Device reviews in publications
such as AnandTech, Notebookcheck, and PCMag, among many others, often feature
WebXPRT test results, and we appreciate the many members of the tech press that
use WebXPRT. As we move forward with the WebXPRT 4 development process, we’re especially
interested in learning what longtime users would like to see in a new version
of the benchmark.
In previous posts,
we’ve asked people to weigh in on the potential addition of a WebAssembly workload or a battery life test. We’d also like to ask experienced testers some other
test-related questions. To that end, this week we’ll be sending a WebXPRT 4
survey directly to members of the tech press who frequently publish WebXPRT
Regardless of whether you are a member of the tech press, we invite you to participate by sending your answers to any or all the questions below to email@example.com. We ask you to do so by the end of May.
Do you think WebXPRT 3’s selection of workload scenarios is representative of modern web tasks?
How do you think WebXPRT compares to other common browser-based benchmarks, such as JetStream, Speedometer, and Octane?
Are there web technologies that you’d like us to include in additional workloads?
Are you happy with the WebXPRT 3 user interface? If not, what UI changes would you like to see?
Are there any aspects of WebXPRT 2015 that we changed in WebXPRT 3 that you’d like to see us change back?
Have you ever experienced significant connection issues when testing with WebXPRT?
Given the array of workloads, do you think the WebXPRT runtime is reasonable? Would you mind if the average runtime were a bit longer?
Are there any other aspects of WebXPRT 3 that you’d like to see us change?
If you’d like to discuss any topics
that we did not cover in the questions above, please feel free to include additional
comments in your response. We look forward to hearing your thoughts!
We’re excited to see
that users have successfully completed over 750,000 WebXPRT runs! If you’ve run WebXPRT in any of the more than 654 cities
and 68 countries from which we’ve received complete test data—including
newcomers Belize, Cambodia, Croatia, and Pakistan—we’re grateful for your help.
We could not have reached this milestone without you!
As the chart below illustrates, WebXPRT use has grown steadily over the years. We now record, on average, almost twice as many WebXPRT runs in one month as we recorded in the entirety of our first year. In addition, with over 82,000 runs to date in 2021, there are no signs that growth is slowing.
Developing a new
benchmark is never easy, and the obstacles multiply when you attempt to create
a cross-platform benchmark, such as WebXPRT, that will run on a wide variety of
devices. Establishing trust with the benchmarking community is another
challenge. Transparency, consistency, and technical competency on our part are critical
factors in building that trust, but the people who take time out of their busy
schedules to run the benchmark for the first time also play a role. We thank
all of the manufacturers, OEM labs, and members of the tech press who decided
to give WebXPRT a try, and we look forward to your input as we continue to improve WebXPRT in the years to come.
If you have any
questions or comments about WebXPRT, we’d love to hear from you!
We’re currently formulating our 2021 development roadmap for the XPRTs. In addition to planning CloudXPRT and WebXPRT updates, we’re discussing the possibility of releasing HDXPRT 5 in 2021. It’s hard for me to believe, but it’s been about two and a half years since we started work on HDXPRT 4, and February 2021 will mark two years since the first HDXPRT 4 release. Windows PCs are more powerful than ever, so it’s a good time to talk about how we can enhance the benchmark’s ability to measure how well the latest systems handle real-world media technologies and applications.
When we plan a new
version of an XPRT benchmark, one of our first steps is updating the
benchmark’s workloads so that they will remain relevant in years to come. We
almost always update application content, such as photos and videos, to
contemporary file resolutions and sizes. For example, we added both higher-resolution
photos and a 4K video conversion task in HDXPRT 4. Are there specific types of
media files that you think would be especially relevant to high-performance
media tasks over the next few years?
Next, we will assess
the suitability of the real-world trial applications that the editing photos,
editing music, and converting videos test scenarios use. Currently, these are Adobe
Photoshop Elements, Audacity, CyberLink MediaEspresso, and HandBrake. Can you
think of other applications that belong in a high-performance media processing
In HDXPRT 4, we gave
testers the option to target a system’s discrete graphics card during the video
conversion workload. Has this proven useful in your testing? Do you have
suggestions for new graphics-oriented workloads?
We’ll also strive to
make the UI more intuitive, to simplify installation, and to reduce the size of
the installation package. What elements of the current UI do you find
especially useful or think we could improve?
We welcome your answers to these questions and any additional suggestions or comments on HDXPRT 5. Send them our way!
A few months ago, we invited readers to send in their thoughts and ideas about web
technologies and workload scenarios that may be a good fit for the next WebXPRT. We’d like to share a few of those ideas today, and we invite
you to continue to send your feedback. We’re approaching the time when we need to begin firming up
plans for a WebXPRT 4 development cycle in 2021, but there’s still plenty of
time for you to help shape the future of the benchmark.
One of the most
promising ideas for WebXPRT 4 is the potential addition of one or more WebAssembly (WASM) workloads.
WASM is a low-level, binary instruction format that works across all modern browsers.
It offers web developers a great deal of flexibility and provides the speed and
efficiency necessary for running complex client applications in the browser. WASM
enables a variety of workload scenario options, including gaming, video editing, VR, virtual
machines, image recognition, and interactive educational content.
In addition, the
Chrome team is dropping Portable Native Client (PNaCL) support in favor of
WASM, which is why we had to remove a PNaCL workload when updating CrXPRT 2015 to CrXPRT 2. We
generally model CrXPRT workloads on existing WebXPRT workloads, so
familiarizing ourselves with WASM could ultimately benefit more than one XPRT
We are also
considering adding a web-based machine learning workload with TensorFlow for
tasks including image classification, object detection, sentence encoding,
natural language processing, and more. We could also use this technology to
enhance one of WebXPRT’s existing AI-themed workloads, such as Organize Album
using AI or Encrypt Notes and OCR Scan.
Other ideas include using
a WebGL-based workload to target GPUs and investigating ways to incorporate a
battery life test. What do you think? Let us know!
It’s been about two years since we released WebXPRT 3, and we’re starting to think about the WebXPRT 4 development cycle. With over 529,000 runs to date, WebXPRT continues to be our most popular benchmark because it’s quick and easy to run, it runs on almost anything with a web browser, and it evaluates performance using the types of web technologies that many people use every day.
For each new version of WebXPRT, we start the development process by looking at browser trends and analyzing the feasibility of incorporating new web technologies into our workload scenarios. For example, in WebXPRT 3, we updated the Organize Album workload to include an image-classification task that uses deep learning. We also added an optical character recognition task to the Encrypt Notes and OCR scan workload, and introduced a new Online Homework workload that combined part of the DNA Sequence Analysis scenario with a writing sample/spell check scenario.
Here are the current WebXPRT 3 workloads:
Photo Enhancement: Applies three effects, each using Canvas, to two photos.
Organize Album Using AI: Detects faces and classifies images using the ConvNetJS neural network library.
Stock Option Pricing: Calculates and displays graphic views of a stock portfolio using Canvas, SVG, and dygraphs.js.
Encrypt Notes and OCR Scan: Encrypts notes in local storage and scans a receipt using optical character recognition.
Sales Graphs: Calculates and displays multiple views of sales data using InfoVis and d3.js.
Online Homework: Performs science and English assignment tasks using Web Workers and Typo.js spell check.
What new technologies or workload scenarios should we add? Are there any existing features we should remove? Would you be interested in an associated battery life test? We want to hear your thoughts and ideas about WebXPRT, so please tell us what you think!
It’s been a while since we last discussed the AIXPRT Community Preview 3 (CP3) release schedule, so we want to let everyone know where things stand. Testing for CP3 has taken longer than we predicted, but we believe we’re nearly ready for the release.
Testers can expect three significant changes in AIXPRT CP3. First, we updated support for the Ubuntu test packages. During the initial development phase of AIXPRT, Ubuntu version 16.04 LTS (Long Term Support) was the most current LTS version, but version 18.04 is now available.
Second, we have added TensorRT test packages for Windows and Ubuntu. Previously, AIXPRT testers could test only the TensorFlow variant of TensorRT. Now, they can use TensorRT to test systems with NVIDIA GPUs.
Third, we have added the Wide and Deep recommender system workload with the MXNet toolkit. Recommender systems are AI-based information-filtering tools that learn from end user input and behavior patterns and try to present them with optimized outputs that suit their needs and preferences. If you’ve used Netflix, YouTube, or Amazon accounts, you’ve encountered recommender systems that learn from your behavior.
Currently, the recommender system workload in AIXPRT CP3 is available for Ubuntu testing, but not for Windows. Recommender system inference workloads typically run on datacenter hardware, which tends to be Linux based. If enough community members are interested in running the MXNet/Wide and Deep test package on Windows, we can investigate what that would entail. If you’d like to see that option, please let us know.
As always, if you have any questions about the AIXPRT development process, feel free to ask!