Category: AI

Notes from the lab: Updates on HDXPRT 4, MobileXPRT 3, and AIXPRT

on November 29, 2018

The next couple of months will be very busy with XPRT activity, so we want to update readers on what to expect. Depending on a number of factors, we expect to release HDXPRT 4 and MobileXPRT 3 community previews (CPs) within the next four to six weeks. We’re also hoping to publish an early AIXPRT request-for-comment (RFC) build on GitHub within the same time frame. Here’s a little more detail about each of these developments.

HDXPRT 4: We originally planned to release the HDXPRT 4 CP several weeks ago. As we recently discussed in the blog, a lot has changed in the Windows 10 development world within a short period of time, and Microsoft has released a number of new Redstone 5/October 2018 Update builds in quick succession. While our HDXPRT 4 CP candidate testing went well overall, we observed some inconsistent workload scores when testing on some of the new Windows builds. Since then, we believe we’ve narrowed down the list of possible causes to a few specific graphics driver versions, but we’re still testing to make sure there are no other immediate issues. As soon as we’re confident in that assessment, we’ll release the CP along with any relevant information about the affected graphics drivers.

MobileXPRT 3: MobileXPRT 3 development is progressing nicely, and we’re close to completing a CP candidate build. We’ll test that build extensively on our library of Android phones and tablets, and barring any unforeseen issues, we plan to release the CP in the next few weeks.

AIXPRT: AIXPRT is the umbrella name for a set of tools we’re developing to help evaluate machine learning performance. After a great deal of research, we’re getting closer to releasing a build – tentatively called the AIXPRT RFC – for community members and other interested parties to download and review. For a number of reasons, the AIXPRT RFC process will be a little different than our normal XPRT RFC and CP process. We’ll be offering more information on the AIXPRT RFC build over the next several weeks.

We’re grateful to everyone who’s contributed in any way to each of these projects, and we look forward to sharing the benchmarks with the world. If you have any questions about the XPRTs, please don’t hesitate to ask!

Justin

Posted in AI, AIXPRT, Android, BenchmarkXPRT development community, HDXPRT, HDXPRT 4, HDXPRT development process, HDXPRT release cycle, MobileXPRT, MobileXPRT 3, Windows 10 |

News from the MobileXPRT 3 team

By Justin Greene

on September 27, 2018

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

Posted in AI, Android, Benchmarking, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Machine learning, Mobile devices, MobileXPRT, MobileXPRT 2015, MobileXPRT 3, Performance benchmarking |

XPRTs in the datacenter

By Bill Catchings

on September 6, 2018

The XPRTs have been very successful on desktops, notebooks, tablets, and phones. People have run WebXPRT over 295,000 times. It and other benchmarks such as MobileXPRT, HDXPRT, and CrXPRT are important tools globally for evaluating device performance on various consumer and business client platforms.

We’ve begun branching out with tests for edge devices with AIXPRT, our new artificial intelligence benchmark. While typical consumers won’t be able to run AIXPRT on their devices initially, we feel that it is important for the XPRTs to play an active role in a critical emerging market. (We’ll have some updates on the AIXPRT front in the next few weeks.)

Recently, both community members and others have asked about the possibility of the XPRTs moving into the datacenter. Folks face challenges in evaluating the performance and suitability to task of such datacenter mainstays as servers, storage, networking infrastructure, clusters, and converged solutions. These challenges include the lack of easy-to-run benchmarks, the complexity and cost of the equipment (multi-tier servers, large amounts of storage, and fast networks) necessary to run tests, and confusion about best testing practices.

PT has a lot of expertise in measuring datacenter performance, as you can tell from the hundreds of datacenter-focused test reports on our website. We see great potential in our working with the BenchmarkXPRT Development Community to help in this area. It is very possible that, as with AIXPRT, our approach to datacenter benchmarks would differ from the approach we’ve taken with previous benchmarks. While we have ideas for useful benchmarks we might develop down the road, more immediate steps could be drafting white papers, developing testing guidelines, or working with vendors to set up a lab.

Right now, we’re trying to gauge the level of interest in having such tools and in helping us carry out these initiatives. What are the biggest challenges you face in datacenter-focused performance and suitability to task evaluations? Would you be willing to work with us in this area? We’d love to hear from you and will be reaching out to members of the community over the coming weeks.

As always, thanks for your help!

Bill

Posted in AI, AIXPRT, Benchmarking, Collaborative benchmark development, Datacenter, Future of performance evaluation, Networking, Performance benchmarking, Principled Technologies, Servers, Storage | Tagged benchmark, cluster, converged solutions, Data centers, networking, servers, storage |

AI and the next MobileXPRT

By Justin Greene

on August 16, 2018

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

Posted in AI, Android, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Future of performance evaluation, Google, Machine learning, Mobile devices, MobileXPRT, Performance benchmarking | Tagged AI, benchmark, Google, image processing, mobile, MobileXPRT, optical character recognition |

MWCS18 and AIXPRT: a new video

By Justin Greene

on July 12, 2018

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!

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

Posted in 5G, AI, AIXPRT, AR, Augmented reality, Future of performance evaluation, Machine learning, Mobile devices, Mobile World Congress, Mobile World Congress, Performance benchmarking, Virtual reality | Tagged 5G, AI, AIXPRT, MWCS18, Shanghai, VR |

Thoughts from MWC Shanghai 2018

By Bill Catchings

on June 28, 2018

Ni hao from Shanghai! It is amazing the change that happens in a year. This year’s MWC Shanghai, like last year’s, took up about half of the Shanghai New International Expo Centre (SNIEC). “5G +” is the major theme and, unlike last year, 5G is not something in the distant future. It is now assumed to be in progress.

The biggest of the pluses was AI, with a number of booths explicitly sporting 5G + AI signage. There were also 5G plus robots, cars, and cloud services. Many of those are really about AI as well. The show makes it feel like 5G is everywhere and will make everything better (or at least a lot faster). And Asia is leading the way.

[caption id="attachment_3447" align="alignleft" width="640"]

5G + robotics at MWCS 18.[/caption]

Most of the booths touted their 5G support as they did last year, but rather than talking about the future, they tried to say that their 5G was now. They claimed their products were in real-world tests with anticipated deployment schedules. One of the keynote speakers talked about 1.2 billion 5G connections by 2025, with more than half of those in Asia. The purported scale and speed of the transition to 5G is staggering.

[caption id="attachment_3449" align="alignleft" width="640"]

The keynote stage, displaying some big numbers.[/caption]

The last two halls I visited showed that world is not all 5G and AI. These halls looked at current fun applications of mobile technologies and companies developing technologies in the near future. MWC allowed children into one of the halls, where they (and we adults) could fly drones and experience VR technology. I watched in some amusement as people crashed drones, rode bikes with VR gear to simulate horses, were 3D scanned, and generally tried out new tech that didn’t always work.

The second hall included small booths from new companies working on future technologies that might be ready “4 years from now” (4YFN). These companies did not have much to show yet, but each booth displayed the company name and a short phrase summing up their future tech. That led to “Deepscent Labs is a smart scent data company,” ChineSpain is a “Marketplace of experiences for Chinese tourists in Spain,” and “Juice is a tech-based music contents startup that creates an ecosystem of music.” The mind boggles!

The XPRTs’ foray into AI with AIXPRT seems well timed based on this show. Other areas from this show that may be worth considering for the XPRTs are 5G and the cloud. We would love to hear your thoughts on those areas. We know they are important, but do you need the XPRTs and their emphasis on real-world benchmarks and workloads in those areas? Drop us a line and let us know!

Bill

Posted in 5G, AI, AIXPRT, AR, Augmented reality, Automated cars, Future of performance evaluation, IoT, Mobile data, Mobile devices, Mobile World Congress, Virtual reality | Tagged 5G, AI, China, mobile, Mobile World Shanghai, MWC, Shanghai, VR |

Category: AI

Notes from the lab: Updates on HDXPRT 4, MobileXPRT 3, and AIXPRT

News from the MobileXPRT 3 team

XPRTs in the datacenter

AI and the next MobileXPRT

MWCS18 and AIXPRT: a new video

Thoughts from MWC Shanghai 2018

Check out the other XPRTs: