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Category: Web-based testing

Web AI frameworks: Possible paths for the AI-focused WebXPRT 4 auxiliary workload

A few months ago, we announced that we’re moving forward with the development of a new auxiliary WebXPRT 4 workload focused on local, browser-side AI technology. Local AI has many potential benefits, and it now seems safe to say that it will be a common fixture of everyday life for many people in the future. As the growth of browser-based inference technology picks up steam, our goal is to equip WebXPRT 4 users with the ability to quickly and reliably evaluate how well devices can handle substantial local inference tasks in the browser.

To reach our goal, we’ll need to make many well-researched and carefully considered decisions along the development path. Throughout the decision-making process, we’ll be balancing our commitment to core XPRT values, such as ease of use and widespread compatibility, with the practical realities of working with rapidly changing emergent technologies. In today’s blog, we’re discussing one of the first decision points that we face—choosing a Web AI framework.

AI frameworks are suites of tools and libraries that serve as building blocks for developers to create new AI-based models and apps or integrate existing AI functions in custom ways. AI frameworks can be commercial, such as OpenAI, or open source, such as Hugging Face, PyTorch, and TensorFlow. Because the XPRTs are available at no cost for users and we publish our source code, open-source frameworks are the right choice for WebXPRT.

Because the new workload will focus on locally powered, browser-based inference tasks, we also need to choose an AI framework that has browser integration capabilities and does not rely on server-side computing. These types of frameworks—called Web AI—use JavaScript (JS) APIs and other web technologies, such as WebAssembly and WebGPU, to run machine learning (ML) tasks on a device’s CPU, GPU, or NPU.

Several emerging Web AI frameworks may provide the compatibility and functionality we need for the future WebXPRT workload. Here are a few that we’re currently researching:

  • ONNX Runtime Web: Microsoft and other partners developed the Open Neural Network Exchange (ONNX) as an open standard for ML models. With available tools, users can convert models from several AI frameworks to ONNX, which can then be used by ONNX Runtime Web. ONNX Runtime Web allows developers to leverage the broad compatibility of ONNX-formatted ML models—including pre-trained vision, language, and GenAI models—in their web applications.
  • Transformers.js: Transformers.js, which uses ONNX Runtime Web, is a JS library that allows users to run AI models from the browser and offline. Transformers.js supports language, computer vision, and audio ML models, among others.
  • MediaPipe: Google developed MediaPipe as a way for developers to adapt TensorFlow-based models for use across many platforms in real-time on-device inference applications such as face detection and gesture recognition. MediaPipe is particularly useful for inference work in images, videos, and live streaming.
  • TensorFlow.js: TensorFlow has been around for a long time, and the TensorFlow ecosystem provides users with a broad variety of models and datasets. TensorFlow is an end-to-end ML solution—training to inference—but with available pre-trained models, developers can focus on inference. TensorFlow.js is an open-source JS library that helps developers integrate TensorFlow with web apps.

We have not made final decisions about a Web AI framework or any aspect of the future workload. We’re still in the research, discussion, and experimentation stages of development, but we want to be transparent with our readers about where we are in the process. In future blog posts, we’ll discuss some of the other major decision points in play.

Most of all, we invite you to join us in these discussions, make recommendations, and give us any other feedback or suggestions you may have, so please feel free to share your thoughts!

Justin

Contribute to WebXPRT’s AI capabilities with your NPU-equipped gear

A few weeks ago, we announced that we’re developing a new auxiliary WebXPRT 4 workload focused on local, browser-based AI technology. This is an exciting project for us, and as we work to determine the best approach from the perspective of frameworks, APIs, inference models, and test scenarios, we’re also thinking ahead to the testing process. To best understand how the new workload will impact system performance, we’re going to need to test it on hardware equipped with the latest generation of neural processing units (NPUs).

NPUs are not new, but the technology is advancing rapidly, and a growing number of PC and laptop manufacturers are releasing NPU-equipped systems. Several vendors have announced plans to release systems equipped with all-new NPUs in the latter half of this year. As is often the case with bleeding-edge technology, however, official release dates do not always coincide with widespread availability.

We want to evaluate new AI-focused WebXPRT workloads on the widest possible range of new systems, but getting a wide selection of gear equipped with the latest NPUs may take quite a while through normal channels. For that reason, we’ve decided to ask our readers for help to expedite the process.

If you’re an OEM or vendor representative with access to the latest generation of NPU-equipped gear and want to contribute to WebXPRT’s evolution, consider sending us any PCs, white boxes, laptops, 2-in-1s, or tablets (on loan) that would be suitable for NPU-focused testing. We have decades of experience serving as trusted testers of confidential and pre-release gear, so we’re well-acquainted with concerns about confidentiality that may come into play, and we won’t publish any information about the systems or related test results without your permission.

We will, though, be happy to share with you our test results on your systems, and we’d love to hear any guidance or other feedback from you on this new workload.

We’re open to any suitable gear, but we’re especially interested in AMD Ryzen AI, Apple M4, Intel Lunar Lake and Arrow Lake, and Qualcomm Snapdragon X Elite systems.

If you’re interested in sending us gear for WebXPRT development testing, please contact us. We’ll work out all the necessary details. Thanks in advance for your help!

Justin

WebXPRT in PT reports

We don’t just make WebXPRT—we use it, too. If you normally come straight to BenchmarkXPRT.com or WebXPRT.com, you may not even realize that Principled Technologies (PT) does a lot more than just managing and administering the BenchmarkXPRT Development Community. We’re also the tech world’s leading provider of hands-on testing and related fact-based marketing services. As part of that work, we’re frequent WebXPRT users.

We use the benchmark when we test devices such as Chromebooks, desktops, mobile workstations, and consumer laptops for our clients. (You can see a lot of that work and many of our clients on our public marketing portfolio page.) We run the benchmark for the same reasons that others do—it’s a reliable and easy-to-use tool for measuring how well devices handle web browsing and other web work.

We also sometimes use WebXPRT simply because our clients request it. They request it for the same reason the rest of us like and use it: it’s a great tool. Regardless of job titles and descriptions, most laptop and tablet users surf the web and access web-based applications every day. Because WebXPRT is a browser benchmark, higher scores on it could indicate that a device may provide a superior online experience.

Here are just a few of the recent PT reports that used WebXPRT:

  • In a project for Dell, we compared the performance of a Dell Latitude 7340 Ultralight to that of a 13-inch Apple MacBook Air (2022).
  • In this study for HP, we compared the performance of an HP ZBook Firefly G10, an HP ZBook Power G10, and an HP ZBook Fury G10.
  • Finally, in a set of comparisons for Lenovo, we evaluated the system performance and end-user experience of eight Lenovo ThinkBook, ThinkCentre, and ThinkPad systems along with their Apple counterparts.

All these projects, and many more, show how a variety of companies rely on PT—and on WebXPRT—to help buyers make informed decisions. P.S. If we publish scores from a client-commissioned study in the WebXPRT 4 results viewer, we will list the source as “PT”, because we did the testing.

By Mark L. Van Name and Justin Greene

Comparing the WebXPRT 4 performance of five popular browsers

Every so often, we like to refresh a series of in-house WebXPRT comparison tests to see if recent updates have changed the performance rankings of popular web browsers. We published our most recent comparison last February, when we used WebXPRT 4 to compare the performance of five browsers on the same system.

For this round of tests, we used the same Dell XPS 13 7930 laptop as last time, which features an Intel Core i3-10110U processor and 4 GB of RAM, running Windows 11 Home updated to version 23H2 (22631.307). We installed all current Windows updates, and updated each of the browsers under test: Brave, Google Chrome, Microsoft Edge, Mozilla Firefox, and Opera.

After the update process completed, we turned off updates to prevent them from interfering with test runs. We ran WebXPRT 4 three times on each of the five browsers. The score we post for each browser is the median of the three test runs.

In our last round of tests, the range between high and low scores was tight, with an overall difference of only 4.3 percent. Edge squeaked out a win, with a 2.1 percent performance advantage over Chrome. Firefox came in last, but was only one overall score point behind the tied score of Brave and Opera.

In this round of testing, the rank order did not change, but we saw more differentiation in the range of scores. While the performance of each browser improved, the range between high and low scores widened to a 19.1 percent difference between first-place Edge and last-place Firefox. The scores of the four Chromium-based browsers (Brave, Opera, Chrome, and Edge) all improved by at least 21 points, while the Firefox score only improved by one point. 

Do these results mean that Microsoft Edge will always provide a faster web experience, or Firefox will always be slower than the others? Not necessarily. It’s true that a device with a higher WebXPRT score will probably feel faster during daily web activities than one with a much lower score, but your experience depends in part on the types of things you do on the web, along with your system’s privacy settings, memory load, ecosystem integration, extension activity, and web app capabilities.

In addition, browser speed can noticeably increase or decrease after an update, and OS-specific optimizations can affect performance, such as with Edge on Windows 11 and Chrome on Chrome OS. All these variables are important to keep in mind when considering how WebXPRT results may translate to your everyday experience.

Have you used WebXPRT 4 to compare browser performance on the same system? Let us know how it turned out!

Justin

How we evaluate new WebXPRT workload proposals

A key value of the BenchmarkXPRT Development Community is our openness to user feedback. Whether it’s positive feedback about our benchmarks, constructive criticism, ideas for completely new benchmarks, or proposed workload scenarios for existing benchmarks, we appreciate your input and give it serious consideration.

We’re currently accepting ideas and suggestions for ways we can improve WebXPRT 4. We are open to adding both non-workload features and new auxiliary tests, which can be experimental or targeted workloads that run separately from the main test and produce their own scores. You can read more about experimental WebXPRT 4 workloads here. However, a recent user question about possible WebGPU workloads has prompted us to explain the types of parameters that we consider when we evaluate a new WebXPRT workload proposal.

Community interest and real-life relevance

The first two parameters we use when evaluating a WebXPRT workload proposal are straightforward: are people interested in the workload and is it relevant to real life? We originally developed WebXPRT to evaluate device performance using the types of web-based tasks that people are likely to encounter daily, and real-life relevancy continues to be an important criterion for us during development. There are many technologies, functions, and use cases that we could test in a web environment, but only some of them are both relevant to common applications or usage patterns and likely to be interesting to lab testers and tech reviewers.

Maximum cross-platform support

Currently, WebXPRT runs in almost any web browser, on almost any device that has a web browser, and we would ideally maintain that broad level of cross-platform support when introducing new workloads. However, technical differences in the ways that different browsers execute tasks mean that some types of scenarios would be impossible to include without breaking our cross-platform commitment.

One reason that we’re considering auxiliary workloads with WebXPRT, e.g., a battery life rundown, is that those workloads would allow WebXPRT to offer additional value to users while maintaining the cross-platform nature of the main test. Even if a battery life test ran on only one major browser, it could still be very useful to many people.

Performance differentiation

Computer benchmarks such as the XPRTs exist to provide users with reliable metrics that they can use to gauge how well target platforms or technologies perform certain tasks. With a broadly targeted benchmark such as WebXPRT, if the workloads are so heavy that most devices can’t handle them, or so light that most devices complete them without being taxed, the results will have little to no use for OEM labs, the tech press, or independent users when evaluating devices or making purchasing decisions.

Consequently, with any new WebXPRT workload, we try to find a sweet spot in terms of how demanding it is. We want it to run on a wide range of devices—from low-end devices that are several years old to brand-new high-end devices and everything in between. We also want users to see a wide range of workload scores and resulting overall scores, so they can easily grasp the different performance capabilities of the devices under test.

Consistency and replicability

Finally, workloads should produce scores that consistently fall within an acceptable margin of error, and are easily to replicate with additional testing or comparable gear. Some web technologies are very sensitive to uncontrollable or unpredictable variables, such as internet speed. A workload that measures one of those technologies would be unlikely to produce results that are consistent and easily replicated.

We hope this post will be useful for folks who are contemplating potential new WebXPRT workloads. If you have any general thoughts about browser performance testing, or specific workload ideas that you’d like us to consider, please let us know.

Justin

Looking forward to an important WebXPRT milestone

February 28, 2013 was a momentous day for the BenchmarkXPRT Development Community. On that day, we published a press release announcing the official launch of the first version of the WebXPRT benchmark, WebXPRT 2013. As difficult as it is for us to believe, the 10-year anniversary of the initial WebXPRT launch is in just a few short months!

We introduced WebXPRT as a truly unique browser performance benchmark in a field that was already crowded with a variety of measurement tools. Since those early days, the WebXPRT market presence has grown from a small foothold into a worldwide industry standard. Over the years, hundreds of tech press publications have used WebXPRT in thousands of articles and reviews, and the WebXPRT completed-runs counter rolled over the 1,000,000-run mark.

New web technologies are continually changing the way we use the web, and browser-performance benchmarks should evaluate how well new devices handle the web of today, not the web of several years ago. While some organizations have stopped development for other browser performance benchmarks, we’ve had the opportunity to continue updating and refining WebXPRT. We can look back at each of the four major iterations of the benchmark—WebXPRT 2013, WebXPRT 2015, WebXPRT 3, and WebXPRT 4—and see a consistent philosophy and shared technical lineage contributing to a product that has steadily improved.

As we get closer to the 10-year anniversary of WebXPRT next year, we’ll be sharing more insights about its reach and impact on the industry, discussing possible future plans for the benchmark, and announcing some fun anniversary-related opportunities for WebXPRT users. We think 2023 will be the best year yet for WebXPRT!

Justin

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