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Category: Mobile devices

Notes from the lab: choosing a calibration system for MobileXPRT 3

Last week, we shared some details about what to expect in MobileXPRT 3. This week, we want to provide some insight into one part of the MobileXPRT development process, choosing a calibration system.

First, some background. For each of the benchmarks in the XPRT family, we select a calibration system using criteria we’ll explain below. This system serves as a reference point, and we use it to calculate scores that will help users understand a single benchmark result. The calibration system for MobileXPRT 2015 is the Motorola DROID RAZR M. We structured our calculation process so that the mean performance score from repeated MobileXPRT 2015 runs on that device is 100. A device that completes the same workloads 20 percent faster than the DROID RAZR M would have a performance score of 120, and one that performs the test 20 percent more slowly would have a score of 80. (You can find a more in-depth explanation of MobileXPRT score calculations in the Exploring MobileXPRT 2015 white paper.)

When selecting a calibration device, we are looking for a relevant reference point in today’s market. The device should be neither too slow to handle modern workloads nor so fast that it outscores most devices on the market. It should represent a level of performance that is close to what the majority of consumers experience, and one that will continue to be relevant for some time. This approach helps to build context for the meaning of the benchmark’s overall score. Without that context, testers can’t tell whether a score is fast or slow just by looking at the raw number. When compared to a well-known standard such as the calibration device, however, the score has more informative value.

To determine a suitable calibration device for MobileXPRT 3, we started by researching the most popular Android phones by market share around the world. It soon became clear that in many major markets, the Samsung Galaxy S8 ranked first or second, or at least appeared in the top five. As last year’s first Samsung flagship, the S8 is no longer on the cutting edge, but it has specs that many current mid-range phones are deploying, and the hardware should remain relevant for a couple of years.

For all of these reasons, we made the Samsung Galaxy S8 the calibration device for MobileXPRT 3. The model in our lab has a Qualcomm Snapdragon 835 SoC, 4 GB of RAM, and runs Android 7.0 (Nougat). We think it has the balance we’re looking for.

If you have any questions or concerns about MobileXPRT 3, calibration devices, or score calculations, please let us know. We look forward to sharing more information about MobileXPRT 3 as we get closer to the community preview.

Justin

News from the MobileXPRT 3 team

A few months ago, we shared some of our thoughts during the early planning stages of MobileXPRT 3 development. Since then, we’ve started building the new benchmark with Android Studio SDK 27. We’re now at a place where we can share more details about what to expect in MobileXPRT 3. In a nutshell, one of the five workloads in the previous version, MobileXPRT 2015, is getting a major overhaul, the remaining four workloads are getting updated test content, and we’re adding one completely new workload.

One of the first challenges we tackled was to completely rebuild the Create Slideshow workload. In MobileXPRT 2015, the workload uses FFmpeg to convert photos into video. FFmpeg utilizes a C++ executable, and it needs to be compiled differently for different architectures such as x86, x64, arm32, arm64, etc. With each new Android version, the task of maintaining FFmpeg compatibility with numerous architectures and Android versions becomes more complex. MobileXPRT 2015 still works well on most Android devices, but we wanted a more future-proof solution. In MobileXPRT 3, the Create Slideshow workload will use the Android MediaCodec API instead of FFmpeg. This change enables the workload to run successfully on devices that could not complete the workload in MobileXPRT 2015.

We are updating the test content of the following workloads: Apply Photo Effects, Create Photo Collages, Encrypt Personal Content, and Detect Faces to Organize Photos. We will replace items such as photos and videos with more contemporary file resolutions and sizes where applicable.

In the mobile device market, artificial intelligence and machine learning capabilities are rapidly moving from the level of novelty to being integrated into many daily tasks, so we wanted to include an AI or ML element in MobileXPRT 3. Our new workload uses Google’s Mobile Vision API to perform optical character recognition (OCR) tasks involving scanning receipts for personal records or an expense report. The scenario is similar to the OCR receipt-scanning task in WebXPRT 3, though the two workloads are based on different text-recognition technologies.

Finally, we’re updating the MobileXPRT UI to improve the look of the benchmark and make it easier to use. We’ll share a sneak peek of the new UI here in the blog around the time of the community preview. If you have any questions about MobileXPRT 2015 or MobileXPRT 3, please let us know!

Justin

Check out our new WebXPRT video!

At over 305,000 runs and counting, WebXPRT is our most popular benchmark app. Device manufacturers, tech journalists, and developers around the world use WebXPRT because test runs are quick and easy, it runs on almost anything with a web browser, and it provides reliable data about how well devices perform when completing real-world tasks.

WebXPRT is not just for “techies,” however. To help explain what WebXPRT does and why it matters to everyday consumers, we’ve published a new video, What is WebXPRT and why should I care? The video explains the concepts behind some of WebXPRT’s workloads and how even small delays in common online tasks can add up to big headaches and a significant amount of wasted time. We all want to avoid those problems, and WebXPRT can help anyone that wants to see how their device, or a new device they’re thinking about buying, stacks up against the alternatives. We encourage you to check out the video below, which you can also find on YouTube and WebXPRT.com. If you have any questions about WebXPRT, please let us know!

Justin

What is WebXPRT and why should I care?

AI and the next MobileXPRT

As we mentioned a few weeks ago, we’re in the early planning stages for the next version of MobileXPRT—MobileXPRT 3. We’re always looking for ways to make XPRT benchmark workloads more relevant to everyday users, and a new version of MobileXPRT provides a great opportunity to incorporate emerging tech such as AI into our apps. AI is everywhere and is beginning to play a huge role in our everyday lives through smarter-than-ever phones, virtual assistants, and smart homes. The challenge for us is to identify representative mobile AI workloads that have the necessary characteristics to work well in a benchmark setting. For MobileXPRT, we’re researching AI workloads that have the following characteristics:

  • They work offline, not in the cloud.
  • They don’t require additional training prior to use.
  • They support common use cases such as image processing, optical character recognition (OCR), etc.


We’re researching the possibility of using Google’s Mobile Vision library, but there may be other options or concerns that we’re not aware of. If you have tips for places we should look, or ideas for workloads or APIs we haven’t mentioned, please let us know. We’ll keep the community informed as we narrow down our options.

Justin

Planning the next version of MobileXPRT

We’re in the early planning stages for the next version of MobileXPRT, and invite you to send us any suggestions you may have. What do you like or not like about MobileXPRT? What features would you like to see in a new version?

When we begin work on a new version of any XPRT, one of the first steps we take is to assess the benchmark’s workloads to determine whether they will provide value during the years ahead. This step almost always involves updating test content such as photos and videos to more contemporary file resolutions and sizes, and it can also involve removing workloads or adding completely new scenarios. MobileXPRT currently includes five performance scenarios (Apply Photo Effects, Create Photo Collages, Create Slideshow, Encrypt Personal Content, and Detect Faces to Organize Photos). Should we stick with these five or investigate other use cases? What do you think?

As we did with WebXPRT 3 and the upcoming HDXPRT 4, we’re also planning to update the MobileXPRT UI to improve the look of the benchmark and make it easier to use.

Crucially, we’ll also build the app using the most current Android Studio SDK. Android development has changed significantly since we released MobileXPRT 2015 and apps must now conform to stricter standards that require explicit user permission for many tasks. Navigating these changes shouldn’t be too difficult, but it’s always possible that we’ll encounter unforeseen challenges at some point during the process.

Do you have suggestions for test scenarios that we should consider for MobileXPRT? Are there existing features we should remove? Are there elements of the UI that you find especially useful or have ideas for improving? Please let us know. We want to hear from you and make sure that MobileXPRT continues to meet your needs.

Justin

MWCS18 and AIXPRT: a new video

A few weeks ago, Bill shared his first impressions from this year’s Mobile World Congress Shanghai (MWCS). “5G +” was the major theme, and there was a heavy emphasis on 5G + AI. This week, we published a video about Bill’s MWCS experience and the role that the XPRTs can play in evaluating emerging technologies such as 5G, AI, and VR. Check it out!

MWC Shanghai 2018: 5G, AI, VR, and the XPRTs

 

You can read more about AIXPRT development here. We’re still accepting responses to the AIXPRT Request for Comments, so if you would like to share your ideas on developing an AI/machine learning benchmark, please feel free to contact us.

Justin

 

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