$300 CPU Beats $4000 CPU?? - Cores vs clockspeed for video encoding
Linus Tech Tips
·Linus Tech Tips
·2017-05-06
·
1,489 words · ~7 min read
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okay do you remember that project I was working on where for the better part of
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6 months I built up this badass 36 core
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dual xon server machine to handle our video encoding and transcoding tasks
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over the network here well fast forward almost a year and many many hours spent
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on diagnosis not to mention a kick in the right direction from this post over
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on Puget systems I think I finally figured out why we never got quite the
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performance that I expected is it
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possible then that a $4,000 22 core CPU could be outperformed
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by one that costs only a few hundred bucks for video encoding is it possible
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that I made a mistake nothing to hold on
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to fails a lot if they reading the sign I'm definitely getting their attention
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so does one of the recurring themes of these laptop or bus videos
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become line failure
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montages I I mean aside from those
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ones let's find
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click now at the link in the video description to try it for
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free Okay so to open this video up we need to take a closer than usual look at
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my test bench I wanted to eliminate bottlenecks wherever possible so that
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the CPU is the only factor in my
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performance evaluation so for that reason most of the performance testing
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was done on an Intel 750 series 1.2 TB
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ndme SSD a GTX Titan x 128 gigs of ddr4
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quad Channel memory on an x99 Deluxe 2 motherboard and the CPUs tested are as
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follows Intel's top of the server line
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2699 V4 22 coron they top of the
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high-end desktop line 10 core core i7
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extreme 6950x the 8 core and 6 core 6900k and
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6800k and finally I decided to throw in their Flagship mainstream 6700k quad
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core to give us the most complete picture possible at the end of the day
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as for the video tests I apologize in advance if the codec or encoder
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application that you personally prefer wasn't covered but this was done as much
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to optimize the Linus Media Group workflow as it was for the purposes of
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creating a video so I'm looking at four different scenarios that we encounter
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pretty much daily one transcoding a 4K
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mxf off of our Sony fs5 to 1080p copor
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our mezzanine codec of choice for editing two exporting a finished project
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in this case a green screened episode as fast as possible directly to h264 for
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publication to YouTube Three a quick
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export in cfor how we normally export so that a network media encoder machine
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with a watch folder can transcode it to h264 and automatically upload it to the
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channel and four finally the performance
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of that copor to h264 conversion with
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the 1080p to 4K upsampling that we perform for the reasons we covered more
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thoroughly in this video here so I ran
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every test with and without Cuda acceleration enabled in Adobe Media
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encoder and used a second machine to capture the screen output with CPU and
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GPU usage displayed so I could review it later let's begin then with scenario one
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this is what most people probably expect
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from a multicore CPU in a video encoding
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Benchmark traditionally this is one of the easiest workloads to scale AC
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crossmore course and our CPU usage graph
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indicates that all is working beautifully throwing a GPU into the mix
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levels the playing field somewhat but this won't surprise anyone who knows how
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GPU dependent a video Codec cineform is and how that bastard law of diminishing
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returns Works moving on to exporting a
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project directly from our cfor timeline
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in CPU only mode we see nice scaling
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with more cores but maybe not quite the
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dominance we'd expect from a chip with and yes I know it doesn't quite work
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this way like 60 GHz of theoretical
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total performance this is a hint of
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things to come and Bam throwing a GPU
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into the mix paints a much more extreme picture here the Cuda accelerated code
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path not only reaps very little benefit
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from more than six cores it punishes
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CPUs with lower clock speed in a way that I really didn't expect observed GPU
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usage is much lower than any other
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processor in this test for our $4,000
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chip and the CPU usage we see of about
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25% tells us this is not a heavily
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threaded workload oops all right so let's break that down
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then into the individual steps and find out where our heavy multi thousand
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investment in an Uber Zeon falls apart
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exporting the project from a cfor 1080p timeline to a cineform 1080p file
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theoretically Elsewhere on the network but I'm using my NVMe drive as a target
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for these benchmarks for consistency sake is pretty flat across the board and
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curiously this is true with or without Cuda acceleration enabled in media
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encoder GPU usage is 85% regardless of
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which drop down so this is clearly nearly 100% GPU dependent which leads us
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then to the second step in the process converting from cineform 1080 to h264 4K
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in CP mode only we see a similar Trend
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to our initial injest test more horses
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is better but only to a point then in GPU assisted mode there it is we are
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almost entirely Bound by per core performance with a lowly quad core
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costing one/ tenth as much handily beating our Zeon be
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so then did I horribly misconfigure our video encoding injest stations and
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output server our Zeon basically pointless NVIDIA
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work well if you're looking simply at the graphs I just showed you along with
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these charts of approximate CPU and GPU
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usage in all the different scenarios I tested then it's pretty clear that these
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lower clocked many core chips are being
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underutilized and the money though I fortunately didn't pay for them would be
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better invested almost anywhere else but
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as always the real world isn't really that simple and it's going to come down
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to the needs and workflow of each individual or organization
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virtualization can be used to get damn near 100% scaling out of as many cores
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as you please encoding software like Sor and squeeze can process many files at a
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time and on the subject of different software testing any given codec in any
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given soft software could yield very different results from what you're
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looking at here so there's no way around
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testing just make sure that when you do so for yourself you go in without any
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assumptions about what the right tool for the job will end up being so you can
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avoid pulling a Linus speaking of tools for the job it's
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