Category: Collaborative benchmark development

CloudXPRT is on the way

on January 16, 2020

A few months ago, we wrote about the possibility of creating a datacenter XPRT. In the intervening time, we’ve discussed the idea with folks both in and outside of the XPRT Community. We’ve heard from vendors of datacenter products, hosting/cloud providers, and IT professionals that use those products and services.

The common thread that emerged was the need for a cloud benchmark that can accurately measure the performance of modern, cloud-first applications deployed on modern infrastructure as a service (IaaS) platforms, whether those platforms are on-premises, hosted elsewhere, or some combination of the two (hybrid clouds). Regardless of where clouds reside, applications are increasingly using them in latency-critical, highly available, and high-compute scenarios.

Existing datacenter benchmarks do not give a clear indication of how applications will perform on a given IaaS infrastructure, so the benchmark should use cloud-native components on the actual stacks used for on-prem and public cloud management.

We are planning to call the benchmark CloudXPRT. Our goal is for CloudXPRT to address the needs described above while also including the elements that have made the other XPRTs successful. We plan for CloudXPRT to

Be relevant to on-prem (datacenter), private, and public cloud deployments
Run on top of cloud platform software such as Kubernetes
Include multiple workloads that address common scenarios like web applications, AI, and media analytics
Support multi-tier workloads
Report relevant metrics including both throughput and critical latency for responsiveness-driven applications and maximum throughput for applications dependent on batch processing

CloudXPRT’s workloads will use cloud-native components on an actual stack to provide end-to-end performance metrics that allow users to choose the best IaaS configuration for their business.

We’ve been building and testing preliminary versions of CloudXPRT for the last few months. Based on the progress so far, we are shooting to have a Community Preview of CloudXPRT ready in mid- to late-March with a version for general availability ready about two months later.

Over the coming weeks, we’ll be working on getting out more information about CloudXPRT and continuing to talk with interested parties about how they can help. We’d love to hear what workflows would be of most interest to you and what you would most like to see in a datacenter/cloud benchmark. Please feel free to contact us!

Bill

Posted in benchmark, BenchmarkXPRT, BenchmarkXPRT development community, Cloud, CloudXPRT, Collaborative benchmark development, Datacenter, Future of performance evaluation, Hosted cloud, Hybrid cloud, IaaS, On-premise, Performance benchmarking, Server | Tagged benchmark, cloud, CloudXPRT, datacenter, hosted cloud, hybrid cloud, IaaS, Kubernetes, on-premise |

AIXPRT’s unique development path

By Justin Greene

on December 12, 2019

With four separate machine learning toolkits on their own development schedules, three workloads, and a wide range of possible configurations and use cases, AIXPRT has more moving parts than any of the XPRT benchmark tools to date. Because there are so many different components, and because we want AIXPRT to provide consistently relevant evaluation data in the rapidly evolving AI and machine learning spaces, we anticipate a cadence of AIXPRT updates in the future that will be more frequent than the schedules we’ve used for other XPRTs in the past. With that expectation in mind, we want to let AIXPRT testers know that when we release an AIXPRT update, they can expect minimized disruption, consideration for their testing needs, and clear communication.

Minimized disruption

Each AIXPRT toolkit (Intel OpenVINO, TensorFlow, NVIDIA TensorRT, and Apache MXNet) is on its own development schedule, and we won’t always have a lot of advance notice when new versions are on the way. Hypothetically, a new version of OpenVINO could release one month, and a new version of TensorRT just two months later. Thankfully, the modular nature of AIXPRT’s installation packages ensures that we won’t need to revise the entire AIXPRT suite every time a toolkit update goes live. Instead, we’ll update each package individually when necessary. This means that if you only test with a single AIXPRT package, updates to the other packages won’t affect your testing. For us to maintain AIXPRT’s relevance, there’s unfortunately no way to avoid all disruption, but we’ll work to keep it to a minimum.

Consideration for testers

As we move forward, when software compatibility issues force us to update an AIXPRT package, we may discover that the update has a significant effect on results. If we find that results from the new package are no longer comparable to those from previous tests, we’ll share the differences that we’re seeing in our lab. As always, we will use documentation and versioning to make sure that testers know what to expect and that there’s no confusion about which package to use.

Clear communication

When we update any package, we’ll make sure to communicate any updates in the new build as clearly as possible. We’ll document all changes thoroughly in the package readmes, and we’ll talk through significant updates here in the blog. We’re also available to answer questions about AIXPRT and any other XPRT-related topic, so feel free to ask!

Justin

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, image classification, Machine learning, OpenVINO, Performance benchmarking, TensorRT | Tagged AI, AIXPRT, Apache MXNet, benchmark, Intel OpenVINO, machine learning, NVIDIA TensorRT, TensorFlow |

Planning for the next CrXPRT

By Justin Greene

on December 5, 2019

We’re currently planning the next version of CrXPRT, our benchmark that evaluates the performance and battery life of Chromebooks. If you’re unfamiliar with CrXPRT, you can find out more about how it works both here in the blog and at CrXPRT.com. If you’ve used CrXPRT, we’d love to hear any suggestions you may have. What do you like or dislike about CrXPRT? What features do you hope to see in a new version?

When we begin work on a new version of any benchmark, one of our first steps is to determine whether the workloads will provide value during the years ahead. As technology and user behavior evolve, we update test content to be more relevant. One example is when we replace photos with ones that use more contemporary file resolutions and sizes.

Sometimes the changing tech landscape prompts us to remove entire workloads and add new ones. The Photo Collage workload in CrXPRT uses Portable Native Client (PNaCl) technology, for which the Chrome team will soon end support. CrXPRT 2015 has a workaround for this issue, but the best course of action for the next version of CrXPRT will be to remove this workload altogether.

The battery life test will also change. Earlier this year, we started to see unusual battery life estimates and high variance when running tests at CrXPRT’s default battery life test length of 3.5 hours, so we’ve been recommending that users perform full rundowns instead. In the next CrXPRT, the battery life test will require full rundowns.

We’ll also be revamping the CrXPRT UI to improve the look of the benchmark and make it easier to use, as we’ve done with the other recent XPRT releases.

We really do want to hear your ideas, and any feedback you send has a chance to shape the future of the benchmark. Let us know what you think!

Justin

Posted in Battery life, benchmark, Benchmarking, BenchmarkXPRT, Browser-based benchmarks, Chrome OS, Chromebooks, Collaborative benchmark development, CrXPRT, Google Chrome, Performance benchmarking | Tagged battery life, benchmark, Chrome, Chrome OS, Chromebooks, CrXPRT |

How to use alternate configuration files with AIXPRT

By Justin Greene

on October 10, 2019

In last week’s AIXPRT Community Preview 3 announcement, we mentioned the new public GitHub repository that we’re using to publish AIXPRT-related information and resources. In addition to the installation readmes for each AIXPRT installation package, the repository contains a selection of alternative test config files that testers can use to quickly and easily change a test’s parameters.

As we discussed in previous blog entries about batch size, levels of precision, and number of concurrent instances, AIXPRT testers can adjust each of these key variables by editing the JSON file in the AIXPRT/Config directory. While the process is straightforward, editing each of the variables in a config file can take some time, and testers don’t always know the appropriate values for their system. To address both of these issues, we are offering a selection of alternative config files that testers can download and drop into the AIXPRT/Config directory.

In the GitHub repository, we’ve organized the available config files first by operating system (Linux_Ubuntu and Windows) and then by vendor (All, Intel, and NVIDIA). Within each section, testers will find preconfigured JSON files set up for several scenarios, such as running with multiple concurrent instances on a system’s CPU or GPU, running with FP32 precision instead of FP16, etc. The picture below shows the preconfigured files that are currently available for systems running Ubuntu on Intel hardware.

Because potential AIXPRT use cases cut across a wide range of hardware segments, including desktops, edge devices, and servers, not all AIXPRT workloads and configs will be applicable to each segment. As we move towards the AIXPRT GA, we’re working to find the best way to parse out these distinctions and communicate them to end users. In many cases, the ideal combination of test configuration variables remains an open question for ongoing research. However, we hope the alternative configuration files will help by giving testers a starting place.

If you experiment with an alternative test configuration file, please note that it should replace the existing default config file. If more than one config file is present, AIXPRT will run all the configurations and generate a separate result for each. More information about the config files and detailed instructions for how to handle the files are available in the EditConfig.md document in the public repository.

We’ll continue to keep everyone up to date with AIXPRT news here in the blog. If you have any questions or comments, please let us know.

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, Collaborative benchmark development, Community Preview, GitHub, Intel, Linux, Machine learning, NVIDIA, Performance benchmarking, Ubuntu | Tagged AI, AIXPRT, batch size, concurrent instances, FP16, FP32, GitHub, Intel, NVIDIA, precision, Windows |

AIXPRT Community Preview 3 is here!

By Justin Greene

on October 3, 2019

We’re happy to announce that the AIXPRT Community Preview 3 (CP3) is now available! As we discussed in last week’s blog, testers can expect three significant changes in AIXPRT CP3:

We updated support for the Ubuntu test packages from Ubuntu version 16.04 LTS to version 18.04 LTS.
We added TensorRT test packages for Windows and Ubuntu. Previously, AIXPRT testers could test only the TensorFlow variant of TensorRT. Now, they can use TensorRT to test systems with NVIDIA GPUs.
We added the Wide and Deep recommender system workload with the MXNet toolkit for Ubuntu systems.

To access AIXPRT CP3, click this access link and submit the brief information form unless you’ve already done so for CP2. You will then gain access to the AIXPRT community preview page. (If you’re not already a BenchmarkXPRT Development Community member, we’ll contact you with more information about your membership.)

On the community preview page, a download table displays the currently available AIXPRT CP3 test packages. Locate the operating system and toolkit you wish to test, and click the corresponding Download link. For detailed installation instructions and information on hardware and software requirements for each package, click the corresponding Readme link. Instead of providing installation guide PDFs as we did for CP2, we are now directing testers to a public GitHub repository. The repository contains the installation readmes for all the test packages, as well as a selection of alternative test configuration files. We’ll discuss the alternative configuration files in more detail in a future blog post.

Note: Those who have access to the existing AIXPRT GitHub repository will be able to access CP3 in the same way as previous versions.

We’ll continue to keep everyone up to date with AIXPRT news here in the blog. If you have any questions or comments, please let us know.

Justin

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Future of performance evaluation, image classification, Machine learning, MXNet, NVIDIA, object detection, OpenVINO, Performance benchmarking, recommender system, ResNet-50, SSD-MobileNet v1, TensorFlow, TensorRT, Ubuntu, Wide and Deep | Tagged AI, AIXPRT, benchmark, image processing, machine learning, MXNet, object detection, OpenVINO, preview, recommender, TensorFlow, TensorRT, Ubuntu, Windows |

An update on AIXPRT development

By Justin Greene

on September 26, 2019

It’s been a while since we last discussed the AIXPRT Community Preview 3 (CP3) release schedule, so we want to let everyone know where things stand. Testing for CP3 has taken longer than we predicted, but we believe we’re nearly ready for the release.

Testers can expect three significant changes in AIXPRT CP3. First, we updated support for the Ubuntu test packages. During the initial development phase of AIXPRT, Ubuntu version 16.04 LTS (Long Term Support) was the most current LTS version, but version 18.04 is now available.

Second, we have added TensorRT test packages for Windows and Ubuntu. Previously, AIXPRT testers could test only the TensorFlow variant of TensorRT. Now, they can use TensorRT to test systems with NVIDIA GPUs.

Third, we have added the Wide and Deep recommender system workload with the MXNet toolkit. Recommender systems are AI-based information-filtering tools that learn from end user input and behavior patterns and try to present them with optimized outputs that suit their needs and preferences. If you’ve used Netflix, YouTube, or Amazon accounts, you’ve encountered recommender systems that learn from your behavior.

Currently, the recommender system workload in AIXPRT CP3 is available for Ubuntu testing, but not for Windows. Recommender system inference workloads typically run on datacenter hardware, which tends to be Linux based. If enough community members are interested in running the MXNet/Wide and Deep test package on Windows, we can investigate what that would entail. If you’d like to see that option, please let us know.

As always, if you have any questions about the AIXPRT development process, feel free to ask!

Justin

Posted in AI, AIXPRT, benchmark, Benchmarking, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Linux, Machine learning, MXNet, Performance benchmarking, Ubuntu, What makes a good benchmark?, Wide and Deep, Windows |

Category: Collaborative benchmark development

CloudXPRT is on the way

AIXPRT’s unique development path

Minimized disruption

Consideration for testers

Clear communication

Planning for the next CrXPRT

How to use alternate configuration files with AIXPRT

AIXPRT Community Preview 3 is here!

An update on AIXPRT development

Check out the other XPRTs: