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 time-series 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

Source Repository

github.com

Test Created

9 November 2018

Last Updated

14 December 2022

Test Maintainer

Michael Larabel 

Test Type

System

Average Install Time

58 Seconds

Average Run Time

18 Minutes, 10 Seconds

Test Dependencies

Python

Accolades

60k+ Downloads

Supported Platforms

Public Result Uploads *Reported Installs **Reported Test Completions **Test Profile Page Views ***OpenBenchmarking.orgEventsNumenta Anomaly Benchmark Popularity Statisticspts/numenta-nab2018.112019.012019.032019.052019.072019.092019.112020.012020.032020.052020.072020.092020.112021.012021.032021.052021.072021.092021.112022.012022.032022.052022.072022.092022.112023.012023.032023.052023.072023.092023.112024.012024.032024.052024.072024.092024.114K8K12K16K20K
* 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 20 November 2024.
KNN CAD6.8%Contextual Anomaly Detector OSE6.7%Windowed Gaussian18.1%EXPoSE7.0%Relative Entropy20.4%Earthgecko Skyline20.5%Bayesian Changepoint20.6%Detector Option PopularityOpenBenchmarking.org

Revision History

pts/numenta-nab-1.1.1   [View Source]   Wed, 14 Dec 2022 14:04:05 GMT
Fixes for running on modern Linux distributions.

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

HPC - High Performance Computing

CPU Massive

Server CPU Tests

Python


Performance Metrics

Analyze Test Configuration:

Numenta Anomaly Benchmark 1.1

Detector: Bayesian Changepoint

OpenBenchmarking.org metrics for this test profile configuration based on 731 public results since 28 February 2020 with the latest data as of 5 November 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.

Component
Percentile Rank
# Compatible Public Results
Seconds (Average)
AMD Ryzen 9 7950X3D 16-Core
100th
6
12 +/- 1
AMD Ryzen 9 7950X 16-Core
99th
9
13 +/- 2
AMD EPYC 9654 96-Core
96th
4
16
Intel Core i9-13900K
95th
6
17 +/- 1
2 x AMD EPYC 9554 64-Core
94th
3
17
2 x AMD EPYC 9654 96-Core
92nd
37
18 +/- 2
Intel Xeon Platinum 8490H
90th
4
19 +/- 1
2 x Intel Xeon Platinum 8490H
88th
9
20 +/- 2
AMD Ryzen Threadripper 3960X 24-Core
86th
5
22 +/- 3
AMD Ryzen 9 5950X 16-Core
86th
20
22 +/- 1
AMD Ryzen Threadripper 3970X 32-Core
83rd
11
23 +/- 2
AMD Ryzen 9 5900X 12-Core
82nd
14
23
AMD Ryzen Threadripper 3990X 64-Core
80th
13
24 +/- 2
AMD Ryzen 7 5800X3D 8-Core
79th
6
25 +/- 1
AMD Ryzen 7 7840U
78th
3
25
AMD Ryzen 9 3950X 16-Core
76th
10
26
Mid-Tier
75th
> 26
AMD EPYC 75F3 32-Core
75th
3
26
2 x Intel Xeon Platinum 8380
75th
6
26 +/- 3
2 x AMD EPYC 75F3 32-Core
74th
4
27
Intel Core i7-12700K
72nd
3
27 +/- 2
AMD Ryzen 9 3900XT 12-Core
71st
11
28
2 x AMD EPYC 7F52 16-Core
68th
3
29
AMD EPYC 7F52 16-Core
68th
8
29 +/- 1
AMD Ryzen 9 3900X 12-Core
67th
14
29 +/- 4
AMD EPYC 7F72 24-Core
67th
4
29 +/- 1
AMD Ryzen 7 5800X 8-Core
66th
7
30 +/- 2
2 x AMD EPYC 7713 64-Core
65th
5
30
2 x AMD EPYC 7763 64-Core
63rd
5
31
AMD EPYC 7713 64-Core
62nd
4
31
AMD EPYC 7763 64-Core
61st
4
31
Intel Core i9-10980XE
60th
9
32 +/- 2
AMD Ryzen 7 3800XT 8-Core
59th
8
32 +/- 1
AMD EPYC 7742 64-Core
57th
5
33
AMD EPYC 7502P 32-Core
57th
5
33
2 x AMD EPYC 7742 64-Core
57th
7
33 +/- 1
AMD EPYC 7542 32-Core
56th
4
33
AMD Ryzen 5 5600X 6-Core
56th
10
33 +/- 4
Intel Core i9-7980XE
55th
3
33
AMD EPYC 7642 48-Core
54th
6
33 +/- 1
AMD EPYC 7702 64-Core
54th
5
33
AMD EPYC 7662 64-Core
53rd
5
33
AMD EPYC 7532 32-Core
53rd
5
34
Median
50th
34
AMD EPYC 7552 48-Core
50th
5
34
Intel Core i9-10900K
49th
16
34 +/- 2
AMD EPYC 7402P 24-Core
49th
5
34
2 x Intel Xeon Gold 5220R
49th
9
35 +/- 1
AMD Ryzen 9 5900HX
49th
3
35
2 x Intel Xeon Platinum 8280
47th
6
35 +/- 1
Intel Xeon Gold 6258R
46th
3
35
Intel Xeon Platinum 8280
45th
3
35
AMD Ryzen 7 3700X 8-Core
45th
6
35 +/- 2
AMD EPYC 7F32 8-Core
43rd
8
36
AMD EPYC 7282 16-Core
40th
5
36
Intel Core i7-1280P
40th
3
37 +/- 1
AMD Ryzen 5 3600X 6-Core
39th
5
37 +/- 1
Intel Xeon Gold 6226R
38th
7
38 +/- 3
AMD EPYC 7272 12-Core
38th
5
38
Apple M1
38th
3
38
2 x AMD EPYC 7601 32-Core
37th
4
39 +/- 1
Intel Xeon Gold 5220R
35th
3
41
AMD Ryzen 7 PRO 6850U
35th
3
41 +/- 1
Intel Core i9-9900KS
33rd
8
42 +/- 3
AMD EPYC 7551 32-Core
32nd
3
44 +/- 1
Intel Xeon E-2288G
31st
4
44 +/- 1
AMD EPYC 7232P 8-Core
30th
6
45 +/- 1
Intel Core i5-10600K
29th
10
45 +/- 2
Intel Xeon Gold 5218
29th
3
46 +/- 5
Intel Core i7-8700K
27th
3
49 +/- 1
Ampere Altra ARMv8 Neoverse-N1 160-Core
27th
3
49
AMD Ryzen 7 4700U
26th
3
50
Low-Tier
25th
> 51
Intel Core i7-1185G7
24th
3
54
Intel Core i7-1165G7
23rd
3
57 +/- 1
Intel Core i5-9400F
23rd
3
57 +/- 7
Intel Core i7-10700T
22nd
5
60 +/- 2
Intel Core i7-9750H
21st
3
61 +/- 6
AMD Ryzen 5 5500U
21st
4
62 +/- 1
Intel Xeon E5-1680 v3
21st
3
62 +/- 3
Intel Core i7-5960X
20th
7
64 +/- 1
AMD Ryzen 5 4500U
18th
3
67
Intel Xeon E3-1270 v5
17th
3
70
POWER9 44-Core
17th
6
71 +/- 1
Intel Xeon E3-1280 v5
15th
3
74 +/- 1
Intel Core i7-4960X
15th
3
75
Intel Core i5-1135G7
14th
3
76 +/- 4
Intel Core i7-8565U
12th
4
85 +/- 2
Intel Xeon E3-1260L v5
12th
3
85
Intel Core i5-7600K
12th
3
86
AMD Ryzen 7 4800U
10th
4
92 +/- 1
Intel Core i7-9700TE
9th
4
97
Intel Core i3-7100
6th
3
144
Intel Pentium Gold G6400
6th
5
145 +/- 13
Intel Celeron G5900
4th
4
209 +/- 1
OpenBenchmarking.orgDistribution Of Public Results - Detector: Bayesian Changepoint731 Results Range From 10 To 1732 Seconds105192133174215256297338379420461502543584625666707748789830871912953994103510761117115811991240128113221363140414451486152715681609165016911732120240360480600

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: 3.41 / 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: 0.74 / Max: 4

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 Changepoint24681216182432641281.00832.01663.02494.03325.0415

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 Cortex-A76 4-Core, ARMv8 Cortex-A78E 6-Core, ARMv8 Neoverse-N1, ARMv8 rev 0 8-Core, Ampere Altra ARMv8 Neoverse-N1 160-Core, Rockchip ARMv8 Cortex-A76 6-Core

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ARMv8 Cortex-A76 - Raspberry Pi 5 Model B Rev 1.0 - Broadcom BCM2712

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11 Systems - 217 Benchmark Results

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