* Uploading of benchmark result data to OpenBenchmarking.org is always optional (opt-in) via the Phoronix Test Suite for users wishing to share their results publicly. ** Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform. *** Test profile page view reporting began March 2021. Data updated weekly as of 23 December 2024.
Revision History
pts/plaidml-1.0.4 [View Source] Thu, 26 Sep 2019 14:22:09 GMT Fixes for latest upstream PlaidML working around configuration files and library issues.
pts/plaidml-1.0.3 [View Source] Sun, 27 Jan 2019 16:21:22 GMT Set RequiresDisplay = FALSE
pts/plaidml-1.0.2 [View Source] Fri, 11 Jan 2019 12:06:07 GMT Always set --user for pip3 to avoid issues on some distros.
pts/plaidml-1.0.1 [View Source] Thu, 10 Jan 2019 14:30:27 GMT Add --train option which works in some configurations.
pts/plaidml-1.0.0 [View Source] Thu, 10 Jan 2019 10:51:47 GMT Initial commit of PlaidML deep learning framework benchmark, plaidbench.
FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
OpenBenchmarking.org metrics for this test profile configuration based on 653 public results since 15 October 2019 with the latest data as of 20 August 2024.
Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.
Based on OpenBenchmarking.org data, the selected test / test configuration (PlaidML - FP16: No - Mode: Inference - Network: VGG16 - Device: CPU) has an average run-time of 8 minutes. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.
Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.8%.
Does It Scale Well With Increasing Cores?
Yes, based on the automated analysis of the collected public benchmark data, this test / test settings does generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.
Tested CPU Architectures
This benchmark has been successfully tested on the below mentioned architectures. The CPU architectures listed is where successful OpenBenchmarking.org result uploads occurred, namely for helping to determine if a given test is compatible with various alternative CPU architectures.
