Gigabyte GA-7PESH1 Review: A Dual Processor Motherboard through a Scientist’s Eyes
by Ian Cutress on January 5, 2013 10:00 AM EST- Posted in
- Motherboards
- Gigabyte
- C602
Simulations, Memory Requirements and Dual Processors
Throughout my simulation career, it would have been easy enough to just write code, compile and simply watch it run. But the enthusiast and speed freak within me wanted the code to go a little faster, then a little faster, until learning about types of memory and how to prioritize code became part of my standard code scenario. There are multiple issues that all come together from all sides of the equation.
First of all, let us discuss at a high level the concept of memory caching on a pseudo-processor.
The picture above is a loose representation of a dual core processor, with each core represented as ‘P’, Registers labeled as ‘R’, and the size of the lines is representative of the bandwidth.
The processor has access to some registers which are a high-bandwidth, low space memory store. These registers are used to store intermediary calculation data, as well as context switching with HyperThreading. The processor is also directly linked to an L1 (‘level 1’) cache, which is the first place the processor looks if it needs data from memory. If the data is not in the L1 then it looks in the L2 (‘level 2’), and so on until the data is found. Obviously the closer the data is to the processor, the quicker it can be accessed and the calculation should prove to be quicker, and thus there are large benefits to larger caches.
In the diagram above, each processor core has its own L1 cache and L2 cache, but a shared L3 cache. This allows each core to probe the data in L3. What is not shown is that there are some snoop protocols designed to let each core know what is going on in another core’s L2 cache. With data flying around it is most important to maintain cache coherency.
Take an example where we have a simulation running two threads on our imaginary processor, and each thread requires 200 kB of data. If our L2 cache is 256 kB then the thread can easily run inside the L2 keeping data rates high. In the event that each core needs data from the other thread, values are copied into L3 at the expense of time.
Now imagine that our processor supports HyperThreading. This allows us to run two threads on each processor core. We still have the same amount of hardware, but when one thread is performing a memory read or write operation, that creates a delay until the read or write operation is confirmed. While this delay is occurring, the processor core can save the state of the first thread and move on with the second.
The downside to our new HyperThreading scenario is when we launch a program with four threads, and each thread uses 200 kB of memory. If our L2 cache is only 256 kB, then the combined 400 kB of data spills over into our L3 cache. This has the potential of slowing down simulations if read and write operations are very slow. (In modern processors, a lot of the logic built into the processor is designed to move data around such that these memory operations are as quick as possible – it goes ahead and predicts which data is needed next.)
This is the simple case of a dual core processor with HyperThreading. It gets even more complicated if you add in the concept of dual processors.
If we have two dual core processors (four cores total) with HyperThreading (eight threads), the only memory share between the processors is the main random access memory. When a standard program launches multiple threads, there is no say in where those threads will end up – they may be run out-of-order on whatever processor core is available. Thus if one thread needs data from another, several things may occur:
(1) The thread may be delayed until the other thread is processed
(2) The data may already be on the same processor
(3) The data may be on the other processor, which causes delays
There are many different types of simulation that can be performed, each with their own unique way of requesting memory or dealing with threads. As mentioned in the first page of this review, even in the research group I was in, if two people wrote code to perform the same simulation, memory requirements of each could be vastly different. This makes it even more complicated, as when moving into a multithreaded scenario the initially slower simulation might be sped up the most.
Talking About Simulations
The next few pages will talk about a different type of simulation in turn based on my own experiences and what I have coded up. Several are based on finite-difference grid solvers (both explicit and implicit), we have a Brownian test based on six movement algorithms, an n-body simulation, and our usual compression / video editing tests. The ones we have written for this review will be explained briefly both mathematically and in code.
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dj christian - Monday, January 14, 2013 - link
No please!This article should be a one time only or once every 2 years at most.
nadana23 - Sunday, January 6, 2013 - link
From the looks of results some of the benchmarks are HIGHLY sensitive to effective bandwidth per thread (ie, GDDR5 feeding a GPU stream processor >> DDR3 feeding a Xeon HT core).However - it must be noted that 8x DIMMS is insufficient to achieve full memory bandwidth on Xeon E5 2S!
I'd suggest throwing a pure memory bandwidth test into the mix to make sure you're actually getting the rated number (51.2GB/s)...
http://ark.intel.com/products/64596/Intel-Xeon-Pro...
... as I strongly suspect your memory config is crippling results.
Dell's 12G config guidelines are as good a place as any to start on this :-
http://en.community.dell.com/cfs-file.ashx/__key/c...
Simply removing one E5-2590 and moving to 1-Package, 8 DIMM config may (counter-intuitively) bench(market) faster... for you.
dapple - Sunday, January 6, 2013 - link
Great article, thanks! This is the sort of benchmark I've been wanting to see for quite some time now - simple, brute-force numerics where the code is visible and straightforward. Too many benchmarks are black boxes with processor- and compiler-specific tunes to make manufacturer "X" appear superior to "Y". That said, it would be most illustrative to perform a similar 'mark using vanilla gcc on both MS and *nix OS.daosis - Sunday, January 6, 2013 - link
It is long known issue, when windows does not start after changing hardware, especially GPU (not always so). There is as long known trick so. Just before last "power off" one should replace GPU's own driver with basic microsoft's one. In case of GPU it is "standart Vga adapter" (device manager - update driver - browse my computer - let me pick up). In fact one can replace all specific drivers on OS with similiar basic from MS and then to put this hard drive virtually to any system without any need for fresh install. Mind you, that after first boot it takes some time for OS to find and install specific drivers.jamesf991 - Sunday, January 6, 2013 - link
In the early '70s I was doing very similar simulations using a PDP 11/40 minicomputer. (I can send citations to my publications if anyone is interested.) At Texas Tech and later at Caltech, I simulated systems involving heterogeneous electron transfer kinetics, various chemical reactions in solution, coulostatics, galvanostatics, voltammetry, chronocoulometry, AC voltammetry, migration, double layer effects, solution hydrodynamics (laminar only), etc. Much of this was done on a PDP 11/40, originally with 8K words (= 16K bytes) of core memory. Later the machine was upgraded to 24 K words (!), we got a floating point board, and a hard disk drive (5 M words, IIRC). My research director probably paid in excess of $50K for the hardware. One cute project was to put a simulation "inside" a nonlinear regression routine to solve for electrode kinetic parameters such as k and alpha. Each iteration of the nonlinear solver required a new simulation -- hand-coding the innermost loops using floating point assembly instructions was a big speedup!I wonder how the old PDP would stack up against the 3770?
flynace - Monday, January 7, 2013 - link
Do you guys think that once Haswell moves the VRM on package that someone might do a 2 socket mATX board?Even if it means giving up 2 of the 4 PCIe slots and/or 2 DIMMs per socket it would be nice to have a high core count standard SFF board for those that need just that.
samsp99 - Monday, January 7, 2013 - link
I found this review interesting, but I don't think this board is really targeted at the HPC market. It seems like it would be good as part of a 2U / 12 + 2 drive system, similar to the Dell C2100. It would make a good virtual host, SQL, active web server etc. Having the 3 mSAS connectors would enable 4 drive each without the need for a SAS expander.Servers are designed for 99.999% uptime, remote management, and hands-off operation. To achieve that you need redundent power, UPS, Networking, storage etc. They also require high airflow, which is noisy and not something you want sitting under your desk. Based on that, it makes sense that the MB is intended for sale to system builders not your general build your own enthusiast.
HW manufactuerers are faced with a similar problem to airlines - consumers gravitate to the cheapest price, and so the only real money to be made is selling higher profit margin products to businesses. Servers are where intel etc makes their profits.
For the computational problems the author is trying to solve, to me it would seem to be better to consider:
a) At one point, I think google was using commodity hardware, with custom shelving etc. Assuming the algorithms can be paralleled on different hosts, you shouldn't need the reliability of traditional servers, so why not use a number of commodity systems together, choosing the components that have the best perf/$.
b) There are machines designed for HPC scenarios, such as HPC Systems E5816 that supports 8x Xeon E7-8000 (10 core) processors, or the E4002G8 - that will take 8 nVidia Tesla cards.
c) What about developing and testing the software on cheap worstations, and then when you are sure its ready, buying compute time from Amazon cloud services etc.
babysam - Monday, January 7, 2013 - link
It is quite delighting to look at your review on Anandtech (especially when I am using software and computer configurations of similar nature for my studies), as it is quite difficult for me to evaluate the performance gain of "real-life" software (i.e. science oriented in my case) on new hardware before buying.From what I have seen in your code segments provided (especially for the n-body simulation part) , there are large amount of floating-point divisions. Is there any possibility that the code is not only limited by the cache size(and thrashing), but by the limited throughput of the floating-point divider? (i.e. The performance degradations when HT is enabled may also be caused by the competition of the two running threads on the only floating-point divider in the core)
SanX - Tuesday, January 8, 2013 - link
if you post zipped sources and exes for anyone to follow, learn, play, argue and eventually improve.I'd also preferred to see Fortran sources and benchmarks when possible.
Intel/AMD should start promote 2/4/8 socket monster mobos for enthusiasts and then general public since this is the beginning of the infinite in time era for multiprocessing.
Also where are games benchmarks like for example GTA4 which benefits a lot from multicores as well as from GPUs?
IanCutress - Wednesday, January 9, 2013 - link
The n-body simulations are part of the C++ AMP example page, free for everyone to use. The rest of the code is part of a benchmark package I'm creating, hence I only give the loops in the code. Unfortunately I know no Fortran for benchmarks.Most mainstream users (i.e. gamers) still debate whether 4 or 6 cores are even necessary, so moving to 2P/4P/8P is a big leap in that regard. Enthusiasts can still get the large machines (a few folders use quad AMD setups) if they're willing to buy from ebay which may not always be wholly legal. You may see 2P/4P/8P becoming more mainstream when we start to hit process node limits.
Ian