CPU Performance: System Tests

Our System Test section focuses significantly on real-world testing, user experience, with a slight nod to throughput. In this section we cover application loading time, image processing, simple scientific physics, emulation, neural simulation, optimized compute, and 3D model development, with a combination of readily available and custom software. For some of these tests, the bigger suites such as PCMark do cover them (we publish those values in our office section), although multiple perspectives is always beneficial. In all our tests we will explain in-depth what is being tested, and how we are testing.

All of our benchmark results can also be found in our benchmark engine, Bench.

Application Load: GIMP 2.10.4

One of the most important aspects about user experience and workflow is how fast does a system respond. A good test of this is to see how long it takes for an application to load. Most applications these days, when on an SSD, load fairly instantly, however some office tools require asset pre-loading before being available. Most operating systems employ caching as well, so when certain software is loaded repeatedly (web browser, office tools), then can be initialized much quicker.

In our last suite, we tested how long it took to load a large PDF in Adobe Acrobat. Unfortunately this test was a nightmare to program for, and didn’t transfer over to Win10 RS3 easily. In the meantime we discovered an application that can automate this test, and we put it up against GIMP, a popular free open-source online photo editing tool, and the major alternative to Adobe Photoshop. We set it to load a large 50MB design template, and perform the load 10 times with 10 seconds in-between each. Due to caching, the first 3-5 results are often slower than the rest, and time to cache can be inconsistent, we take the average of the last five results to show CPU processing on cached loading.

AppTimer: GIMP 2.10.4

We saw a slight regression here with the 10980XE, which may be down to some of the security updates given that this benchmark tests loading a program which can involve user mode changes.

3D Particle Movement v2.1: Brownian Motion

Our 3DPM test is a custom built benchmark designed to simulate six different particle movement algorithms of points in a 3D space. The algorithms were developed as part of my PhD., and while ultimately perform best on a GPU, provide a good idea on how instruction streams are interpreted by different microarchitectures.

A key part of the algorithms is the random number generation – we use relatively fast generation which ends up implementing dependency chains in the code. The upgrade over the naïve first version of this code solved for false sharing in the caches, a major bottleneck. We are also looking at AVX2 and AVX512 versions of this benchmark for future reviews.

For this test, we run a stock particle set over the six algorithms for 20 seconds apiece, with 10 second pauses, and report the total rate of particle movement, in millions of operations (movements) per second. We have a non-AVX version and an AVX version, with the latter implementing AVX512 and AVX2 where possible.

3DPM v2.1 can be downloaded from our server: 3DPMv2.1.rar (13.0 MB)

3D Particle Movement v2.1

3D Particle Movement v2.1 (with AVX)

The extra frequency shows a bit here in MT mode, but otherwise equal performance to the 9980XE.

Dolphin 5.0: Console Emulation

One of the popular requested tests in our suite is to do with console emulation. Being able to pick up a game from an older system and run it as expected depends on the overhead of the emulator: it takes a significantly more powerful x86 system to be able to accurately emulate an older non-x86 console, especially if code for that console was made to abuse certain physical bugs in the hardware.

For our test, we use the popular Dolphin emulation software, and run a compute project through it to determine how close to a standard console system our processors can emulate. In this test, a Nintendo Wii would take around 1050 seconds.

The latest version of Dolphin can be downloaded from https://dolphin-emu.org/

Dolphin 5.0 Render Test

DigiCortex 1.20: Sea Slug Brain Simulation

This benchmark was originally designed for simulation and visualization of neuron and synapse activity, as is commonly found in the brain. The software comes with a variety of benchmark modes, and we take the small benchmark which runs a 32k neuron / 1.8B synapse simulation, equivalent to a Sea Slug.

Example of a 2.1B neuron simulation

We report the results as the ability to simulate the data as a fraction of real-time, so anything above a ‘one’ is suitable for real-time work. Out of the two modes, a ‘non-firing’ mode which is DRAM heavy and a ‘firing’ mode which has CPU work, we choose the latter. Despite this, the benchmark is still affected by DRAM speed a fair amount.

DigiCortex can be downloaded from http://www.digicortex.net/

DigiCortex 1.20 (32k Neuron, 1.8B Synapse)

y-Cruncher v0.7.6: Microarchitecture Optimized Compute

I’ve known about y-Cruncher for a while, as a tool to help compute various mathematical constants, but it wasn’t until I began talking with its developer, Alex Yee, a researcher from NWU and now software optimization developer, that I realized that he has optimized the software like crazy to get the best performance. Naturally, any simulation that can take 20+ days can benefit from a 1% performance increase! Alex started y-cruncher as a high-school project, but it is now at a state where Alex is keeping it up to date to take advantage of the latest instruction sets before they are even made available in hardware.

For our test we run y-cruncher v0.7.6 through all the different optimized variants of the binary, single threaded and multi-threaded, including the AVX-512 optimized binaries. The test is to calculate 250m digits of Pi, and we use the single threaded and multi-threaded versions of this test.

Users can download y-cruncher from Alex’s website: http://www.numberworld.org/y-cruncher/

y-Cruncher 0.7.6 Single Thread, 250m Digits

Agisoft Photoscan 1.3.3: 2D Image to 3D Model Conversion

One of the ISVs that we have worked with for a number of years is Agisoft, who develop software called PhotoScan that transforms a number of 2D images into a 3D model. This is an important tool in model development and archiving, and relies on a number of single threaded and multi-threaded algorithms to go from one side of the computation to the other.

In our test, we take v1.3.3 of the software with a good sized data set of 84 x 18 megapixel photos and push it through a reasonably fast variant of the algorithms, but is still more stringent than our 2017 test. We report the total time to complete the process.

Agisoft’s Photoscan website can be found here: http://www.agisoft.com/

Agisoft Photoscan 1.3.3, Complex Test

The 10980XE here becomes the fastest Intel CPU we've tested on Photoscal, with a sizeable uplift over the 9980XE. This is likely due to the faster memory.

CPU Performance: Rendering Tests CPU Performance: Encoding Tests
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  • Santoval - Monday, November 25, 2019 - link

    Wait for the prices of both to adjust first.
  • Drumsticks - Monday, November 25, 2019 - link

    I don't care about process nodes, as long as they're delivering competitive prices, core counts, and performance per core. Intel's not quite out of the game yet since AMD's HEDT goes higher than Intel's, but they've gotten smashed at the halo spot, and they won't be able to deliver on price and performance if they can't get something in order.
  • Braincruser - Tuesday, November 26, 2019 - link

    No they haven't been "smashed at the halo spot". The 3900X and 3950X are both beasts and both shred in most of the important benchmarks. For video rendering both the 3900x and 3950X hand out with both the threadrippers and the intels. You get 90% of the performance for 1/4th the price. 12-16 cores is also a very important number for programmers, since you have enough CPUs for compiling, and running 2-3 VMs comfortably.
  • Dolda2000 - Monday, November 25, 2019 - link

    Why is it that Intel gains so incredibly much more from AVX512 than AMD gains from AVX2?

    In the 3DPM2 test, the AMD CPUs gain roughly a factor of two in performance, which is exactly what I'd expect given that AVX2 is twice as wide as standard SSE. The Intel CPUs, on the other hand, gain almost a factor of 9, which is more than twice what I'd expect given that AVX512 as four times as wide as SSE.

    What causes this? Does AVX512 have some other kind of tricks up its sleeves? Does opmasking benefit 3DPM2?
  • Xyler94 - Monday, November 25, 2019 - link

    Basically, AVX-512 is double the performance of AVX2 (or another way to see it, 256bit vs 512bits, which 512 is double 256). So anything optimized for 512 will be about double in speed from 256, even on the exact same processor.
  • Xyler94 - Monday, November 25, 2019 - link

    To note: That's a highly overly simplistic view of it, there's a lot more under the hood.
  • eek2121 - Monday, November 25, 2019 - link

    Well that and the obvious point that AMD CPUs do not support AVX-512.
  • DanNeely - Monday, November 25, 2019 - link

    AVX-2 is 256 bits wide, and thus only does have as much/instruction as AVX-512.
  • JayNor - Monday, November 25, 2019 - link

    I believe for 10 cores and up there are dual avx512 units per core. You can see the dual avx512 units in the Execution Engine diagram at this link.
    https://en.wikichip.org/wiki/intel/microarchitectu...

    Also, cascade lake added dlboost 8 bit operations in avx512 to support ai inference convolutions.
  • Dolda2000 - Monday, November 25, 2019 - link

    But Zen 1/2 also has two 256-bit FMAs per core. And Intel also has two SSE units per core as well, so I don't see how that would explain the ratios.

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