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Category: Android

A new BatteryXPRT 2014 for Android build is available

In last week’s blog, we discussed why we now consider full BatteryXPRT rundown tests to be the most accurate and why we’re releasing a new build (v110) that increases the default BatteryXPRT test from 5.25 hours (seven iterations) to 45 hours (60 iterations). We also built v110 using Android Studio SDK 27, in order to bring BatteryXPRT up to date with current Android standards. Today, we’ve posted the new build on BatteryXPRT.com and in the Google Play Store, and we’ve also published an updated user manual. Please contact us if you have any questions about BatteryXPRT testing.

Justin

The new WebXPRT 3 Processor Comparison Chart

Last fall, we published the WebXPRT 2015 Processor Comparison Chart, a tool that makes it easier to access hundreds of PT-curated, real-world performance scores from a wide range of devices including everything from TVs to phones to tablets to PCs. Today, we’re happy to announce that we’ve added a WebXPRT 3 Processor Comparison Chart.

The WebXPRT 3 chart follows the same format as the WebXPRT 2015 chart, letting you click the average score of each processor to view all the WebXPRT 3 results we currently have for that processor. You can change the number of results the chart displays on each page, and as the screenshot below shows, a new drop-down menu lets you toggle back and forth between the WebXPRT 3 and WebXPRT 2015 charts. W plan to add additional capabilities on a regular basis, so if you have ideas for features and types of data you’d like to see, let us know!

If you’d like to submit results for us to consider for publication in the chart, follow the detailed instructions here. The submission process is quick and easy. We look forward to seeing your results!

WebXPRT 3 Proc Chart

Justin

A BatteryXPRT bug fix is on the way

Some time ago, we started to see unusual BatteryXPRT battery life estimates and high variance on some devices when running tests at the default length of 5.25 hours (seven 45-minute iterations). We suspected that the problem resulted from changes in how new OS versions report battery life on certain devices (e.g., charging past a reported level of 100 percent). In addition, the progress of battery technology in general means that the average phone battery lasts much longer than it did a few years ago. Together, these factors sometimes led to BatteryXPRT runs where the OS reported little to no battery decrease during the first few iterations of a test. We concluded that 5.25 hours wasn’t long enough to produce an accurate battery life estimate.

After extensive experimentation and testing, we’ve decided to release a new build that increases the default BatteryXPRT test length from 5.25 hours (seven iterations) to 45 hours (60 iterations) to allow enough time for a full rundown on most phones. Based on our testing, we consider full rundown tests to be the most accurate and will use those exclusively in our Spotlight testing and elsewhere. Testers will still have the option of choosing shorter test durations, but BatteryXPRT will flag the results with a qualifier that recommends performing a full rundown.

We plan to release the updated build by the end of next week and update BatteryXPRT documentation to reflect the changes. We have not changed any of the workloads and both performance results and full-rundown battery life estimates will be comparable to results from earlier builds.

BatteryXPRT continues to be a useful tool for gauging the performance and expected battery life of Android devices while simulating real-world tasks. If you have any questions about BatteryXPRT, please feel free to ask!

Justin

Updates on HDXPRT 4 and MobileXPRT 3

There’s a lot going on with the XPRTs, so we want to offer a quick update.

On the HDXPRT 4 front, we’re currently testing community preview candidate builds across a variety of laptops and desktops. Testing is going well, but as is often the case prior to a release, we’re still tweaking the code as necessary when we run into bugs. We’re excited about HDXPRT 4 and look forward to the community seeing how much faster and easier to use it is than previous versions. You can read more about what’s to come in HDXPRT 4 here.

On the MobileXPRT 3 front, proof-of-concept testing for the new and updated workloads went well, and we’re now working to implement the new UI. Below, you can see a mockup of the new MobileXPRT 3 start screen for phones. The aesthetic is completely different than MobileXPRT 2015, and is in line with the clean, bright look we used for WebXPRT 3 and HDXPRT 4. We’ve made it easy to select and deselect individual workloads by tapping the workload name (deselected workloads are grayed out), and we’ve consolidated common menu items into an Android-style taskbar at the bottom of the screen. Please note that this is an early view and some aspects of the screen will change. For instance, we’re certain that the final receipt-scanning workload won’t be called “Optical character recognition.”

We’ll share more information about HDXPRT 4 and MobileXPRT 3 in the coming weeks. If you have any questions about HDXPRT or MobileXPRT, or would like to share your ideas, please get in touch!

Justin

MobileXPRT-3-main-phone

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

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

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