Category: NVIDIA

AIXPRT is here!

on November 1, 2019

We’re happy to announce that AIXPRT is now available to the public! AIXPRT includes support for the Intel OpenVINO, TensorFlow, and NVIDIA TensorRT toolkits to run image-classification and object-detection workloads with the ResNet-50 and SSD-MobileNet v1networks, as well as a Wide and Deep recommender system workload with the Apache MXNet toolkit. The test reports FP32, FP16, and INT8 levels of precision.

To access AIXPRT, visit the AIXPRT download page. There, a download table displays the AIXPRT 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 package’s Readme link. If you’re not sure which AIXPRT package to choose, the AIXPRT package selector tool will help to guide you through the selection process.

In addition, the Helpful Info box on AIXPRT.com contains links to a repository of AIXPRT resources, as well links to XPRT blog discussions about key AIXPRT test configuration settings such as batch size and precision.

We hope AIXPRT will prove to be a valuable tool for you, and we’re thankful for all the input we received during the preview period! If you have any questions about AIXPRT, please let us know.

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT, Cross-platform benchmarks, image classification, Linux, Machine learning, MXNet, NVIDIA, object detection, OpenVINO, Performance benchmarking, recommender system, ResNet-50, SSD-MobileNet v1, TensorFlow, TensorRT, Wide and Deep, Windows | Tagged AI, AIXPRT, benchmark, image classification, machine learning, MXNet, object detection, OpenVINO, recommender system, TensorFlow, TensorRT |

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 |

Navigating the AIXPRT Community Preview download page just got easier

By Justin Greene

on July 25, 2019

AIXPRT Community Preview 2 (CP2) has been generating quite a bit of interest among the BenchmarkXPRT Development Community and members of the tech press. We’re excited that the tool has piqued curiosity and that folks are recognizing its value for technical analysis. When talking with folks about test setup and configuration, we keep hearing the same questions:

How do I find the exact toolkit or package that I need?
How do I find the instructions for a specific toolkit?
What test configuration variables are most important for producing consistent, relevant results?
How do I know which values to choose when configuring options such as iterations, concurrent instances, and batch size?

In the coming weeks, we’ll be working to provide detailed answers to questions about test configuration. In response to the confusion about finding specific packages and instructions, we’ve redesigned the CP2 download page to make it easier for you to find what you need. Below, we show a snapshot from the new CP2 download table. Instead of having to download the entire CP2 package that includes the OpenVINO, TensorFlow, and TensorRT in TensorFlow test packages, you can now download one package at a time. In the Documentation column, we’ve posted package-specific instructions, so you won’t have to wade through the entire installation guide to find the instructions you need.

We hope these changes make it easier for people to experiment with AIXPRT. As always, please feel free to contact us with any questions or comments you may have.

Justin

Posted in AI, AIXPRT, AMD, benchmark, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Intel, Linux, NVIDIA, OpenVINO, TensorFlow, TensorRT, Windows 10 | Tagged AI, AIXPRT, Community Preview, CPU, GPU, Linux, machine learning, OpenVINO, TensorFlow, TensorRT |

A new HDXPRT 4 build is available!

By Justin Greene

on July 18, 2019

A few weeks ago, we announced that a new HDXPRT 4 build, v1.1, was on the way. This past Monday, we published the build on HDXPRT.com.

The new build includes an updated version of HandBrake, the commercial application that HDXPRT uses for certain video conversion tasks. HandBrake 1.2.2 supports hardware acceleration with AMD Video Coding Engine (VCE), Intel Quick Sync, and the NVIDIA video encoder (NVENC). By default, HDXPRT4 v1.1 uses the encoder available through a system’s integrated graphics, but testers can target discrete graphics by changing a configuration file flag before running the benchmark. HDXPRT will then use the encoder provided by the discrete graphics hardware. This configuration setting takes effect only when more than one of the supported encoders (VCE, QSV, or NVENC) is present on the system.

As we mentioned before, in all other respects, the benchmark has not changed. That means that, apart from a scenario where a tester changes the targeted graphics hardware, scores from previous HDXPRT 4 builds will be comparable to those from the new build.

The updated HDXPRT 4 User Manual contains additional information and instructions for changing the configuration file flag. Please contact us if you have any questions about the new build. Happy testing!

Justin

Posted in AMD, benchmark, BenchmarkXPRT development community, graphics, HDXPRT, HDXPRT 4, NVIDIA, Performance benchmarking, Windows 10 | Tagged AMD, graphics, HDXPRT, HDXPRT 4, Intel, NVENC, NVIDIA, QSV, VCE, Windows |

An updated HDXPRT 4 build is on the way

By Justin Greene

on July 3, 2019

HandBrake recently released a new version, v1.2.2, of their video conversion software. Among other improvements, the new version includes support for certain AMD (VCE) and NVIDIA (NVENC) hardware-accelerated video encoders. Because we include HandBrake as one of the commercial applications in the HDXPRT installer package, and because we want to keep HDXPRT 4 up-to-date for testers, we’ve put together a new HDXPRT 4 build: v1.1. It includes HandBrake 1.2.2’s new capabilities, and we’re currently testing it in the lab.

With the new build, testers will be able to choose whether HDXPRT’s HandBrake tasks target a system’s integrated or discrete graphics cards by changing a flag called “UseIntegrated” in the config file. In HDXPRT 4 v1.1, the flag is set to “true” by default, directing HandBrake to use the codec provided by the system’s integrated graphics hardware. On the other hand, if a system has both integrated and discrete graphics available, and a user sets the flag to “false,” HandBrake will use the codec provided by the discrete graphics.

This update allows users to compare the video conversion performance of different video codecs on the same system. In all other respects, the benchmark has not changed. So apart from a scenario where a tester changes the targeted graphics hardware, scores from previous HDXPRT 4 builds will be comparable to those from the new build.

We’ll let the community know as soon as the new build is available, and we’ll update the HDXPRT 4 User Manual to reflect the changes.

If you have any questions about the upcoming HDXPRT 4 build, please let us know!

Justin

Posted in AMD, Future of performance evaluation, HandBrake, HDXPRT, HDXPRT 4, HDXPRT capabilities, HDXPRT release cycle, NVIDIA, Performance benchmarking | Tagged AMD, codec, discrete, Handbrake, HDXPRT, HDXPRT 4, integrated, NVIDIA, video |

Category: NVIDIA

AIXPRT is here!

How to use alternate configuration files with AIXPRT

AIXPRT Community Preview 3 is here!

Navigating the AIXPRT Community Preview download page just got easier

A new HDXPRT 4 build is available!

An updated HDXPRT 4 build is on the way

Check out the other XPRTs: