Sizing Up Servers: Intel's Skylake-SP Xeon versus AMD's EPYC 7000 - The Server CPU Battle of the Decade?
by Johan De Gelas & Ian Cutress on July 11, 2017 12:15 PM EST- Posted in
- CPUs
- AMD
- Intel
- Xeon
- Enterprise
- Skylake
- Zen
- Naples
- Skylake-SP
- EPYC
Apache Spark 2.1 Benchmarking
Apache Spark is the poster child of Big Data processing. Speeding up Big Data applications is the top priority project at the university lab I work for (Sizing Servers Lab of the University College of West-Flanders), so we produced a benchmark that uses many of the Spark features and is based upon real world usage.
The test is described in the graph above. We first start with 300 GB of compressed data gathered from the CommonCrawl. These compressed files are a large amount of web archives. We decompress the data on the fly to avoid a long wait that is mostly storage related. We then extract the meaningful text data out of the archives by using the Java library "BoilerPipe". Using the Stanford CoreNLP Natural Language Processing Toolkit, we extract entities ("words that mean something") out of the text, and then count which URLs have the highest occurrence of these entities. The Alternating Least Square algorithm is then used to recommend which URLs are the most interesting for a certain subject.
In previous articles, we tested with Spark 1.5 in standalone mode (non-clustered). That worked out well enough, but we saw diminishing returns as core counts went up. In hindsight, just dumping 300 GB of compressed data in one JVM was not optimal for 30+ core systems. The high core counts of the Xeon 8176 and EPYC 7601 caused serious performance issues when we first continued to test this way. The 64 core EPYC 7601 performed like a 16-core Xeon, the Skylake-SP system with 56 cores was hardly better than a 24-core Xeon E5 v4.
So we decided to turn our newest servers into virtual clusters. Our first attempt is to run with 4 executors. Researcher Esli Heyvaert also upgraded our Spark benchmark so it could run on the latest and greatest version: Apache Spark 2.1.1.
Here are the results:
If you wonder who needs such server behemoths besides the people who virtualize a few dozen virtual machines, the answer is Big Data. Big Data crunching has an unsatisfiable hunger for – mostly integer – processing power. Even on our fastest machine, this test needs about 4 hours to finish. It is nothing less than a killer app.
Our Spark benchmark needs about 120 GB of RAM to run. The time spent on storage I/O is negligible. Data processing is very parallel, but the shuffle phases require a lot of memory interaction. The ALS phase does not scale well over many threads, but is less than 4% of the total testing time.
Given the higher clockspeed in lightly threaded and single threaded parts, the faster shuffle phase probably gives the Intel chip an edge of only about 5%.
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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 basissuch 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!
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.
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.
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.Shankar1962 - Monday, July 17, 2017 - link
Don't go by the labs idiotUnderstand what real world workloads are.....understand what owning an entire rack means ......you started foul language so you deserve the same respect from me......
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.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.
mdw9604 - Tuesday, July 11, 2017 - link
Nice, comprehensive article. Glad to see AMD is competitive once again in the server CPU space.nathanddrews - Tuesday, July 11, 2017 - link
"Competitive" seems like an understatement, but yes, AMD is certainly bringing it!ddriver - Tuesday, July 11, 2017 - link
Yeah, offering pretty much double the value is so barely competitive LOL.