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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  • psychobriggsy - Tuesday, July 11, 2017 - link

    Indeed it is a ridiculous comment, and puts the earlier crying about the older Ubuntu and GCC into context - just an Intel Fanboy.

    In fact Intel's core architecture is older, and GCC has been tweaked a lot for it over the years - a slightly old GCC might not get the best out of Skylake, but it will get a lot. Zen is a new core, and GCC has only recently got optimisations for it.
    Reply
  • EasyListening - Wednesday, July 12, 2017 - link

    I thought he was joking, but I didn't find it funny. So dumb.... makes me sad. Reply
  • blublub - Tuesday, July 11, 2017 - link

    I kinda miss Infinity Fabric on my Haswell CPU and it seems to only have on die - so why is that missing on Haswell wehen Ryzen is an exact copy? Reply
  • blublub - Tuesday, July 11, 2017 - link

    Your actually sound similar to JuanRGA at SA Reply
  • Kevin G - Wednesday, July 12, 2017 - link

    @CajunArson The cache hierarchy is radically different between these designs as well as the port arrangement for dispatch. Scheduling on Ryzen is split between execution resources where as Intel favors a unified approach. Reply
  • bill.rookard - Tuesday, July 11, 2017 - link

    Well, that is something that could be figured out if they (anandtech) had more time with the servers. Remember, they only had a week with the AMD system, and much like many of the games and such, optimizing is a matter of run test, measure, examine results, tweak settings, rinse and repeat. Considering one of the tests took 4 hours to run, having only a week to do this testing means much of the optimization is probably left out.

    They went with a 'generic' set of relative optimizations in the interest of time, and these are the (very interesting) results.
    Reply
  • CoachAub - Wednesday, July 12, 2017 - link

    Benchmarks just need to be run on as level as a field as possible. Intel has controlled the market so long, software leans their way. Who was optimizing for Opteron chips in 2016-17? ;) Reply
  • theeldest - Tuesday, July 11, 2017 - link

    The compiler used isn't meant to be the the most optimized, but instead it's trying to be representative of actual customer workloads.

    Most customer applications in normal datacenters (not google, aws, azure, etc) are running binaries that are many years behind on optimizations.

    So, yes, they can get better performance. But using those optimizations is not representative of the market they're trying to show numbers for.
    Reply
  • CajunArson - Tuesday, July 11, 2017 - link

    That might make a tiny bit of sense if most of the benchmarks run were real-world workloads and not C-Ray or POV-Ray.

    The most real-world benchmark in the whole setup was the database benchmark.
    Reply
  • coder543 - Tuesday, July 11, 2017 - link

    The one benchmark that favors Intel is the "most real-world"? Absolutely, I want AnandTech to do further testing, but your comments do not sound unbiased. Reply

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