Performance Metrics

As of release 24.11, for each release, USD generates performance metrics using a specific set of assets and specific hardware and software configurations. This page describes what metrics are collected, what hardware and software configurations are used, the actual metrics results, and how to generate the metrics locally.

What We Measure

For a given asset, our performance script captures the following metrics by default (items in bold are reported on this page):

• Time to load and configure USD plugins for usdview

• Time to open the stage

• Time to reset the usdview prim browser

• Time to initialize the usdview UI

• Time to render first image (in usdview)

• Time to shutdown Hydra

• Time to close the stage

• Time to tear down the usdview UI

• Total time to start and quit usdview

• Time to traverse the prims in the stage

We run 100 iterations for each asset, and capture the minimum and maximum times for that set of iterations. We also calculate the mean time across the 100 iterations.

For each asset, we first warm the filesystem cache by loading the asset in usdview, to ensure we’re not including cache performance issues in our metrics.

All assets used to measure performance are assumed to be available locally. Time to download assets is not measured or included as part of the gathered performance metrics.

What Environment Is Used

This section describes the computing environment used to generate the published performance metrics. The following operating systems and hardware are currently used.

Note

Machine specifications are subject to change. If specifications do change for a give release, historical performance measurements will be run to backfill any outdated data.

Linux

• OS: AlmaLinux 9

• CPU: AMD EPYC 9654 96-Core Processor

• CPU Utilization: 47 Core(s), 47 Logical Processor(s) (no hyperthreading)

• RAM: 145GB

• GPU: NVIDIA L40S-24Q

macOS

• OS: macOS 14.3

• CPU: Apple M2 Ultra (20 Core)

• RAM: 192GB

• GPU: Apple M2 Ultra GPU (76 Core)

Windows

• OS: Microsoft Windows 11 Enterprise

• CPU: AMD EPYC 7763 64-Core Processor, 2450 Mhz

• CPU Utilization: 31 Core(s), 31 Logical Processor(s) (no hyperthreading)

• RAM: 128GB

• GPU: NVIDIA RTXA6000-24Q

USD Build

For each of the operating systems and hardware platforms listed previously, we build USD with the same build configuration. We use a stock invocation of build_usd.py with the default options (release build, Python components, imaging and USD imaging components, usdview, etc). Note that this build uses the default system memory allocator, and does not use an alternate allocator (as described in Use an allocator optimized for multithreading).

Metrics

Metrics are all measured in seconds.

Performance Graphs Per Platform

The following graphs show the time (in seconds) to open and close usdview for each asset. Graphs are provided for Linux, macOS, and Windows platforms (as described in What Environment Is Used). Performance data from the four most recent releases is reported on a rolling basis.

Note

A full historical rerun on linux was performed for the 26.03 release due to changes in machine configuration.

Interpret the metrics on this page with caution. Variation may arise from a variety of sources, including run-to-run inconsistency in how the GL driver schedules its flushes against our requests. Investigation on these is ongoing.

Standard Shader Ball

This asset is designed to be a comprehensive test of a broad array of material properties in a single render. Geometry is expressed using USD, materials are defined using MaterialX, texture maps are provided in OpenEXR format and encoded using the Academy Color Encoding System ACEScg color space.

In our performance sweep we use the mtlx_bubblegum material. Note the image below may not exactly match the image generated during the performance run; the primary intention is not necessarily to exercise specific renderers.

The shader ball asset can be downloaded here.

Linux Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.098586 max: 0.152453 mean: 0.1147789	min: 0.102143 max: 0.141323 mean: 0.12018247	min: 0.108347 max: 0.142785 mean: 0.12522259	min: 0.125322 max: 0.148907 mean: 0.13320966
Render first image	min: 1.061915 max: 4.389524 mean: 1.11305927	min: 1.05858 max: 4.813072 mean: 1.11026738	min: 1.132672 max: 5.047174 mean: 1.18685452	min: 1.110248 max: 7.867876 mean: 1.19898046
Close stage	min: 0.000612 max: 0.007621 mean: 0.0009525	min: 0.000585 max: 0.00771 mean: 0.00113363	min: 0.000627 max: 0.00862 mean: 0.00108197	min: 0.000657 max: 0.008608 mean: 0.00118314
Shut down Hydra	min: 0.035256 max: 0.044038 mean: 0.03787955	min: 0.035166 max: 0.041641 mean: 0.03717791	min: 0.038391 max: 0.046654 mean: 0.04102367	min: 0.038988 max: 0.049061 mean: 0.04176199

macOS Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.096033 max: 0.106589 mean: 0.10036488	min: 0.124269 max: 0.135039 mean: 0.1273308	min: 0.12139 max: 0.129204 mean: 0.12521404	min: 0.13547 max: 0.146209 mean: 0.13819121
Render first image	min: 1.088926 max: 1.434863 mean: 1.11545414	min: 0.743375 max: 1.198365 mean: 0.76448214	min: 0.787785 max: 1.314961 mean: 0.80603199	min: 0.407376 max: 0.898428 mean: 0.41666772
Close stage	min: 0.000119 max: 0.000491 mean: 0.00016517	min: 0.000121 max: 0.000378 mean: 0.00017008	min: 0.000138 max: 0.000498 mean: 0.0001761	min: 0.000148 max: 0.000533 mean: 0.00018699
Shut down Hydra	min: 0.012247 max: 0.020553 mean: 0.01637388	min: 0.012409 max: 0.020834 mean: 0.01706989	min: 0.012981 max: 0.022092 mean: 0.01737945	min: 0.009067 max: 0.015838 mean: 0.01351388

Windows Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.298701 max: 0.315642 mean: 0.30742671	min: 0.263552 max: 0.277434 mean: 0.27102602	min: 0.261372 max: 0.281605 mean: 0.27079815	min: 0.271862 max: 0.289156 mean: 0.28077484
Render first image	min: 2.184459 max: 8.745092 mean: 2.3044740200000002	min: 2.140281 max: 2.4015 mean: 2.23789929	min: 2.275701 max: 8.259477 mean: 2.43007551	min: 2.215232 max: 13.666641 mean: 2.40498329
Close stage	min: 0.000316 max: 0.001709 mean: 0.00068915	min: 0.00033 max: 0.000791 mean: 0.00044066	min: 0.000341 max: 0.000752 mean: 0.00045084	min: 0.000374 max: 0.000939 mean: 0.0004813
Shut down Hydra	min: 0.060228 max: 0.069912 mean: 0.06285791	min: 0.06344 max: 0.071558 mean: 0.06660486	min: 0.069612 max: 0.07776 mean: 0.07274253	min: 0.06478 max: 0.074391 mean: 0.06826856

Kitchen Set

This asset provides a complex kitchen scene.

The Kitchen Set asset can be downloaded here.

Linux Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.043523 max: 0.08078 mean: 0.0529231	min: 0.048359 max: 0.090553 mean: 0.05856095	min: 0.047789 max: 0.076912 mean: 0.05592291	min: 0.052998 max: 0.08932 mean: 0.0620716
Render first image	min: 0.218023 max: 0.233961 mean: 0.22471585	min: 0.217668 max: 0.243507 mean: 0.22705873	min: 0.218671 max: 0.237097 mean: 0.22637969	min: 0.232486 max: 0.265385 mean: 0.24223985
Close stage	min: 0.008055 max: 0.020271 mean: 0.00992871	min: 0.008124 max: 0.021591 mean: 0.01004208	min: 0.008197 max: 0.02467 mean: 0.01009473	min: 0.008709 max: 0.025533 mean: 0.01088784
Shut down Hydra	min: 0.034244 max: 0.040128 mean: 0.03604341	min: 0.033784 max: 0.038803 mean: 0.03593167	min: 0.030789 max: 0.0356 mean: 0.03314213	min: 0.028225 max: 0.032347 mean: 0.03045488

macOS Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.061154 max: 0.084453 mean: 0.0677701	min: 0.085585 max: 0.109018 mean: 0.09204828	min: 0.081513 max: 0.114839 mean: 0.0883299	min: 0.092551 max: 0.113511 mean: 0.10160631
Render first image	min: 0.259431 max: 0.27281 mean: 0.26522798000000003	min: 0.259301 max: 0.273705 mean: 0.26457672	min: 0.261286 max: 0.274061 mean: 0.26642707	min: 0.254831 max: 0.315395 mean: 0.26072938
Close stage	min: 0.002038 max: 0.003781 mean: 0.00244283	min: 0.002021 max: 0.003675 mean: 0.00247759	min: 0.001925 max: 0.00337 mean: 0.00238511	min: 0.002041 max: 0.003517 mean: 0.00242477
Shut down Hydra	min: 0.017222 max: 0.02856 mean: 0.023692190000000002	min: 0.01651 max: 0.031314 mean: 0.02202539	min: 0.015542 max: 0.026238 mean: 0.02142616	min: 0.013821 max: 0.024521 mean: 0.02036446

Windows Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.105351 max: 0.120111 mean: 0.11219626	min: 0.10763 max: 0.123955 mean: 0.11461558	min: 0.099474 max: 0.116492 mean: 0.10618527	min: 0.108297 max: 0.124659 mean: 0.11513973
Render first image	min: 0.387114 max: 0.430436 mean: 0.40021584	min: 0.387877 max: 0.417364 mean: 0.39946686	min: 0.385847 max: 0.415152 mean: 0.39539667	min: 0.387191 max: 0.417565 mean: 0.39784305
Close stage	min: 0.01869 max: 0.028976 mean: 0.02328194	min: 0.019835 max: 0.032572 mean: 0.02471053	min: 0.018316 max: 0.029745 mean: 0.02298045	min: 0.018545 max: 0.028232 mean: 0.022193459999999998
Shut down Hydra	min: 0.045518 max: 0.05392 mean: 0.049226980000000004	min: 0.045756 max: 0.054098 mean: 0.04928151	min: 0.041524 max: 0.04916 mean: 0.04623087	min: 0.03841 max: 0.046465 mean: 0.04293331

ALab

ALab is a full production scene created by Animal Logic and contains over 300 assets, complete with high-quality textures and two characters with looping animation in shot context. Supplied as four separate downloads: the full production scene, high-quality textures, shot cameras matching the ALab trailer, and baked procedural fur and fabric for the animated characters.

The metrics have been measured with the base asset merged with the additional “techvars” info.

The ALab asset can be downloaded here.

Linux Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.262824 max: 0.381295 mean: 0.3155681	min: 0.278403 max: 0.377748 mean: 0.3208941	min: 0.241601 max: 0.318423 mean: 0.27954225	min: 0.2802 max: 0.376605 mean: 0.32259145
Render first image	min: 6.034976 max: 6.376652 mean: 6.20103818	min: 6.002758 max: 6.374273 mean: 6.17849757	min: 5.810938 max: 6.169416 mean: 5.93609673	min: 6.129386 max: 6.508681 mean: 6.31419199
Close stage	min: 0.10802 max: 0.130206 mean: 0.11329438	min: 0.107958 max: 0.13805 mean: 0.11300532	min: 0.106509 max: 0.130405 mean: 0.1123268	min: 0.11729 max: 0.148149 mean: 0.12301499
Shut down Hydra	min: 0.530892 max: 0.882976 mean: 0.65899259	min: 0.542178 max: 0.844146 mean: 0.65962838	min: 0.563382 max: 0.868353 mean: 0.70578134	min: 0.787496 max: 0.966843 mean: 0.8351985

macOS Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.387289 max: 0.476831 mean: 0.41920031	min: 0.401131 max: 0.479708 mean: 0.43101433	min: 0.374424 max: 0.477764 mean: 0.42913736	min: 0.314381 max: 0.548683 mean: 0.38178418
Render first image	min: 3.374587 max: 3.796386 mean: 3.59016131	min: 3.247161 max: 3.697333 mean: 3.51718546	min: 3.196071 max: 3.900934 mean: 3.59978127	min: 3.225269 max: 4.021977 mean: 3.64870661
Close stage	min: 0.032904 max: 0.046616 mean: 0.03683789	min: 0.03243 max: 0.044327 mean: 0.0365513	min: 0.032126 max: 0.050107 mean: 0.03739121	min: 0.027333 max: 0.037311 mean: 0.03149304
Shut down Hydra	min: 0.142813 max: 0.177915 mean: 0.15281977	min: 0.135475 max: 0.170974 mean: 0.15313856	min: 0.140115 max: 0.166896 mean: 0.15124781	min: 0.132456 max: 0.161829 mean: 0.1425976

Windows Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.388077 max: 0.416403 mean: 0.4029927	min: 0.397166 max: 0.587801 mean: 0.41254627	min: 0.378314 max: 0.523401 mean: 0.39922649	min: 0.384181 max: 0.418874 mean: 0.40105012
Render first image	min: 7.782884 max: 8.019821 mean: 7.89220199	min: 7.648056 max: 8.347713 mean: 7.87855149	min: 7.585018 max: 8.511921 mean: 7.9203183600000004	min: 7.882253 max: 8.234776 mean: 7.9930347
Close stage	min: 0.317541 max: 0.405188 mean: 0.35938741	min: 0.340327 max: 0.432633 mean: 0.38996225	min: 0.317643 max: 0.496103 mean: 0.3783046	min: 0.340219 max: 0.401597 mean: 0.36910621
Shut down Hydra	min: 0.329635 max: 0.572838 mean: 0.44994447	min: 0.351016 max: 0.632485 mean: 0.50048855	min: 0.377651 max: 0.643099 mean: 0.52348612	min: 0.349316 max: 0.618448 mean: 0.4948539

Moore Lane

4004 Moore Lane is a fully composed, high-quality scene for the purpose of testing various visual computing issues. The house itself was wrapped around a number of typical problem areas for light transport and noise sampling. This includes things like thin openings in exterior walls, recessed area light sources, deeply shadowed corners, and high-frequency details. The exterior landscape surrounding the house consisted of a relatively simple ecosystem of instanced plants which could provide some additional levels of complexity. In addition to the geometry itself being designed to exacerbate some typical issues, the USD structure itself was created for several layers of testing.

The metrics have been measured using the contained MooreLane_ASWF_0623.usda file.

The Moore Lane asset can be downloaded here.

Linux Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.045243 max: 0.061459 mean: 0.04774144	min: 0.051093 max: 0.059576 mean: 0.05292827	min: 0.049433 max: 0.068931 mean: 0.05256769	min: 0.055556 max: 0.075718 mean: 0.05865626
Render first image	min: 7.024242 max: 7.330487 mean: 7.1917083	min: 7.106522 max: 7.491846 mean: 7.30991904	min: 7.113698 max: 7.396277 mean: 7.21710089	min: 7.015587 max: 7.460851 mean: 7.29381541
Close stage	min: 0.034029 max: 0.038338 mean: 0.03618818	min: 0.033988 max: 0.038964 mean: 0.03618142	min: 0.034322 max: 0.041997 mean: 0.03567777	min: 0.041617 max: 0.05171 mean: 0.04280516
Shut down Hydra	min: 0.997761 max: 1.145262 mean: 1.0445001	min: 1.001618 max: 1.119256 mean: 1.04177241	min: 0.796864 max: 1.011868 mean: 0.83473395	min: 0.648444 max: 1.225414 mean: 0.99355296

macOS Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.0779 max: 0.083264 mean: 0.07919203	min: 0.103792 max: 0.115776 mean: 0.10550355	min: 0.098452 max: 0.103779 mean: 0.09996708	min: 0.107984 max: 0.113351 mean: 0.10950673
Render first image	min: 4.928251 max: 5.818517 mean: 5.27123588	min: 4.825569 max: 5.729761 mean: 5.17014079	min: 4.78398 max: 5.725201 mean: 5.1418585	min: 4.742426 max: 5.715785 mean: 5.17064111
Close stage	min: 0.003165 max: 0.008126 mean: 0.0034966899999999998	min: 0.003239 max: 0.007467 mean: 0.00347463	min: 0.003191 max: 0.008941 mean: 0.00384508	min: 0.003213 max: 0.008801 mean: 0.00360833
Shut down Hydra	min: 0.121073 max: 0.137621 mean: 0.12819004	min: 0.121472 max: 0.188133 mean: 0.13085061	min: 0.104195 max: 0.171836 mean: 0.12383931	min: 0.119972 max: 0.163464 mean: 0.12754035

Windows Metrics (min/max/mean in seconds)

Metric	25.11	26.03	26.05	26.08
Open stage	min: 0.135837 max: 0.148618 mean: 0.14251247	min: 0.125819 max: 0.141703 mean: 0.1328517	min: 0.116097 max: 0.131661 mean: 0.12227848	min: 0.123235 max: 0.160999 mean: 0.13125285
Render first image	min: 8.113206 max: 9.48835 mean: 8.81574719	min: 7.307756 max: 9.21315 mean: 8.49179167	min: 8.443403 max: 9.740621 mean: 9.20294465	min: 8.515424 max: 10.411091 mean: 9.12166075
Close stage	min: 0.094986 max: 0.124853 mean: 0.10156746999999999	min: 0.096086 max: 0.127534 mean: 0.1035804	min: 0.095105 max: 0.121288 mean: 0.10279636	min: 0.095786 max: 0.13272 mean: 0.10288532
Shut down Hydra	min: 0.321071 max: 0.470964 mean: 0.38557374	min: 0.354215 max: 0.457584 mean: 0.41048186	min: 0.349249 max: 0.474159 mean: 0.41690432	min: 0.349943 max: 0.460264 mean: 0.41932649

Running Performance Metrics Locally

We encourage developers to run the USD performance metrics to measure performance impacts of OpenUSD code contributions. Performance metrics can also be run to validate local runtime environments and hardware configurations.

Performance metrics are generating using the usdmeasureperformance.py script found in pxr/extras/performance. See the usdmeasureperformance tool docs for more information on the different parameters available.

usdmeasureperformance.py uses usdview and testusdview, so you will need to make sure those are in your current path, or aliased properly.

For gathering the metrics published on this page, the following parameters are used (for each asset):

python usdmeasureperformance.py <asset.usda> -i 100 -a min -o <metrics output filename.yaml>

Optionally, –tracedir <dir> will output trace information to dir that may aid in performance debugging.

Adding Custom Metrics

You can add your own custom metrics and have usdmeasureperformance.py include them as part of the set of metrics that it measures.

To define a custom metric, create a script file that defines a testUsdviewInputFunction() function that will be passed to testusdview. For example, if you wanted to add a metric named “process prims”, that traversed the stage and processed each prim in some way, you might have a processPrimsMetric.py script that looks something like:

from pxr import Usd, UsdUtils, Usdviewq

def testUsdviewInputFunction(appController):
    with Usdviewq.Timer("process prims", True):
        stage = appController._dataModel.stage
        for prim in stage.Traverse():
            # process prim as needed, etc

See also the “traverse stage” example in pxr/extras/performance/explicitMetrics/stageTraversalMetric.py.

To include your custom metrics when running usdmeasureperformance.py, add your metrics script name and metric name as part of the --custom-metrics script parameter. For example, if you wanted to include the “process prims” metric example for “MyTestAsset.usda”, you would use a usdmeasureperformance.py command line similar to:

python usdmeasureperformance.py MyTestAsset.usda --custom-metrics processPrimsMetric.py:'process prims'

usdmeasureperformance.py will look for your custom metric script relative to the directory from which the usdmeasureperformance.py script is run.