Category: Collaborative benchmark development

Thinking ahead to WebXPRT 2017

on April 13, 2017

A few months ago, Bill discussed our intention to update WebXPRT this year. Today, we want to share some initial ideas for WebXPRT 2017 and ask for your input.

Updates to the workloads provide an opportunity to increase the relevance and value of WebXPRT in the years to come. Here are a few of the ideas we’re considering:

For the Photo Enhancement workload, we can increase the data sizes of pictures. We can also experiment with additional types of photo enhancement such as background/foreground subtraction, collage creation, or panoramic/360-degree image viewing.
For the Organize Album workload, we can explore machine learning workloads by incorporating open source JavaScript libraries into web-based inferencing tests.
For the Local Notes workload, we’re investigating the possibility of leveraging natural-brain libraries for language processing functions.
For a new workload, we’re investigating the possibility of using online 3D modeling applications such as Tinkercad.

For the UI, we’re considering improvements to features like the in-test progress bars and individual subtest selection. We’re also planning to update the UI to make it visually distinct from older versions.

Throughout this process, we want to be careful to maintain the features that have made WebXPRT our most popular tool, with more than 141,000 runs to date. We’re committed to making sure that it runs quickly and simply in most browsers and produces results that are useful for comparing web browsing performance across a wide variety of devices.

Do you have feedback on these ideas or suggestions for browser technologies or test scenarios that we should consider for WebXPRT 2017? Are there existing features we should ditch? Are there elements of the UI that you find especially useful or would like to see improved? Please let us know. We want to hear from you and make sure that we’re crafting a performance tool that continues to meet your needs.

Justin

Posted in BenchmarkXPRT development community, Browser-based benchmarks, Collaborative benchmark development, Future of performance evaluation, Let us know your thoughts, Machine learning, Performance benchmarking, WebXPRT, WebXPRT 2017, What makes a good benchmark? |

Looking under the hood

By Justin Greene

on April 6, 2017

In the next couple of weeks, we’ll publish the source code and build instructions for the latest HDXPRT 2014 and BatteryXPRT 2014 builds. Access to XPRT source code is one of the benefits of BenchmarkXPRT Development Community membership. For readers who may not know, this a good time to revisit the reasons we make the source code available.

The primary reason is transparency; we want the XPRTs to be as open as possible. As part of our community model for software development, the source code is available to anyone who joins the community. Closed-source benchmark development can lead some people to infer that a benchmark is biased in some way. Our approach makes it impossible to hide any biases.

Another reason we publish source code is to encourage collaborative development and innovation. Community members are involved in XPRT development from the beginning, helping to identify emerging technologies in need of reliable benchmarking tools, suggesting potential workloads and improvements, reviewing design documents, and offering all sorts of general feedback.

Simply put, if you’re interested in benchmarking and the BenchmarkXPRT Development Community, then we’re interested in what you have to say! Community input helps us at every step of the process, and ultimately helps us to create benchmarking tools that are as reliable and relevant as possible.

If you’d like to review XPRT source code, but haven’t yet joined the community, we encourage you to go ahead and join! It’s easy, and if you work for a company or organization with an interest in benchmarking, you can join the community for free. Simply fill out the form with your company e-mail address and click the option to be considered for a free membership. We’ll contact you to verify the address is real and then activate your membership.

If you have any other questions about community membership or XPRT source code, feel free to contact us. We look forward to hearing from you!

Justin

Posted in BatteryXPRT 2014 for Android, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, HDXPRT, HDXPRT 2014, Open Source, Source code |

A new reality

By Eric Hale

on February 23, 2017

A while back, I wrote about a VR demo built by students from North Carolina State University. We’ve been checking it out over the last couple of months and are very impressed. This workload will definitely heat up your device! While the initial results look promising, this is still an experimental workload and it’s too early to use results in formal reviews or product comparisons.

We’ve created a page that tells all about the VR demo. As an experimental workload, the demo is available only to community members. As always, members can download the source as well as the APK.

We asked the students to try to build the workload for iOS as a stretch goal. They successfully built an iOS version, but this was at the end of the semester and there was little time for testing. If you want to experiment with iOS yourself, look at the build instructions for Android and iOS that we include with the source. Note that you will need Xcode to build and deploy the demo on iOS.

After you’ve checked out the workload, let us know what you think!

Finally, we have a new video featuring the VR demo. Enjoy!

Eric

Posted in Application-based benchmarks, BenchmarkXPRT development community, Collaborative benchmark development, Education, Future of performance evaluation, Mobile devices, North Carolina State University, Virtual reality, VR Demo, What makes a good benchmark? |

A new HDXPRT 2014 build is available

By Justin Greene

on February 9, 2017

Last fall, we identified a way to run HDXPRT 2014, originally developed for Windows 8, on Windows 10. The method involved overwriting the HDXPRT CPU-Z files with newer versions and performing a few additional pre-test configuration steps. You can read more details about those steps here.

Today, we’re releasing a new build of HDXPRT 2014 (v1.2) that eliminates the need to overwrite the CPU-Z files. The new build is available for download at HDXPRT.com. Please note that the app package is 5.08 GB, so allow time and space for the download process.

We also updated the HDXPRT 2014 User Manual to reflect changes in pre-test system configuration and to include the settings we recommend for newer builds of Windows 10.

The changes in the new build do not affect results, so v1.2 scores are comparable to v1.1 scores on the same system.

The new build ran well during testing in our labs, but issues could emerge as Microsoft releases new Windows updates. If you have any questions about HDXPRT or encounter any issues during testing, we encourage you to let us know.

We look forward to seeing your test results!

Justin

Posted in Application-based benchmarks, Collaborative benchmark development, HDXPRT, HDXPRT 2014, Performance benchmarking, Windows 10 |

Creating a machine-learning benchmark

By Bill Catchings

on December 22, 2016

Recently, we wrote about one of the most exciting emerging technology areas, machine learning, and the question of what role the XPRTs could play in the field.

Experts expect machine learning to be the analytics backbone of the IoT data explosion. It is a disruptive technology with potential to influence a broad range of industries. Consumer and industrial applications that take advantage of machine-learning advancements in computer vision, natural language processing, and data analytics are already available and many more are on the way.

Currently, there is no comprehensive machine-learning or deep-learning benchmark that includes home, automotive, industrial, and retail use cases. The challenge with developing a benchmark for machine learning is that these are still the early days of the technology. A fragmented software and hardware landscape and lack of standardized implementations makes benchmarking machine learning complex and challenging.

Based on the conversations we’ve had over the last few weeks, we’ve decided to take on that challenge. With the community’s help, of course!

As we outlined in a blog entry last month, we will work with interested folks in the community, key vendors, and academia to pull together what we are internally calling MLXPRT.

While the result may differ substantially from the existing XPRTs, we think the need for something is great. Whether that will turn out to be a packaged tool or just sample code and workloads remains to be seen.

What we need most your help. We need both general input about what you would like to see as well as any expertise you may have. Let us know any questions you may have or ways you can help.

On a related note, I’ll be at CES 2017 in Las Vegas during the first week of January. I’d love to meet and talk more about machine learning, benchmarking, or the XPRTs. If you’re planning to be there and would like to connect, let us know.

We will not have a blog entry next week over the holidays, so we wish all of you a wonderful time with your families and a great start to the new year.

Bill

Posted in BenchmarkXPRT, BenchmarkXPRT development community, CES, Collaborative benchmark development, Machine learning, What makes a good benchmark? |

Exploring virtual reality

By Eric Hale

on December 15, 2016

We’ve talked a lot in recent weeks about new technologies we are evaluating for the XPRTs. You may remember that back in June, we also wrote about sponsoring a second senior project with North Carolina State University. Last week, the project ended with the traditional Posters and Pies event. The team gave a very well thought‑out presentation.

As you can tell from the photo below, the team designed and implemented a nifty virtual reality app. It’s a room escape puzzle, and it looks great!

The app is a playable game with the ability to record the gameplay for doing repeatable tests. It also includes a recording that allows you to test a device without playing the game. Finally, the app lets you launch directly into the prerecorded game without using a viewer, which will be handy for testing multiple devices.

The team built the app using the Google Cardboard API and the Unity game engine, which allowed them to create Android and iOS versions. We’re looking forward to seeing what that may tell us!

After Posters and Pies, the team came to PT to present their work and answer questions. We were all very impressed with their knowledge and with how well thought out the application was.

Many thanks to team members Christian McCurdy, Gregory Manning, Grayson Jones, and Shon Ferguson (not shown).

Thanks also to Dr. Lina Battestilli, the team’s technical advisor, and Margaret Heil, Director of the Senior Design Center.

We are currently evaluating the app, and expect to make it available to the community in early 2017!

Eric

Posted in Android, Benchmarking, Collaborative benchmark development, iOS, North Carolina State University, Virtual reality |

Category: Collaborative benchmark development

Thinking ahead to WebXPRT 2017

Looking under the hood

A new reality

A new HDXPRT 2014 build is available

Creating a machine-learning benchmark

Exploring virtual reality

Check out the other XPRTs: