Floating Point

Normally our HPC benchmarking is centered around OpenFoam, a CFD software we have used for a number of articles over the years. However, since we moved to Ubuntu 16.04, we could not get it to work anymore. So we decided to change our floating point intensive benchmark for now. For our latest article, we're testing with C-ray, POV-Ray, and NAMD.

The idea is to measure:

  1. A FP benchmark that is running out of the L1 (C-ray)
  2. A FP benchmark that is running out of the L2 (POV-Ray)
  3. And one that is using the memory subsytem quite often (NAMD)

Floating Point: C-ray

C-ray is an extremely simple ray-tracer which is not representative of any real world raytracing application. In fact, it is essentially a floating point benchmark that runs out of the L1-cache. Luckily it is not as synthetic and meaningless as Whetstone, as you can actually use the software to do simple raytracing.

We use the standard benchmarking resolution (3840x2160) and the "sphfract" file to measure performance. The binary was precompiled.

C-ray rendering at 3840x2160

Wow. What just happened? It looks like a landslide victory for the raw power of the four FP pipes of Zen: the EPYC chip is no less than 50% faster than the competition. Of course, it is easy to feed FP units if everything resides in the L1. Next stop, POV-Ray.

Floating Point: POV-Ray 3.7

The Persistence of Vision Raytracer (POV-Ray) is a well known open source raytracer. We compiled our version based upon the version that can be found on github (https://github.com/POV-Ray/povray.git). No special optimizations were done, we used "prebuild.sh", configure, make, and make install.

Povray

POV-Ray is known to run mostly out of the L2-cache, so the massive DRAM bandwidth of the EPYC CPU does not play a role here. Nevertheless, the EPYC CPU performance is pretty stunning: about 16% faster than Intel's Xeon 8176. But what if AVX and DRAM access come in to play? Let us check out NAMD.

Floating Point: NAMD

Developed by the Theoretical and Computational Biophysics Group at the University of Illinois Urbana-Champaign, NAMD is a set of parallel molecular dynamics codes for extreme parallelization on thousands of cores. NAMD is also part of SPEC CPU2006 FP. In contrast with previous FP benchmarks, the NAMD binary is compiled with Intel ICC and optimized for AVX.

First, we used the "NAMD_2.10_Linux-x86_64-multicore" binary. We used the most popular benchmark load, apoa1 (Apolipoprotein A1). The results are expressed in simulated nanoseconds per wall-clock day. We measure at 500 steps.

NAMD molecular dynamics

Again, the EPYC 7601 simply crushes the competition with 41% better performance than Intel's 28-core. Heavily vectorized code (like Linpack) might run much faster on Intel, but other FP code seems to run faster on AMD's newest FPU.

For our first shot with this benchmark, we used version 2.10 to be able to compare to our older data set. Version 2.12 seems to make better use of "Intel's compiler vectorization and auto-dispatch has improved performance for Intel processors supporting AVX instructions". So let's try again:

NAMD molecular dynamics 2.12

The older Xeons see a perforance boost of about 25%. The improvement on the new Xeons is a lot lower: about 13-15%. Remarkable is that the new binary is slower on the EPYC 7601: about 4%. That simply begs for more investigation: but the deadline was too close. Nevertheless, three different FP tests all point in the same direction: the Zen FP unit might not have the highest "peak FLOPs" in theory, there is lots of FP code out there that runs best on EPYC.

Big Data benchmarking Energy Consumption
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  • StargateSg7 - Sunday, August 6, 2017 - link

    Maybe I'm spoiled, but to me a BIG database is something I usually deal with on a daily basis
    such as 500,000 large and small video files ranging from two megabytes to over a PETABYTE
    (1000 Terabytes) per file running on a Windows and Linux network.

    What sort of read and write speeds do we get between disk, main memory and CPU
    and when doing special FX LIVE on such files which can be 960 x 540 pixel youtube-style
    videos up to full blown 120 fps 8192 x 4320 pixel RAW 64 bits per pixel colour RGBA files
    used for editing and video post-production.

    AND I need for the smaller files, total I/O-transaction rates at around
    OVER 500,000 STREAMS of 1-to-1000 64 kilobyte unique packets
    read and written PER SECOND. Basically 500,000 different users
    simultaneously need up to one thousand 64 kilobyte packets per
    second EACH sent to and read from their devices.

    Obviously Disk speed and network comm speed is an issue here, but on
    a low-level hardware basis, how much can these new Intel and AMD chips
    handle INTERNALLY on such massive data requirements?

    I need EXABYTE-level storage management on a chip! Can EITHER
    Xeon or EPyC do this well? Which One is the winner? ... Based upon
    this report it seems multiple 4-way EPyC processors on waterblocked
    blades could be racked on a 100 gigabit (or faster) fibre backbone
    to do 500,000 simultaneous users at a level MUCH CHEAPER than
    me having to goto IBM or HP for a 30+ million dollar HPC solution!
    Reply
  • PixyMisa - Tuesday, July 11, 2017 - link

    It seems like a well-balanced article to me. Sure the DB performance issue is a corner case, but from a technical point of view its worth knowing.

    I'd love to see a test on a larger database (tens of GB) though.
    Reply
  • philehidiot - Wednesday, July 12, 2017 - link

    It seems to me that some people should set up their own server review websites in order that they might find the unbiased balance that they so crave. They might also find a time dilation device that will allow them to perform the multitude of different workload tests they so desire. I believe this article stated quite clearly the time constraints and the limitations imposed by such constraints. This means that the benchmarks were scheduled down to the minute to get as many in as possible and therefore performing different tests based on the results of the previous benchmarks would have put the entire review dataset in jeopardy.

    It might be nice to consider just how much data has been acquired here, how it might have been done and the degree of interpretation. It might also be worth considering, if you can do a better job, setting up shop on your own and competing as obviously the standard would be so much higher.

    Sigh.
    Reply
  • JohanAnandtech - Thursday, July 13, 2017 - link

    Thank you for being reasonable. :-) Many of the benchmarks (Tinymembench, Stream, SPEC) etc. can be repeated, so people can actually check that we are unbiased. Reply
  • Shankar1962 - Monday, July 17, 2017 - link

    Don't go by the labs idiot
    Understand what real world workloads are.....understand what owning an entire rack means ......you started foul language so you deserve the same respect from me......
    Reply
  • roybotnik - Wednesday, July 12, 2017 - link

    EPYC looks extremely good here aside from the database benchmark, which isn't a useful benchmark anyways. Need to see the DB performance with 100GB+ of memory in use. Reply
  • CarlosYus - Friday, July 14, 2017 - link

    A detailed and unbiased article. I'm awaiting for more tests as testing time passes.
    3.2 Ghz is a moderate Turbo for AMD EPYC, I think AMD could push it further with a higher thermal envelope i/o 14 nm process improvement in the coming months.
    Reply
  • mdw9604 - Tuesday, July 11, 2017 - link

    Nice, comprehensive article. Glad to see AMD is competitive once again in the server CPU space. Reply
  • nathanddrews - Tuesday, July 11, 2017 - link

    "Competitive" seems like an understatement, but yes, AMD is certainly bringing it! Reply
  • ddriver - Tuesday, July 11, 2017 - link

    Yeah, offering pretty much double the value is so barely competitive LOL. Reply

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