience.  One of the main criticisms was the lack of availability – there is no Newegg listing and good luck finding it on eBay or elsewhere.  I send Gigabyte an email, to which I got the following response:

  • Regarding the availability in the US, so far all our server products are available through our local branch, located at:

    17358 Railroad St.
    City of Industry
    CA 91748
    +1-626-854-9338

As a result of being a B2B product, pricing for the GA-7PESH1 (or the GA-7PESH2, its brother with a 10GbE port) is dependent on individual requirements and bulk purchasing.  In contrast, the ASUS Z9PE-D8 WS is $580, and the EVGA SR-X is $650.

Review References for Simulations:

[1] Stripping Voltammetry at Microdisk Electrode Arrays: Theory, IJ Cutress, RG Compton, Electroanalysis, 21, (2009), 2617-2625.
[2] Theory of square, rectangular, and microband electrodes through explicit GPU simulation, IJ Cutress, RG Compton, Journal of Electroanalytical Chemistry, 645, (2010), 159-166.
[3] Using graphics processors to facilitate explicit digital electrochemical simulation: Theory of elliptical disc electrodes, IJ Cutress, RG Compton, Journal of Electroanalytical Chemistry, 643, (2010), 102-109.
[4] Electrochemical random-walk theory Probing voltammetry with small numbers of molecules: Stochastic versus statistical (Fickian) diffusion, IJ Cutress, EJF Dickinson, RG Compton, Journal of Electroanlytical Chemistry, 655, (2011), 1-8.
[5] How many molecules are required to measure a cyclic voltammogram? IJ Cutress, RG Compton, Chemical Physics Letters, 508, (2011), 306-313.
[6] Nanoparticle-electrode collision processes: Investigating the contact time required for the diffusion-controlled monolayer underpotential deposition on impacting nanoparticles, IJ Cutress, NV Rees, YG Zhou, RG Compton, Chemical Physics Letters, 514, (2011), 58-61.
[7] D. Britz, Digital Simulation Electrochemistry, in: D. Britz (Ed.), Springer, New York, 2005, p. 187.
[8] W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, 2007.
[9] D.E. Knuth, in: D.E. Knuth (Ed.), Seminumerical Algorithms, Addison-Wesley, 1981, pp. 130–131
[10] K.Gregory, A.Miller, C++ AMP: Accelerated Massive Parallelism with Microsoft Visual C++, 2012, link.
+ others contained within the references above.

System Benchmarks
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  • Hulk - Saturday, January 05, 2013 - link

    I had no idea you were so adept with mathematics. "Consider a point in space..." Reading this brought me back to Finite Element Analysis in college! I am very impressed. Being a ME I would have preferred some flow models using the Navier-Stokes equations, but hey I like chemistry as well. Reply
  • IanCutress - Saturday, January 05, 2013 - link

    I never did any FEM so wouldn't know where to start. The next angle of testing would have been using a C++ AMP Fluid Dynamics Simulation and adjusting the code from the SDK example like with the n-Body testing. If there is enough interest, I could spend a few days organising it for the normal motherboard reviews :)

    Ian
    Reply
  • mayankleoboy1 - Saturday, January 05, 2013 - link

    How the frick did you get the i7-3770K to *5.4GHZ* ? :shock:
    How the frick did you get the i7-3770K to *5.0GHZ* ? :shock:
    Reply
  • IanCutress - Saturday, January 05, 2013 - link

    A few members of the Overclock.net HWBot team helped testing by running my benchmark while they were using DICE/LN2/Phase Change for overclocking contests (i.e. not 24/7 runs). The i7-3770K will go over 7 GHz if (a) you get a good chip, (b) cool it down enough, and (c) know what you are doing. If you're interested in competitive overclocking, head over to HWBot, Xtreme Systems or Overclock.net - there are plenty of people with info to help you get started.

    Ian
    Reply
  • JlHADJOE - Tuesday, January 08, 2013 - link

    The incredible performance of those overclocked Ivy bridge systems here really hammers home the importance of raw IPC. You can spend a lot of time optimizing code, but IPC is free speed when it's available. Reply
  • jd_tiger - Saturday, January 05, 2013 - link

    http://www.youtube.com/watch?v=Ccoj5lhLmSQ Reply
  • smonsees - Saturday, January 05, 2013 - link

    You might try modifying your algorithm to pin the data to a specific core (therefore cache) to keep the thrashing as low as possible. Google "processor affinity c++". I will admit this adds complexity to your straightforward algorithm. In C#, I would use a parallel loop with a range partition to do it as a starting point: http://msdn.microsoft.com/en-us/library/dd560853.a... Reply
  • nickgully - Saturday, January 05, 2013 - link

    Mr. Cutress,
    Do you think with all the virtualized CPU available, researchers will still build their own system as it is something concrete to put into a grant application, versus the power-by-the-hour of cloud computing?

    Thanks.
    Reply
  • IanCutress - Saturday, January 05, 2013 - link

    We examined both scenarios. Our university had cluster time to buy, and there is always the Amazon cloud. In our calculation, getting a 16 thread machine from Dell paid for itself in under six months of continuous running, and would not require a large adjustment in the way people were currently coding (i.e. staying in Windows rather than moving to Linux), and could also be passed down the research group when newer hardware is released.

    If you are using production level code and manipulating it each time to get results, and you can guarantee the results will be good each time, then power-by-the-hour could work. As we were constantly writing and testing new code for different scenarios, the build/buy your own workstation won out. Having your own system also helps in building GPU codes, if you want to buy a better GPU card it is easier to swap out rather than relying on a cloud computing upgrade.

    Ian
    Reply
  • jtv - Sunday, January 06, 2013 - link

    One big consideration is who the researchers are. I work in x-ray spectroscopy (as a computational theorist). Experimentalists in this field use some of our codes without wanting to bother with having big computational resources. We have looked at trying to provide some of our codes through some cloud-based service so that it can be used on demand.

    Otherwise I would agree with Ian's reply. When I'm improving code, debugging code, or trying to implement new theoretical approaches I absolutely want my own hardware to do it on.
    Reply

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