Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors.

To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark numenta-nab.

Project Site

github.com

Test Created

9 November 2018

Last Updated

28 February 2020

Test Maintainer

Michael Larabel 

Test Type

System

Average Install Time

5 Minutes, 7 Seconds

Average Run Time

19 Minutes, 38 Seconds

Test Dependencies

Python

Accolades

20k+ Downloads

Supported Platforms


Public Result Uploads *Reported Installs **Reported Test Completions **Test Profile Page Views ***OpenBenchmarking.orgEventsNumenta Anomaly Benchmark Popularity Statisticspts/numenta-nab2018.112019.012019.032019.052019.072019.092019.112020.012020.032020.052020.072020.092020.112021.012021.032021.052021.072021.092021.112022.012022.032022.052022.072022.092022.117001400210028003500
* 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 current as of 2 December 2022.
Windowed Gaussian19.7%EXPoSE12.3%Relative Entropy23.0%Earthgecko Skyline21.8%Bayesian Changepoint23.1%Detector Option PopularityOpenBenchmarking.org

Revision History

pts/numenta-nab-1.1.0   [View Source]   Fri, 28 Feb 2020 12:04:17 GMT
Update against NAB 1.1 upstream for Python 3 compatibility.

pts/numenta-nab-1.0.0   [View Source]   Fri, 09 Nov 2018 11:55:56 GMT
Initial commit of Numenta Anomaly Benchmark

Suites Using This Test

Machine Learning

CPU Massive

Server CPU Tests

HPC - High Performance Computing

Python


Performance Metrics

Analyze Test Configuration:

Numenta Anomaly Benchmark 1.1

Detector: Bayesian Changepoint

OpenBenchmarking.org metrics for this test profile configuration based on 623 public results since 28 February 2020 with the latest data as of 23 October 2022.

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.

Component
Percentile Rank
# Compatible Public Results
Seconds (Average)
81st
10
30 +/- 4
Mid-Tier
75th
> 31
59th
17
34 +/- 2
Median
50th
36
47th
3
38
39th
4
44 +/- 1
36th
11
45 +/- 2
27th
3
49 +/- 1
Low-Tier
25th
> 49
21st
3
57 +/- 7
14th
6
71 +/- 1
11th
6
78 +/- 10
3rd
4
209 +/- 1
OpenBenchmarking.orgDistribution Of Public Results - Detector: Bayesian Changepoint623 Results Range From 16 To 1732 Seconds1657981391802212623033443854264675085495906316727137547958368779189591000104110821123116412051246128713281369141014511492153315741615165616971738110220330440550

Based on OpenBenchmarking.org data, the selected test / test configuration (Numenta Anomaly Benchmark 1.1 - Detector: Bayesian Changepoint) has an average run-time of 4 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.

OpenBenchmarking.orgMinutesTime Required To Complete BenchmarkDetector: Bayesian ChangepointRun-Time714212835Min: 1 / Avg: 3.12 / Max: 32

Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.7%.

OpenBenchmarking.orgPercent, Fewer Is BetterAverage Deviation Between RunsDetector: Bayesian ChangepointDeviation246810Min: 0 / Avg: 0.69 / Max: 3

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.

IntelAMDOpenBenchmarking.orgRelative Core Scaling To BaseNumenta Anomaly Benchmark CPU Core ScalingDetector: Bayesian Changepoint246812161832641.46882.93764.40645.87527.344

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.

CPU Architecture
Kernel Identifier
Verified On
Intel / AMD x86 64-bit
x86_64
(Many Processors)
IBM POWER (PowerPC) 64-bit
ppc64le
POWER9 44-Core
ARMv7 32-bit
armv7l
ARMv7 Cortex-A53 4-Core, ARMv7 Cortex-A72 4-Core
ARMv8 64-bit
arm64
Apple M1
ARMv8 64-bit
aarch64
ARMv8 rev 0 8-Core, Ampere Altra ARMv8 Neoverse-N1 160-Core

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Apple M1 - Apple MacBook Pro - 16GB

macOS 11.7 - 20.6.0 - GCC 13.0.0 + Clang 13.0.0

1 System - 80 Benchmark Results

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macOS 11.7 - 20.6.0 - GCC 13.0.0 + Clang 13.0.0

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