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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

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

Gearing up for a busy year ahead

We hope everyone’s 2018 kicked off on a happy note, and you’re starting the year rested, refreshed, and inspired. Here at the XPRTs, we already have a busy slate of activity lined up, and we want to share a quick preview of what community members can expect in the coming months.

Next week, I’ll be travelling to CES 2018 in Las Vegas. CES provides us with a great opportunity to survey emerging tech and industry trends, and I look forward to sharing my thoughts and impressions from the show. If you’re attending this year, and would like to meet and discuss any aspect of the XPRTs, let me know.

There’s also more WebXPRT news to come. We’re working on several new features for the WebXPRT Processor Comparison Chart that we think will prove to be useful, and we hope to take the updated chart live very soon. We’re also getting closer to the much-anticipated WebXPRT 3 general release! If you’ve been testing the WebXPRT 3 Community Preview, be sure to send in your feedback soon.

Work on the next version of HDXPRT is progressing as well, and we’ll share more details about UI and workload updates as we get closer to a community preview build.

Last but not least, we’re considering the prospect of updating TouchXPRT and MobileXPRT later in the year. We look forward to working with the community on improved versions of each of those benchmarks.

Justin

Keeping up with the latest Android news

Ars Technica recently published a deep-dive review of Android 8.0 (Oreo) that contains several interesting tidbits about what the author called “Android’s biggest re-architecture, ever.” After reading the details, it’s hard to argue with that assessment.

The article’s thorough analysis includes a list of the changes Oreo is bringing to the UI, notification settings, locations service settings, and more. In addition to the types of updates that we usually see, a few key points stand out.

  • Project Treble, a complete reworking of Android’s foundational structure intended to increase the speed and efficiency of update delivery
  • A serious commitment to eliminating silent background services, giving users more control over their phone’s resources, and potentially enabling significant gains in battery life
  • Increased machine learning/neural network integration for text selection and recognition
  • A potential neural network API that allows third-party plugins
  • Android Go, a scaled-down version of Android tuned for budget phones in developing markets


There’s too much information about each of the points to discuss here, but I encourage anyone interested in Android development to check out the article. Just be warned that when they say “thorough,” they mean it, so it’s not exactly a quick read.

Right now, Oreo is available on only the Google Pixel and Pixel XL phones, and will not likely be available to most users until sometime next year. Even though widespread adoption is a way off, the sheer scale of the expected changes requires us to adopt a long-term development perspective.

We’ll continue to track developments in the Android world and keep the community informed about any impact that those changes may have on MobileXPRT and BatteryXPRT. If you have any questions or suggestions for future XPRT/Android applications, let us know!

Justin

Machine learning everywhere!

I usually think of machine learning as an emerging technology that will have a big impact on our lives in the not too distant future through applications like autonomous driving. Everywhere I look, however, I see areas where machine learning will affect our lives much sooner in a myriad of smaller ways.

A recent article in Wired described one such example. It told about the work some MIT and Google researchers have done using machine learning to retouch photos. I would do this by using a photo editing program to do something like adjust the color saturation of a whole photo. Instead, their algorithm applies different filters to different parts of a photo. So, faces in the foreground might get different treatment than the sunset in the background.

The researchers train the neural network using professionally retouched photos. I love the idea of a program that automatically improves the look of my less-than-professional personal photos.

What I found more exciting, however, is that the researchers could make their software efficient enough to run on a smartphone in a fraction of a second. That makes it significantly more useful.

This technology is not yet available, but it seems like something that could show up in existing photo or camera apps before long. I hope to see it soon on a smartphone in my hand!

All of that made me think about how we might incorporate such an algorithm in the XPRTs. When I started reading the article, I was thinking it might fit well in our upcoming machine-learning XPRT. By the time I finished it, however, I realized it might belong in a future version of one of the other XPRTs, like MobileXPRT. What do you think?

Bill

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