CPU Tests: Encoding

One of the interesting elements on modern processors is encoding performance. This covers two main areas: encryption/decryption for secure data transfer, and video transcoding from one video format to another.

In the encrypt/decrypt scenario, how data is transferred and by what mechanism is pertinent to on-the-fly encryption of sensitive data - a process by which more modern devices are leaning to for software security.

Video transcoding as a tool to adjust the quality, file size and resolution of a video file has boomed in recent years, such as providing the optimum video for devices before consumption, or for game streamers who are wanting to upload the output from their video camera in real-time. As we move into live 3D video, this task will only get more strenuous, and it turns out that the performance of certain algorithms is a function of the input/output of the content.

HandBrake 1.32: Link

Video transcoding (both encode and decode) is a hot topic in performance metrics as more and more content is being created. First consideration is the standard in which the video is encoded, which can be lossless or lossy, trade performance for file-size, trade quality for file-size, or all of the above can increase encoding rates to help accelerate decoding rates. Alongside Google's favorite codecs, VP9 and AV1, there are others that are prominent: H264, the older codec, is practically everywhere and is designed to be optimized for 1080p video, and HEVC (or H.265) that is aimed to provide the same quality as H264 but at a lower file-size (or better quality for the same size). HEVC is important as 4K is streamed over the air, meaning less bits need to be transferred for the same quality content. There are other codecs coming to market designed for specific use cases all the time.

Handbrake is a favored tool for transcoding, with the later versions using copious amounts of newer APIs to take advantage of co-processors, like GPUs. It is available on Windows via an interface or can be accessed through the command-line, with the latter making our testing easier, with a redirection operator for the console output.

We take the compiled version of this 16-minute YouTube video about Russian CPUs at 1080p30 h264 and convert into three different files: (1) 480p30 ‘Discord’, (2) 720p30 ‘YouTube’, and (3) 4K60 HEVC.

(5-1a) Handbrake 1.3.2, 1080p30 H264 to 480p Discord(5-1b) Handbrake 1.3.2, 1080p30 H264 to 720p YouTube(5-1c) Handbrake 1.3.2, 1080p30 H264 to 4K60 HEVC

7-Zip 1900: Link

The first compression benchmark tool we use is the open-source 7-zip, which typically offers good scaling across multiple cores. 7-zip is the compression tool most cited by readers as one they would rather see benchmarks on, and the program includes a built-in benchmark tool for both compression and decompression.

The tool can either be run from inside the software or through the command line. We take the latter route as it is easier to automate, obtain results, and put through our process. The command line flags available offer an option for repeated runs, and the output provides the average automatically through the console. We direct this output into a text file and regex the required values for compression, decompression, and a combined score.

(5-2c) 7-Zip 1900 Combined Score

AES Encoding

Algorithms using AES coding have spread far and wide as a ubiquitous tool for encryption. Again, this is another CPU limited test, and modern CPUs have special AES pathways to accelerate their performance. We often see scaling in both frequency and cores with this benchmark. We use the latest version of TrueCrypt and run its benchmark mode over 1GB of in-DRAM data. Results shown are the GB/s average of encryption and decryption.

(5-3) AES Encoding

WinRAR 5.90: Link

For the 2020 test suite, we move to the latest version of WinRAR in our compression test. WinRAR in some quarters is more user friendly that 7-Zip, hence its inclusion. Rather than use a benchmark mode as we did with 7-Zip, here we take a set of files representative of a generic stack

  • 33 video files , each 30 seconds, in 1.37 GB,
  • 2834 smaller website files in 370 folders in 150 MB,
  • 100 Beat Saber music tracks and input files, for 451 MB

This is a mixture of compressible and incompressible formats. The results shown are the time taken to encode the file. Due to DRAM caching, we run the test for 20 minutes times and take the average of the last five runs when the benchmark is in a steady state.

For automation, we use AHK’s internal timing tools from initiating the workload until the window closes signifying the end. This means the results are contained within AHK, with an average of the last 5 results being easy enough to calculate.

(5-4) WinRAR 5.90 Test, 3477 files, 1.96 GB

CPU Tests: Rendering CPU Tests: Legacy and Web
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  • TheinsanegamerN - Tuesday, November 10, 2020 - link

    However AMD's boost algorithim is very temperature sensitive. Those coolers may work fine, but if they get to the 70C range you're losing max performance to higher temperatures.
  • Andrew LB - Sunday, December 13, 2020 - link

    Blah blah....

    Ryzen 5800x @ 3.6-4.7ghz : 219w and 82'c.
    Ryzen 5800x @ 4.7ghz locked: 231w and 88'c.

    Fractal Celsius+ S28 Prisma 280mm AIO CPU cooler at full fan and pump speed
    https://www.kitguru.net/components/cpu/luke-hill/a...

    If you actually set your voltages on Intel chips they stay cool. My i7-10700k @ 5.0ghz all-core locked never goes above 70'c.
  • Count Rushmore - Friday, November 6, 2020 - link

    It took 3 days... finally the article load-up.
    AT seriously need to upgrade their server (or I need to stop using IE6).
  • name99 - Friday, November 6, 2020 - link

    "AMD wouldn’t exactly detail what this means but we suspect that this could allude to now two branch predictions per cycle instead of just one"

    So imagine you have wide OoO CPU. How do you design fetch? The current state of the art (and presumably AMD have aspects of this, though perhaps not the *entire* package) goes as follows:

    Instructions come as runs of sequential instructions separated by branches. At a branch you may HAVE to fetch instructions from a new address (think call, goto, return) or you may perhaps continue to the next address (think non-taken branch).
    So an intermediate complexity fetch engine will bring in blobs of instructions, up to (say 6 or 8) with the run of instructions terminating at
    - I've scooped up N or
    - I've hit a branch or
    - I've hit the end of a cache line.

    Basically every cycle should consist of pulling in the longest run of instructions possible subject to the above rules.

    The way really advanced fetch works is totally decoupled from the rest of the CPU. Every cycle the fetch engine predicts the next fetch address (from some hierarchy of : check the link stack, check the BTB, increment the PC), and fetches as much as possible from that address. These are stuck in a queue connected to decode, and ideally that queue would never run dry.

    BUT: on average there is about a branch every 6 instructions.
    Now supposed you want to sustain, let's say, 8-wide. That means that you might set N at 8, but most of the time you'll fetch 6 or so instructions because you'll bail out based on hitting a branch before you have a full 8 instructions in your scoop. So you're mostly unable to go beyond an IPC of 6, even if *everything* else is ideal.

    BUT most branches are conditional. And good enough half of those are not taken. This means that if you can generate TWO branch predictions per cycle then much of the time the first branch will not be taken, can be ignored, and fetch can continue in a straight line past it. Big win! Half the time you can pull in only 6 instructions, but the other half you could pull in maybe 12 instructions. Basically, if you want to sustain 8 wide, you'd probably want to pull in at least 10 or 12 instructions under best case conditions, to help fill up the queue for the cases where you pull in less than 8 instructions (first branch is taken, or you reach the end of the cache line).

    Now there are some technicalities here.
    One is "how does fetch know where the branches are, to know when to stop fetching". This is usually done via pre-decode bits living in the I-cache, and set by a kinda decode when the line is first pulled into the I-cache. (I think x86 also does this, but I have no idea how. It's obviously much easier for a sane ISA like ARM, POWER, even z.)
    Second, and more interesting, is that you're actually performing two DIFFERENT TYPES of prediction, which makes it somewhat easier from a bandwidth point of view. The prediction on the first branch is purely "taken/not taken", and all you care about is "not taken"; the prediction on the second branch is more sophisticated because if you predict taken you also have to predict the target, which means dealing BTB or link stack.

    But you don't have to predict TWO DIFFERENT "next fetch addresses" per cycle, which makes it somewhat easier.
    Note also that any CPU that uses two level branch prediction is, I think, already doing two branch prediction per cycle, even if it doesn't look like it. Think about it: how do you USE a large (but slow) second level pool of branch prediction information?
    You run the async fetch engine primarily from the first level; and this gives a constant stream of "runs of instructions, separated by branches" with zero delay cycles between runs. Great, zero cycle branches, we all want that. BUT for the predictors to generate a new result in a single cycle they can't be too large.
    So you also run a separate engine, delayed a cycle or two, based on the larger pool of second level branch data, checking the predictions of the async engine. If there's a disagreement you flush whatever was fetched past that point (which hopefully is still just in the fetch queue...) and resteer. This will give you a one (or three or four) cycle bubble in the fetch stream, which is not ideal, but
    - it doesn't happen that often
    - it's a lot better catching a bad prediction very early in fetch, rather than much later in execution
    - hopefully the fetch queue is full enough, and filled fast enough, that perhaps it's not even drained by the time decode has walked along it to the point at which the re-steer occurred...

    This second (checking) branch prediction doesn't ever get mentioned, but it is there behind the scenes, even when the CPU is ostensibly doing only a single prediction per cycle.

    There are other crazy things that happen in modern fetch engines (which are basically in themselves as complicated as a whole CPU from 20 years ago).

    One interesting idea is to use the same data that is informing the async fetch engine to inform prefetch. The idea is that you now have essentially two fetch engines running. One is as I described above; the second ONLY cares about the stream of TAKEN branches, and follows that stream as rapidly as possible, ensuring that each line referenced by this stream is being pulled into the I-cache. (You will recognize this as something like a very specialized form of run-ahead.)
    In principle this should be perfect -- the I prefetcher and branch-prediction are both trying to solve the *exact* same problem, so pooling their resources should be optimal! In practice, so far this hasn't yet been perfected; the best simulations using this idea are a very few percent behind the best simulations using a different I prefetch technology. But IMHO this is mostly a consequence of this being a fairly new idea that has so far been explored mainly by using pre-existing branch predictors, rather than designing a branch predictor store that's optimal for both tasks.
    The main difference is that what matters for prefetching is "far future" branches, branches somewhat beyond where I am now, so that there's plenty of time to pull in the line all the way from RAM. And existing branch predictors have had no incentive to hold onto that sort of far future prediction state. HOWEVER
    A second interesting idea is what IBM has been doing for two or three years now. They store branch prediction in what they call an L2 storage but, to avoid things, I'll cal a cold cache. This is stale/far future branch prediction data that is unused for a while but, on triggering events, that cold cache data will be swapped into the branch prediction storage so that the branch predictors are ready to go for the new context in which they find themselves.

    I don't believe IBM use this to drive their I-prefetcher, but obviously it is a great solution to the problem I described above and I suspect this will be where all the performance CPUs eventually find themselves over the next few years. (Apple and IBM probably first, because Apple is Apple, and IBM has the hard part of the solution already in place; then ARM because they's smart and trying hard; then AMD because they're also smart but their technology cycles are slower than ARM; and final Intel because, well, they're Intel and have been running on fumes for a few years now.)
    (Note of course this only solves I-prefetch, which is nice and important; but D-prefetch remains as a difficult and different problem.)
  • name99 - Friday, November 6, 2020 - link

    Oh, one more thing. I referred to "width" of the CPU above. This becomes an ever vaguer term every year. The basic points are two:

    - when OoO started, it seemed reasonable to scale every step of the pipeline together. Make the CPU 4-wide. So it can fetch up to 4 instructions/cycle. decode up to 4, issue up to 4, retire up to 4. BUT if you do this you're losing performance every step of the way. Every cycle that fetches only 3 instructions can never make that up; likewise every cycle that only issues 3 instructions.

    - so once you have enough transistors available for better designs, you need to ask yourself what's the RATE-LIMITING step? For x86 that's probably in fetch and decode, but let's consider sane ISAs like ARM. There the rate limiting step is probably register rename. So lets assume your max rename bandwidth is 6 instructions/cycle. You actually want to run the rest of your machinery at something like 7 or 8 wide because (by definition) you CAN do so (they are not rate limiting, so they can be grown). And by running them wider you can ensure that the inevitable hiccups along the way are mostly hidden by queues, and your rename machinery is running at full speed, 6-wide each and every cycle, rather than frequently running at 5 or 4 wide because of some unfortunate glitch upstream.
  • Spunjji - Monday, November 9, 2020 - link

    These were interesting posts. Thank you!
  • GeoffreyA - Monday, November 9, 2020 - link

    Yes, excellent posts. Thanks.

    Touching on width, I was expecting Zen 3 to add another decoder and take it up to 5-wide decode (like Skylake onwards). Zen 3's keeping it at 4 makes good sense though, considering their constraint of not raising power. Another decoder might have raised IPC but would have likely picked up power quite a bit.
  • ignizkrizalid - Saturday, November 7, 2020 - link

    Rip Intel no matter how hard you try squeezing Intel sometimes on top within your graphics! stupid site bias and unreliable if this site was to be truth why not do a live video comparison side by side using 3600 or 4000Mhz ram so we can see the actual numbers and be 100% assured the graphic table is not manipulated in any way, yea I know you will never do it! personally I don't trust these "reviews" that can be manipulated as desired, I respect live video comparison with nothing to hide to the public. Rip Intel Rip Intel.
  • Spunjji - Monday, November 9, 2020 - link

    I... don't think this makes an awful lots of sense, tbh.
  • MDD1963 - Saturday, November 7, 2020 - link

    It would be interesting to also see the various results of the 10900K the way most people actually run them on Z490 boards, i.e, with higher RAM clocks, MCE enabled, etc...; do the equivalent tuning with 5000 series, I'm sure they will run with faster than DDR4-3200 MHz. plus perhaps a small all-core overclock.

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