BenchmarkXPRT Blog banner

Tag Archives: Windows

Planning for the next TouchXPRT

We’re in the very early planning stages for the next version of TouchXPRT, and we’d love to hear any suggestions you may have. What do you like or dislike about TouchXPRT? What features do you hope to see in a new version?

For those who are unfamiliar with TouchXPRT, it’s a benchmark for evaluating the performance of Windows 10 devices. TouchXPRT 2016, the most recent version, runs tests based on five everyday scenarios (Beautify Photos, Blend Photos, Convert Videos for Sharing, Create Music Podcast, and Create Slideshow from Photos) and produces results for each of the five scenarios plus an overall score. The benchmark is available two ways: as a Universal Windows App in the Microsoft Store and as a sideload installer package on

When we begin work on a new version of any benchmark, one of the first steps we take is to assess its workloads to determine whether they will provide value during the years ahead. This step involves evaluating whether to update test content such as photos and videos to more contemporary file resolutions and sizes, and can also involve removing workloads or adding completely new ones. Should we keep the TouchXPRT workloads listed above or investigate other use cases? Should we research potential AI-related workloads? What do you think?

As we did with MobileXPRT 3 and HDXPRT 4 earlier this year, we’re also planning to update the TouchXPRT UI to improve the look of the benchmark and make it easier to use. We’re just at the beginning of this process, so any feedback you send has a chance to really shape the future of the benchmark.

On a related note, TouchXPRT 2016 testers who use the installer package available on may have noticed that the package has a new file name ( Microsoft requires developers to assign a security certificate to all sideload apps, and the new TouchXPRT file contains a refreshed certificate. We did not change the benchmark in any other way, so scores from this package are comparable to previous TouchXPRT 2016 scores.


Understanding AIXPRT results

Last week, we discussed the changes we made to the AIXPRT Community Preview 2 (CP2) download page as part of our ongoing effort to make AIXPRT easier to use. This week, we want to discuss the basics of understanding AIXPRT results by talking about the numbers that really matter and how to access and read the actual results files.

To understand AIXPRT results at a high level, it’s important to revisit the core purpose of the benchmark. AIXPRT’s bundled toolkits measure inference latency (the speed of image processing) and throughput (the number of images processed in a given time period) for image recognition (ResNet-50) and object detection (SSD-MobileNet v1) tasks. Testers have the option of adjusting variables such as batch size (the number of input samples to process simultaneously) to try and achieve higher levels of throughput, but higher throughput can come at the expense of increased latency per task. In real-time or near real-time use cases such as performing image recognition on individual photos being captured by a camera, lower latency is important because it improves the user experience. In other cases, such as performing image recognition on a large library of photos, achieving higher throughput might be preferable; designating larger batch sizes or running concurrent instances might allow the overall workload to complete more quickly.

The dynamics of these performance tradeoffs ensure that there is no single good score for all machine learning scenarios. Some testers might prefer lower latency, while others would sacrifice latency to achieve the higher level of throughput that their use case demands.

Testers can find latency and throughput numbers for each completed run in a JSON results file in the AIXPRT/Results folder. The test also generates CSV results files that are in the same folder. The raw results files report values for each AI task configuration (e.g., ResNet-50, Batch1, on CPU). Parsing and consolidating the raw data can take some time, so we’re developing a results file parsing tool to make the job much easier.

The results parsing tool is currently available in the AIXPRT CP2 OpenVINO – Windows package, and we hope to make it available for more packages soon. Using the tool is as simple as running a single command, and detailed instructions for how to do so are in the AIXPRT OpenVINO on Windows user guide. The tool produces a summary (example below) that makes it easier to quickly identify relevant comparison points such as maximum throughput and minimum latency.

AIXPRT results summary

In addition to the summary, the tool displays the throughput and latency results for each AI task configuration tested by the benchmark. AIXPRT runs each AI task multiple times and reports the average inference throughput and corresponding latency percentiles.

AIXPRT results details

We hope that this information helps to make it easier to understand AIXPRT results. If you have any questions or comments, please feel free to contact us.


A new HDXPRT 4 build is available!

A few weeks ago, we announced that a new HDXPRT 4 build, v1.1, was on the way. This past Monday, we published the build on

The new build includes an updated version of HandBrake, the commercial application that HDXPRT uses for certain video conversion tasks. HandBrake 1.2.2 supports hardware acceleration with AMD Video Coding Engine (VCE), Intel Quick Sync, and the NVIDIA video encoder (NVENC). By default, HDXPRT4 v1.1 uses the encoder available through a system’s integrated graphics, but testers can target discrete graphics by changing a configuration file flag before running the benchmark. HDXPRT will then use the encoder provided by the discrete graphics hardware. This configuration setting takes effect only when more than one of the supported encoders (VCE, QSV, or NVENC) is present on the system.

As we mentioned before, in all other respects, the benchmark has not changed. That means that, apart from a scenario where a tester changes the targeted graphics hardware, scores from previous HDXPRT 4 builds will be comparable to those from the new build.

The updated HDXPRT 4 User Manual contains additional information and instructions for changing the configuration file flag. Please contact us if you have any questions about the new build. Happy testing!


Making AIXPRT easier to use

We’re glad to see so much interest in the AIXPRT CP2 build. Over the past few days, we’ve received two questions about the setup process: 1) where to find instructions for setting up AIXPRT on Windows, and 2) whether we could make it easier to install Intel OpenVINO on test systems.

In response to the first question, testers can find the relevant instructions for each framework in the readme files included in the AIXPRT install package. Instructions for Windows installation are in section 3 of the OpenVINO and TensorFlow readmes. Please note that whether you’re running AIXPRT on Ubuntu or Windows, be sure to read the “Known Issues” section in the readme, as there may be issues relevant to your specific configuration.

The readme files for each respective framework in the CP2 package are located here:

  • AIXPRT_0.5_CP2\\AIXPRT\Modules\Deep-Learning
  • AIXPRT_0.5_CP2\\AIXPRT\Modules\Deep-Learning
  • AIXPRT_0.5_CP2\\AIXPRT\Modules\Deep-Learning

We’re also working on consolidating the instructions into a central document that will make it easier for everyone to find the instructions they need.

In response to the question about OpenVINO installation, we’re working on an AIXPRT CP2 package that includes a precompiled version of OpenVINO R5.0.1 for easy installation on Windows via a few quick commands, and a script that installs the necessary OpenVINO dependencies. We’re currently testing the build, and we’ll make it available to testers as soon as possible.

The tests themselves will not change, so the new build will not influence existing results from Ubuntu or Windows. We hope it will simply facilitate the setup and testing process for many users.

We appreciate each bit of feedback that we receive, so if you have any suggestions for AIXPRT, please let us know!


We want to hear your thoughts about the AIXPRT development schedule

We released the second AIXPRT Community Preview (CP2) about two weeks ago. The main additions in CP2 were the ability to run certain test configurations in Windows (OpenVINO CPU/GPU and TensorFlow CPU), the option to download the installer package from the AIXPRT tab in the XPRT Members’ Area, and a demo mode.

We’re also investigating ways to support TensorFlow GPU and TensorFlow-TensorRT testing in Windows, and we’d like to eventually add support for TensorRT testing in Ubuntu and Windows. If development and pre-release testing go as planned, we may roll out some of these extra features by the end of June. However, it’s possible that getting all the pieces that we want in place will require a multi-step release process. If so, we’re considering two approaches: (1) issuing a third community preview (CP3) and (2) preparing a general availability (GA) release, to which we would add features over the months following the release. Neither of these paths is likely to affect test results from the currently supported configurations.

Would you like to work with another community preview, or would it be better for us to move straight to a GA release and add features as they become ready? We want to follow the approach that the majority of community members prefer, so please let us know what you think. As always, we also welcome any questions, concerns, or suggestions regarding the AIXPRT development process.


AIXPRT Community Preview 2 is almost here!

In last week’s blog, we predicted that the second AIXPRT Community Preview (CP2) would be ready for release later this month. Since then, the development process has accelerated, and we now expect to release CP2 as early as tomorrow, May 10.

Those who have access to the existing AIXPRT Community Preview GitHub repository will be able to access CP2 the same way as before. In addition to making the build available on GitHub, we’ll also post CP2 on an AIXPRT tab in the XPRT Members’ Area (login required). If you don’t have a BenchmarkXPRT Development Community membership, please contact us and we’ll help you register.

Testing with AIXPRT CP2 in Ubuntu will be the same as with the first CP, and none of the CP2 changes will affect results. In Windows, testers will be able to use OpenVINO to target a system’s CPU and GPU, and TensorFlow to target CPUs. We’re still investigating ways to support TensorFlow GPU and TensorFlow-TensorRT testing in Windows.

We’re also continuing to work on the improvements to the AIXPRT results viewer that we mentioned last week. We won’t be able to implement all of the changes by tomorrow, but rather than waiting until we’re finished, we’ll be rolling out improvements as they become ready.

We’ll continue to keep everyone up to date with AIXPRT news here in the blog. If you have any questions or comments, please let us know.


Check out the other XPRTs:

Forgot your password?