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Category: AI

Here’s what to expect in the WebXPRT 4 Preview

Justin Greene
By Justin Greene
on December 8, 2021

A few months ago, we shared detailed information about the changes we expected to make in WebXPRT 4. We are currently doing internal testing of the WebXPRT 4 Preview build in preparation for releasing it to the public. We want to let our readers know what to expect.

We’ve made some changes since our last update and some of the details we present below could still change before the preview release. However, we are much closer to the final product. Once we release the WebXPRT 4 Preview, testers will be able to publish scores from Preview build testing. We will limit any changes that we make between the Preview and the final release to the UI or features that are not expected to affect test scores.

General changes

Some of the non-workload changes we’ve made in WebXPRT 4 relate to our typical benchmark update process.

  • We have updated the aesthetics of the WebXPRT UI to make WebXPRT 4 visually distinct from older versions. We did not significantly change the flow of the UI.
  • We have updated content in some of the workloads to reflect changes in everyday technology, such as upgrading most of the photos in the photo processing workloads to higher resolutions.
  • We have not yet added a looping function to the automation scripts, but are still considering it for the future.
  • We investigated the possibility of shortening the benchmark by reducing the default number of iterations from seven to five, but have decided to stick with seven iterations to ensure that score variability remains acceptable across all platforms.

Workload changes

  • Photo Enhancement. We increased the efficiency of the workload’s Canvas object creation function, and replaced the existing photos with new, higher-resolution photos.
  • Organize Album Using AI. We replaced ConvNetJS with WebAssembly (WASM) based OpenCV.js for both the face detection and image classification tasks. We changed the images for the image classification tasks to images from the ImageNet dataset.
  • Stock Option Pricing. We updated the dygraph.js library.
  • Sales Graphs. We made no changes to this workload.
  • Encrypt Notes and OCR Scan. We replaced ASM.js with WASM for the Notes task and updated the WASM-based Tesseract version for the OCR task.
  • Online Homework. In addition to the existing scenario which uses four Web Workers, we have added a scenario with two Web Workers. The workload now covers a wider range of Web Worker performance, and we calculate the score by using the combined run time of both scenarios. We also updated the typo.js library.

Experimental workloads

As part of the WebXPRT 4 development process, we researched the possibility of including two new workloads: a natural language processing (NLP) workload, and an Angular-based message scrolling workload. After much testing and discussion, we have decided to not include these two workloads in WebXPRT 4. They will be good candidates for us to add as experimental WebXPRT 4 workloads in 2022.

The release timeline

Our goal is to publish the WebXPRT 4 preview build by December 15th, which will allow testers to publish scores in the weeks leading up to the Consumer Electronics Show in Las Vegas in January 2022. We will provide more detailed information about the GA timeline here in the blog as soon as possible.

If you have any questions about the details we’ve shared above, please feel free to ask!

Justin

Posted in AI, benchmark, Benchmarking, BenchmarkXPRT, Browser-based benchmarks, face detection, Future of performance evaluation, image classification, JavaScript, Performance benchmarking, Web-based testing, WebAssembly, WebXPRT, webxprt 4 | Tagged , , , , , , , , |

Thinking about experimental WebXPRT workloads in 2022

Justin Greene
By Justin Greene
on November 18, 2021

As the WebXPRT 4 development process has progressed, we’ve started to discuss the possibility of offering experimental WebXPRT 4 workloads in 2022. These would be optional workloads that test cutting-edge browser technologies or new use cases. The individual scores for the experimental workloads would stand alone, and would not factor in the WebXPRT 4 overall score.

WebXPRT testers would be able to run the experimental workloads one of two ways: by manually selecting them on the benchmark’s home screen, or by adjusting a value in the WebXPRT 4 automation scripts.

Testers would benefit from experimental workloads by being able to compare how well certain browsers or systems handle new tasks (e.g., new web apps or AI capabilities). We would benefit from fielding workloads for large-scale testing and user feedback before we commit to including them as core WebXPRT workloads.

Do you have any general thoughts about experimental workloads for browser performance testing, or any specific workloads that you’d like us to consider? Please let us know.

Justin

Posted in AI, Automation, benchmark, Browser-based benchmarks, Future of performance evaluation, Performance benchmarking, Web-based testing, WebXPRT, webxprt 4, What makes a good benchmark? | Tagged , , , , , , , , |

The AIXPRT learning tool is now live (and a CloudXPRT version is on the way)!

Justin Greene
By Justin Greene
on December 10, 2020

We’re happy to announce that the AIXPRT learning tool is now live! We designed the tool to serve as an information hub for common AIXPRT topics and questions, and to help tech journalists, OEM lab engineers, and everyone who is interested in AIXPRT find the answers they need in as little time as possible.

The tool features four primary areas of content:

  • The Q&A section provides quick answers to the questions we receive most from testers and the tech press.
  • The AIXPRT: the basics section describes specific topics such as the benchmark’s toolkits, networks, workloads, and hardware and software requirements.
  • The testing and results section covers the testing process, metrics, and how to publish results.
  • The AI/ML primer provides brief, easy-to-understand definitions of key AI and ML terms and concepts for those who want to learn more about the subject.

The first screenshot below shows the home screen. To show how some of the popup information sections appear, the second screenshot shows the Inference tasks (workloads) entry in the AI/ML Primer section. 

We’re excited about the new AIXPRT learning tool, and we’re also happy to report that we’re working on a version of the tool for CloudXPRT. We hope to make the CloudXPRT tool available early next year, and we’ll post more information in the blog as we get closer to taking it live.

If you have any questions about the tool, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT, Cloud, CloudXPRT, computer vision, Cross-platform benchmarks, data analytics, image classification, Machine learning, object detection, OpenVINO, Performance benchmarking, TensorFlow, TensorRT | Tagged , , , , , , |

A first look at the upcoming AIXPRT learning tool

Justin Greene
By Justin Greene
on October 29, 2020

Last month, we announced that we’re working on a new AIXPRT learning tool. Because we want tech journalists, OEM lab engineers, and everyone who is interested in AIXPRT to be able to find the answers they need in as little time as possible, we’re designing this tool to serve as an information hub for common AIXPRT topics and questions.

We’re still finalizing aspects of the tool’s content and design, so some details may change, but we can now share a sneak peak of the main landing page. In the screenshot below, you can see that the tool will feature four primary areas of content:

  • The FAQ section will provide quick answers to the questions we receive most from testers and the tech press.
  • The AIXPRT basics section will describe specific topics such as the benchmark’s toolkits, networks, workloads, and hardware and software requirements.
  • The testing and results section will cover the testing process, the metrics the benchmark produces, and how to publish results.
  • The AI/ML primer will provide brief, easy-to-understand definitions of key AI and ML terms and concepts for those who want to learn more about the subject.

We’re excited about the new AIXPRT learning tool, and will share more information here in the blog as we get closer to a release date. If you have any questions about the tool, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, BenchmarkXPRT, Cross-platform benchmarks, Interactive tools, Machine learning, OpenVINO, Performance benchmarking, results, TensorFlow, TensorRT |

Following up

Justin Greene
By Justin Greene
on October 1, 2020

This week, we’re sharing news on two topics that we’ve discussed here in the blog over the past several months: CloudXPRT v1.01 and a potential AIXPRT OpenVINO update.

CloudXPRT v1.01

Last week, we announced that we were very close to releasing an updated CloudXPRT build (v1.01) with two minor bug fixes, an improved post-test results processing script, and an adjustment to one of our test configuration recommendations. Our testing and prep is complete, and the new version is live in the CloudXPRT GitHub repository and on our site!

None of the v1.01 changes affect performance or test results, so scores from the new build are comparable to those from previous CloudXPRT builds. If you’d like to know more about the changes, take a look at last week’s blog post.

The AIXPRT OpenVINO update

In late July, we discussed our plans to update the AIXPRT OpenVINO packages with OpenVINO 2020.3 Long-Term Support (LTS). While there are no known problems with the existing AIXPRT OpenVINO package, the LTS version targets environments that benefit from maximum stability and don’t require a constant stream of new tools and feature changes, so we thought it would be well suited for a benchmark like AIXPRT.

We initially believed that the update process would be relatively simple, and we’d be able to release a new AIXPRT OpenVINO package in September. However, we’ve discovered that the process is involved enough to require substantial low-level recoding. At this time, it’s difficult to estimate when the updated build will be ready for release. For any testers looking forward to the update, we apologize for the delay.

If you have any questions or comments about these or any other XPRT-related topics, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Cloud, CloudXPRT, Datacenter, GitHub, Machine learning, OpenVINO, Performance benchmarking | Tagged , , , , , |

We’re working on an AIXPRT learning tool

Justin Greene
By Justin Greene
on September 10, 2020

For anyone interested in learning more about AIXPRT, the Introduction to AIXPRT white paper provides detailed information about its toolkits, workloads, system requirements, installation, test parameters, and results. However, for AIXPRT.com visitors who want to find the answers to specific AIXPRT-related questions quickly, a white paper can be daunting.

Because we want tech journalists, OEM lab engineers, and everyone who is interested in AIXPRT to be able to find the answers they need in as little time as possible, we’ve decided to develop a new learning tool that will serve as an information hub for common AIXPRT topics and questions.

The new learning tool will be available online through our site. It will offer quick bites of information about the fundamentals of AIXPRT, why the benchmark matters, the benefits of AIXPRT testing and results, machine learning concepts, key terms, and practical testing concerns.

We’re still working on the tool’s content and design. Because we’re designing this tool for you, we’d love to hear the topics and questions you think we should include. If you have any suggestions, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, Benchmarking, Cross-platform benchmarks, Interactive tools, Machine learning, White papers | Tagged , , , , |

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