Category: Intel

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

on July 11, 2024

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

Posted in AI, AMD, Apple, Arrow Lake, benchmark, Benchmarking, browser performance, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, Intel, Laptops, Lunar Lake, M4, Machine learning, Mobile devices, NPU, on-device AI, PCs, Performance benchmarking, Qualcomm, Ryzen AI, Snapdragon X Elite, Tablets, Web-based testing, WebXPRT, WebXPRT 4 | Tagged 2-in-1s, AI, AMD, Apple, Arrow Lake, artificial intelligence, browser performance, desktops, Intel, laptops, Lunar Lake, M4, NPU, PCs, Qualcomm, Ryzen, Ryzen AI, Snapdragon, Snapdragon X Elite, tablets, WebXPRT, WebXPRT 4, white box |

The WebXPRT 4 results viewer: A powerful tool for browsing hundreds of test results

By Justin Greene

on May 16, 2024

In our recent blog post about the XPRT results database, we promised to discuss the WebXPRT 4 results viewer in more detail. We developed the results viewer to serve as a feature-rich interactive tool that visitors to WebXPRT.com can use to browse the test results that we’ve published on our site, dig into the details of each result, and compare scores from multiple devices. The viewer currently has almost 700 test results, and we add new PT-curated entries each week.

Figure 1 shows the tool’s default display. Each vertical bar in the graph represents the overall score of a single test result, with bars arranged left-to-right, from lowest to highest. To view a single result in detail, hover over a bar to highlight it, and a small popup window will display the basic details of the result. You can then click to select the highlighted bar. The bar will turn dark blue, and the dark blue banner at the bottom of the viewer will display additional details about that result.

Figure 1: The WebXPRT 4 results viewer tool’s default display

In the example in Figure 1, the banner shows the overall score (237), the score’s percentile rank (66^th) among the scores in the current display, the name of the test device, and basic hardware configuration information. If the source of the result is PT, you can click the Run info button in the bottom right-hand corner of the display to see the run’s individual workload scores. If the source is an external publisher, users can click the Source link to navigate to the original site.

The viewer includes a drop-down menu that lets users quickly filter results by major device type categories, plus a tab with additional filtering options, such as browser type, processor vendor, and result source. Figure 2 shows the viewer after I used the device type drop-down filter to select only laptops.

Figure 2: Screenshot from the WebXPRT 4 results viewer showing results filtered by the device type drop-down menu.

Figure 3 shows the viewer as I use the filter tab to explore additional filter options, such as processor vendor.

Figure 3: Screenshot from the WebXPRT 4 results viewer showing the filter options available with the filter tab.

The viewer will also let you pin multiple specific runs, which is helpful for making side-by-side comparisons. Figure 4 shows the viewer after I pinned four runs and viewed them on the Pinned runs screen.

Figure 4: Screenshot from the WebXPRT 4 results viewer showing four pinned runs on the Pinned runs screen.

Figure 5 shows the viewer after I clicked the Compare runs button. The overall and individual workload scores of the pinned runs appear in a table.

Figure 5: Screenshot from the WebXPRT 4 results viewer showing four pinned runs on the Compare runs screen.

We hope that you’ll enjoy using the results viewer to browse our WebXPRT 4 results database and that it will become one of your go-to resources for device comparison data.

Are there additional features you’d like to see in the viewer, or other ways we can improve it? Please let us know, and send us your latest test results!

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, BenchmarkXPRT, browser performance, Browser-based benchmarks, Cross-platform benchmarks, Dell, HP, Intel, Performance benchmarking, results, results submission, WebXPRT, WebXPRT 4 | Tagged benchmark, BenchmarkXPRT, browser benchmark, browser performance, cross-platform, results, results submission, WebXPRT, WebXPRT 4 |

Local AI and new frontiers for performance evaluation

By Justin Greene

on December 19, 2023

Recently, we discussed some ways the PC market may evolve in 2024, and how new Windows on Arm PCs could present the XPRTs with many opportunities for benchmarking. In addition to a potential market shakeup from Arm-based PCs in the coming years, there’s a much broader emerging trend that could eventually revolutionize almost everything about the way we interact with our personal devices—the development of local, dedicated AI processing units for consumer-oriented tech.

AI already impacts daily life for many consumers through technologies such as such as predictive text, computer vision, adaptive workflow apps, voice recognition, smart assistants, and much more. Generative AI-based technologies are rapidly establishing a permanent, society-altering presence across a wide range of industries. Aside from some localized inference tasks that the CPU and/or GPU typically handle, the bulk of the heavy compute power that fuels those technologies has been in the cloud or in on-prem servers. Now, several major chipmakers are working to roll out their own versions of AI-optimized neural processing units (NPUs) that will enable local devices to take on a larger share of the AI load.

Examples of dedicated AI hardware in recently-released or upcoming consumer devices include Intel’s new Meteor Lake NPU, Apple’s Neural Engine for M-series SoCs, Qualcomm’s Hexagon NPU, and AMD’s XDNA 2 architecture. The potential benefits of localized, NPU-facilitated AI are straightforward. On-device AI could reduce power consumption and extend battery life by offloading those tasks from the CPUs. It could alleviate certain cloud-related privacy and security concerns. Without the delays inherent in cloud queries, localized AI could execute inference tasks that operate much closer to real time. NPU-powered devices could fine-tune applications around your habits and preferences, even while offline. You could pull and utilize relevant data from cloud-based datasets without pushing private data in return. Theoretically, your device could know a great deal about you and enhance many areas of your daily life without passing all that data to another party.

Will localized AI play out that way? Some tech companies envision a role for on-device AI that enhances the abilities of existing cloud-based subscription services without decoupling personal data. We’ll likely see a wide variety of capabilities and services on offer, with application-specific and SaaS-determined privacy options.

Regardless of the way on-device AI technology evolves in the coming years, it presents an exciting new frontier for benchmarking. All NPUs will not be created equal, and that’s something buyers will need to understand. Some vendors will optimize their hardware more for computer vision, or large language models, or AI-based graphics rendering, and so on. It won’t be enough for business and consumers to simply know that a new system has dedicated AI processing abilities. They’ll need to know if that system performs well while handling the types of AI-related tasks that they do every day.

Here at the XPRTs, we specialize in creating benchmarks that feature real-world scenarios that mirror the types of tasks that people do in their daily lives. That approach means that when people use XPRT scores to compare device performance, they’re using a metric that can help them make a buying decision that will benefit them every day. We look forward to exploring ways that we can bring XPRT benchmarking expertise to the world of on-device AI.

Do you have ideas for future localized AI workloads? Let us know!

Justin

Posted in AI, AMD, Apple, Arm, battery life, benchmark, Benchmark metrics, Benchmarking, Cloud, computer vision, Cross-platform benchmarks, Data privacy, Future of performance evaluation, graphics, Intel, large language models, Machine learning, Meteor Lake, NPU, on premises, on-device AI, PCs, Qualcomm, SaaS, WebXPRT, WebXPRT 4 | Tagged AI, AMD, Apple, benchmark, BenchmarkXPRT, computer vision, Intel, large language models, local AI, machine learning, Meteor Lake, on-device AI, Qualcomm, rendering, SaaS, WebXPRT, WebXPRT 4 |

The evolving PC market brings new opportunities for WebXPRT

By Justin Greene

on December 7, 2023

Here at the XPRTs, we have to spend time examining what’s next in the tech industry, because the XPRTs have to keep up with the pace of innovation. In our recent discussions about 2024, a major recurring topic has been the potential impact of Qualcomm’s upcoming line of SOCs designed for Windows on Arm PCs.

Now, Windows on Arm PCs are certainly not new. Since Windows RT launched on the Arm-based Microsoft Surface RT in 2012, various Windows on Arm devices have come and gone, but none of them—except for some Microsoft SQ-based Surface devices—have made much of a name for themselves in the consumer market.

The reasons for these struggles are straightforward. While Arm-based PCs have the potential to offer consumers the benefits of excellent battery life and “always-on” mobile communications, the platform has historically lagged Intel- and AMD-based PCs in performance. Windows on Arm devices have also faced the challenge of a lack of large-scale buy-in from app developers. So, despite the past involvement of device makers like ASUS, HP, Lenovo, and Microsoft, the major theme of the Windows on Arm story has been one of very limited market acceptance.

Next year, though, the theme of that story may change. If it does, WebXPRT 4 is well-positioned to play an important part.

At the recent Qualcomm Technology Summit, the company unveiled the new 4nm Snapdragon X Elite SOC, which includes an all-new 12-core Oryon CPU, an integrated Adreno GPU, and an integrated Hexagon NPU (neural processing unit) designed for AI-powered applications. Company officials presented performance numbers that showed the X Elite surpassing the performance of late-gen AMD, Apple, and Intel competitor platforms, all while using less power.

Those are massive claims, and of course the proof will come—or not—only when systems are available for test. (In the past, companies have made similar claims about Windows on Arm advantages, only to see those claims evaporate by the time production devices show up on store shelves.)

Will Snapdragon X Elite systems demonstrate unprecedented performance and battery life when they hit the market? How will the performance of those devices stack up to Intel’s Meteor Lake systems and Apple’s M3 offerings? We don’t yet know how these new devices may shake up the PC market, but we do know that it looks like 2024 will present us with many golden opportunities for benchmarking. Amid all the marketing buzz, buyers everywhere will want to know about potential trade-offs between price, power, and battery life. Tech reviewers will want to dive into the details and provide useful data points, but many traditional PC benchmarks simply won’t work with Windows on ARM systems. As a go-to, cross-platform favorite of many OEMs—that runs on just about anything with a browser—WebXPRT 4 is in a perfect position to provide reviewers and consumers with relevant performance comparison data.

It’s quite possible that 2024 may be the biggest year for WebXPRT yet!

Justin

Posted in AI, AMD, Apple, Arm, ASUS, battery life, Benchmark metrics, Benchmarking, BenchmarkXPRT, browser performance, Browser-based benchmarks, Cross-platform benchmarks, Future of performance evaluation, Intel, Laptops, M3, Meteor Lake, PCs, Performance benchmarking, Qualcomm, WebXPRT, WebXPRT 4 | Tagged Adreno, AMD, Apple, Arm, ASUS, benchmark, BenchmarkXPRT, Hexagon, HP, Intel, laptops, Lenovo, M3, Meteor Lake, Microsoft, Oryon, PCs, Qualcomm, Snapdragon, SOC, Surface |

Helpful tips for WebXPRT 4 results submission

By Justin Greene

on June 9, 2022

Back in March, we discussed the WebXPRT 4 results submission process and reminded readers that everyone who runs a WebXPRT 4 test is welcome to submit scores for us to consider for publication in the WebXPRT 4 results viewer. Unlike sites that publish every result that users submit, we publish only results that meet our evaluation criteria. Among other things, scores must be consistent with general expectations and must include enough detailed system information to help us assess whether individual scores represent valid test runs. Today, we offer a couple of tips to increase the likelihood that we will publish your WebXPRT 4 test results.

Tip 1: Specify your system’s processor

While testers usually include detailed information for the device, model number, operating system, and browser version fields, we receive many submissions with little to no information about the test system’s processor.

In the picture below, you can see an example of the level of detail that we require to consider a submission. We need the full processor name, including the manufacturer and model number (e.g., Intel Core i9-9980HK, AMD Ryzen 3 1300X, or Apple M1 Max). Note that we do not require the processor speed reported by the system.

Tip 2: Include a valid email address

It is also common for submissions to not include a valid email address. While we understand the privacy concerns related to submitting a personal or corporate email address, we need a valid address that we can use as a point of contact to confirm test-related information when necessary. We don’t use those addresses for any other purposes, such as selling them, sharing them with any third parties, or adding them to a mailing list.

We hope this information explains why we might not have published your results. We look forward to receiving your future score submissions. If you have any questions about the submission process, please let us know!

Justin

Posted in AMD, Apple, benchmark, Benchmark metrics, Browser-based benchmarks, Cross-platform benchmarks, Intel, Performance benchmarking, results, results submission, WebXPRT, WebXPRT 4 | Tagged browser benchmark, browser performance, results, results submission, WebXPRT, WebXPRT 4 |

How to use alternate configuration files with AIXPRT

By Justin Greene

on October 10, 2019

In last week’s AIXPRT Community Preview 3 announcement, we mentioned the new public GitHub repository that we’re using to publish AIXPRT-related information and resources. In addition to the installation readmes for each AIXPRT installation package, the repository contains a selection of alternative test config files that testers can use to quickly and easily change a test’s parameters.

As we discussed in previous blog entries about batch size, levels of precision, and number of concurrent instances, AIXPRT testers can adjust each of these key variables by editing the JSON file in the AIXPRT/Config directory. While the process is straightforward, editing each of the variables in a config file can take some time, and testers don’t always know the appropriate values for their system. To address both of these issues, we are offering a selection of alternative config files that testers can download and drop into the AIXPRT/Config directory.

In the GitHub repository, we’ve organized the available config files first by operating system (Linux_Ubuntu and Windows) and then by vendor (All, Intel, and NVIDIA). Within each section, testers will find preconfigured JSON files set up for several scenarios, such as running with multiple concurrent instances on a system’s CPU or GPU, running with FP32 precision instead of FP16, etc. The picture below shows the preconfigured files that are currently available for systems running Ubuntu on Intel hardware.

Because potential AIXPRT use cases cut across a wide range of hardware segments, including desktops, edge devices, and servers, not all AIXPRT workloads and configs will be applicable to each segment. As we move towards the AIXPRT GA, we’re working to find the best way to parse out these distinctions and communicate them to end users. In many cases, the ideal combination of test configuration variables remains an open question for ongoing research. However, we hope the alternative configuration files will help by giving testers a starting place.

If you experiment with an alternative test configuration file, please note that it should replace the existing default config file. If more than one config file is present, AIXPRT will run all the configurations and generate a separate result for each. More information about the config files and detailed instructions for how to handle the files are available in the EditConfig.md document in the public repository.

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.

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, Collaborative benchmark development, Community Preview, GitHub, Intel, Linux, Machine learning, NVIDIA, Performance benchmarking, Ubuntu | Tagged AI, AIXPRT, batch size, concurrent instances, FP16, FP32, GitHub, Intel, NVIDIA, precision, Windows |

Category: Intel

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

The WebXPRT 4 results viewer: A powerful tool for browsing hundreds of test results

Local AI and new frontiers for performance evaluation

The evolving PC market brings new opportunities for WebXPRT

Helpful tips for WebXPRT 4 results submission

How to use alternate configuration files with AIXPRT

Check out the other XPRTs: