As part of this review, we also ran our normal motherboard benchmarks.

WinRAR x64 3.93 - link

With 64-bit WinRAR, we compress the set of files used in the USB speed tests. WinRAR x64 3.93 attempts to use multithreading when possible, and provides as a good test for when a system has variable threaded load.  If a system has multiple speeds to invoke at different loading, the switching between those speeds will determine how well the system will do.

WinRar x64 3.93

WinRAR is another example where enabling HyperThreading is actually hurting the throughput of the system. But even with all 32 threads in the system, the lack of memory speed hurts the benchmark.

FastStone Image Viewer 4.2 - link

FastStone Image Viewer is a free piece of software I have been using for quite a few years now.  It allows quick viewing of flat images, as well as resizing, changing color depth, adding simple text or simple filters.  It also has a bulk image conversion tool, which we use here.  The software currently operates only in single-thread mode, which should change in later versions of the software.  For this test, we convert a series of 170 files, of various resolutions, dimensions and types (of a total size of 163MB), all to the .gif format of 640x480 dimensions.

FastStone Image Viewer 4.2

FastStone is relatively unaffected due to the single-threaded nature of the program.

Xilisoft Video Converter

With XVC, users can convert any type of normal video to any compatible format for smartphones, tablets and other devices.  By default, it uses all available threads on the system, and in the presence of appropriate graphics cards, can utilize CUDA for NVIDIA GPUs as well as AMD APP for AMD GPUs.  For this test, we use a set of 33 HD videos, each lasting 30 seconds, and convert them from 1080p to an iPod H.264 video format using just the CPU.  The time taken to convert these videos gives us our result.

Xilisoft Video Converter 7

With XVC having many threads is what wins the day, and having HT enabled made the process very fast indeed.  With HT on, we have 32 threads, meaning most of the videos were actually converted very quickly – the final 33rd video caused an extra delay at the end.  This is yet another example of an algorithm that can be ported to GPUs, as XVC offers both an AMD and NVIDIA option for conversion.

x264 HD Benchmark

The x264 HD Benchmark uses a common HD encoding tool to process an HD MPEG2 source at 1280x720 at 3963 Kbps.  This test represents a standardized result which can be compared across other reviews, and is dependant on both CPU power and memory speed.  The benchmark performs a 2-pass encode, and the results shown are the average of each pass performed four times.

x264 HD Pass 1

x264 HD Pass 2

In contrast to XVC, which splits its threads across many files, the x264 HD benchmark splits threads across one file.  As a result it seems that having HT off gives a subtle 13.7% boost in performance in the first pass and 11.0% boost in the second pass.  The results of the first pass makes the second pass a lot more efficient across all the threads due to fewer memory accesses.

n-Body Simulations System Benchmarks
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  • Hakon - Saturday, January 5, 2013 - link

    Thank you for the detailed answer. I very much appreciate your article and hope to see more stuff like this on Anandtech.

    What I meant regarding to NUMA is the following. When you have a dual socket Xeon you have two memory controllers. The first time you 'touch' a memory location it is assigned to the memory controller of the CPU that runs the current thread. This assignment is in general permanent and all further memory read/writes to that location will be served by that memory controller.

    If you first-touch (e.g. initialize the array to zero) using one thread, then the whole array is assigned to one of the two memory controllers. When you then run the multi-threaded code on that array one memory controller is idle while the other is oversubscribed since it has to serve both CPUs.

    In contrast, if you first-touch your array in an OpenMP loop and use the same access pattern as in the algorithm, you will benefit from both memory controllers later on. In this case your large array is correctly 'distributed' over both memory controllers.

    This kind of memory layout optimization becomes extremely important when you deal with quad socket Opterons. You then have eight memory controllers. A NUMA aware code is therefore up to eight times as fast since it utilizes all memory controllers.
  • toyotabedzrock - Saturday, January 5, 2013 - link

    You should go ask the people on the assembly boards for help with making your code faster.

    They are very friendly compared to a Linux kernel devs, I think they just enjoy the acknowledgement that they still exist and are useful.
  • snajpa - Saturday, January 5, 2013 - link

    Blame the scheduler. Neither Windows nor linux can effectively handle larger NUMA systems. It randomly moves the process across the physical hardware.
  • psyq321 - Sunday, January 6, 2013 - link

    Hmm, this is definitely not true at least for Windows Server 2008 R2 / Windows 7, and I am sure it holds true for some versions of Linux (I am not a Linux expert).

    Windows Server 2008 R2 / Windows 7 scheduler will try to match the memory allocations (even if they are not tagged for a specific NUMA node) with the NUMA node the process/thread resides on, and they will not move a thread to a foreign NUMA node unless if that has been explicitly requested by the application (by setting the thread affinity)

    Of course, without explicit NUMA node tagging when doing allocations, application code is the main culprit for not respecting the NUMA layout (e.g. creating bunch of threads, allocating memory from one of them - and then pinning the threads to different CPUs - you will have lots of LLC requests from remote DRAM because memory was a-priori allocated on one node).

    For this - some sane coding helps a lot, here:

    http://www.dimkovic.com/node/15

    I describe how I extracted more than double performance by careful memory allocation (NUMA-aware) - please note that neither Windows nor Linux scheduler is able to cope with code which is not written to be NUMA aware and it is using large number of threads that are supposed to run on all CPUs.. Simply put, application writer will have to manage memory allocation and usage in the way so that there are as little remote DRAM requests as possible.
  • snajpa - Sunday, January 6, 2013 - link

    About Windows scheduler - I only worked with Windows XP, now I don't have any reason to work with Win anymore, so what you say probably is really true. As for the linux versions - well, long story short, CFS sucks and everyone knows it - this is particularly noticeable if you have fully virtualized VMs which appear as one single process at the host system - the process is randomly swapped between CPU cores and even CPU dies.... sad story. That's why people have to pin their CPUs to their tasks manually.
  • psyq321 - Sunday, January 6, 2013 - link

    Ah, XP - that explains it. True, XP did not care about NUMA at all.

    Windows Server 2008 / Vista introduced NUMA-aware memory allocations, and changed their CPU scheduler so it does not move the thread across NUMA nodes. They will also try to allocate the memory from the thread's own NUMA node when legacy VirtualAlloc etc. APIs are used.

    Windows Server 2008 R2 / Windows 7 introduced the concept of CPU groups - allowing more than 64 CPUs. This does require some adaptation of the application, as old threading APIs only work with 64-bit affinity bitmask which only allowed recognizing 64 CPUs. Now, there is a new set of APIs that work with GROUP_AFFINITY structure, allowing control of CPU groups, too. However, this needs explicit change of the legacy process/threading APIs to the new ones.

    Furthermore, none of the above can replace some manual intervention*- while Windows scheduler will, indeed, respect NUMA node boundaries and not try to mess around with moving threads across them - it still does not know what the underlying algorithm wants to do.

    * There is no need to set the thread affinity to one specific CPU anymore - this prevents running the thread on any other CPU completely. Instead, there is an API called SetThreadIdealProcessor(Ex) which signals Windows scheduler that thread >should< run on that particular CPU - but, under certain circumstances the scheduler can move the thread somewhere else - if the CPU is completely taken away by some other thread/process. Scheduler will try to move the thread as close as possible - to the next core in the socket, for example - or to the next core in the group (group is always contained within a NUMA node).

    You can, however, absolutely forbid Windows scheduler from passing the thread to another NUMA node under any circumstances by simply getting the said NUMA node affinity mask (GetNumaNodeProcessorMask(Ex)) and setting this affinity as a thread affinity. This + setting the "ideal" processor still gives Windows scheduler some headroom to move the thread to another core if it is found to be better in a given moment, but it will not even attempt to cross the NUMA boundary in any case whatsoever.
  • lmcd - Monday, January 7, 2013 - link

    While I haven't personally researched them, there are tons of other schedulers that have been written for Linux and I'm certain *at least* one of them is more fitting to this line of work. I've heard of alternatives like BFS and the Linux kernel is so widely used I'm sure there's a gem out there for this application.
  • toyotabedzrock - Saturday, January 5, 2013 - link

    Have you ever tried the Intel Math Kernel Library? It might speed up some of the equations. It also hands off work to the Intel MIC card if it thinks it will speed it up.

    http://software.intel.com/en-us/intel-mkl/
  • KAlmquist - Saturday, January 5, 2013 - link

    The GA-7PESH1 motherboard is $855, and the CPU's are $2020 each, which adds up to $4895. On tasks which don't parallelize well, you can get similar performance from the i7-3770K, which costs an order of magnitude less. (Prices: i7-3770K $320, ASRock Z77 Extreme6 motherboard $152, total from motherboard and CPU $472.) On tasks which parallelize well enough that they can be run on a GPU, the system with the GA-7PESH1 will beat the i7-3770K, but will be crushed by a midrange GPU. So the price/performance of this system is pretty bad unless you throw just the right workload at it.

    The motherboard price from super-laptop-parts dot com, and the other prices are from a major online retailer that I won't name in order to get around the spam filter.
  • Death666Angel - Saturday, January 5, 2013 - link

    So, your 3770K has ECC memory or VT-D, TXT etc.?

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