okay do you remember that project I was working on where for the better part of
6 months I built up this badass 36 core
dual xon server machine to handle our video encoding and transcoding tasks
over the network here well fast forward almost a year and many many hours spent
on diagnosis not to mention a kick in the right direction from this post over
on Puget systems I think I finally figured out why we never got quite the
performance that I expected is it
possible then that a $4,000 22 core CPU could be outperformed
by one that costs only a few hundred bucks for video encoding is it possible
that I made a mistake nothing to hold on
to fails a lot if they reading the sign I'm definitely getting their attention
so does one of the recurring themes of these laptop or bus videos
become line failure
montages I I mean aside from those
ones let's find
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free Okay so to open this video up we need to take a closer than usual look at
my test bench I wanted to eliminate bottlenecks wherever possible so that
the CPU is the only factor in my
performance evaluation so for that reason most of the performance testing
was done on an Intel 750 series 1.2 TB
ndme SSD a GTX Titan x 128 gigs of ddr4
quad Channel memory on an x99 Deluxe 2 motherboard and the CPUs tested are as
follows Intel's top of the server line
2699 V4 22 coron they top of the
high-end desktop line 10 core core i7
extreme 6950x the 8 core and 6 core 6900k and
6800k and finally I decided to throw in their Flagship mainstream 6700k quad
core to give us the most complete picture possible at the end of the day
as for the video tests I apologize in advance if the codec or encoder
application that you personally prefer wasn't covered but this was done as much
to optimize the Linus Media Group workflow as it was for the purposes of
creating a video so I'm looking at four different scenarios that we encounter
pretty much daily one transcoding a 4K
mxf off of our Sony fs5 to 1080p copor
our mezzanine codec of choice for editing two exporting a finished project
in this case a green screened episode as fast as possible directly to h264 for
publication to YouTube Three a quick
export in cfor how we normally export so that a network media encoder machine
with a watch folder can transcode it to h264 and automatically upload it to the
channel and four finally the performance
of that copor to h264 conversion with
the 1080p to 4K upsampling that we perform for the reasons we covered more
thoroughly in this video here so I ran
every test with and without Cuda acceleration enabled in Adobe Media
encoder and used a second machine to capture the screen output with CPU and
GPU usage displayed so I could review it later let's begin then with scenario one
this is what most people probably expect
from a multicore CPU in a video encoding
Benchmark traditionally this is one of the easiest workloads to scale AC
crossmore course and our CPU usage graph
indicates that all is working beautifully throwing a GPU into the mix
levels the playing field somewhat but this won't surprise anyone who knows how
GPU dependent a video Codec cineform is and how that bastard law of diminishing
returns Works moving on to exporting a
project directly from our cfor timeline
in CPU only mode we see nice scaling
with more cores but maybe not quite the
dominance we'd expect from a chip with and yes I know it doesn't quite work
this way like 60 GHz of theoretical
total performance this is a hint of
things to come and Bam throwing a GPU
into the mix paints a much more extreme picture here the Cuda accelerated code
path not only reaps very little benefit
from more than six cores it punishes
CPUs with lower clock speed in a way that I really didn't expect observed GPU
usage is much lower than any other
processor in this test for our $4,000
chip and the CPU usage we see of about
25% tells us this is not a heavily
threaded workload oops all right so let's break that down
then into the individual steps and find out where our heavy multi thousand
investment in an Uber Zeon falls apart
exporting the project from a cfor 1080p timeline to a cineform 1080p file
theoretically Elsewhere on the network but I'm using my NVMe drive as a target
for these benchmarks for consistency sake is pretty flat across the board and
curiously this is true with or without Cuda acceleration enabled in media
encoder GPU usage is 85% regardless of
which drop down so this is clearly nearly 100% GPU dependent which leads us
then to the second step in the process converting from cineform 1080 to h264 4K
in CP mode only we see a similar Trend
to our initial injest test more horses
is better but only to a point then in GPU assisted mode there it is we are
almost entirely Bound by per core performance with a lowly quad core
costing one/ tenth as much handily beating our Zeon be
so then did I horribly misconfigure our video encoding injest stations and
output server our Zeon basically pointless NVIDIA
work well if you're looking simply at the graphs I just showed you along with
these charts of approximate CPU and GPU
usage in all the different scenarios I tested then it's pretty clear that these
lower clocked many core chips are being
underutilized and the money though I fortunately didn't pay for them would be
better invested almost anywhere else but
as always the real world isn't really that simple and it's going to come down
to the needs and workflow of each individual or organization
virtualization can be used to get damn near 100% scaling out of as many cores
as you please encoding software like Sor and squeeze can process many files at a
time and on the subject of different software testing any given codec in any
given soft software could yield very different results from what you're
looking at here so there's no way around
testing just make sure that when you do so for yourself you go in without any
assumptions about what the right tool for the job will end up being so you can
avoid pulling a Linus speaking of tools for the job it's
summer apparently something something boarding Planes Trains driving a car
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