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CPU Sizing vs. Latency Analysis FTS EDR Latency Simulation. 5 March 2008 Doug Shannon. Contents. FTS Latency – Simulation & Analyses IDPS NPP Status ATDS/FTS Simulation Overview Example Simulation Results ATDS/FTS Demo FTS HRD/LRD Latency Requirements:

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CPU Sizing vs. Latency Analysis FTS EDR Latency Simulation

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CPU Sizing vs. Latency Analysis FTSEDRLatency Simulation

5 March 2008

Doug Shannon


Contents

  • FTS Latency – Simulation & Analyses

    • IDPS NPP Status

    • ATDS/FTS Simulation Overview

    • Example Simulation Results

    • ATDS/FTS Demo

  • FTS HRD/LRD Latency Requirements:

    • SYS013230 The LRD Field Terminal software, when installed on NPOESS representative hardware, shall produce Imagery EDRs within 2 minutes and all other EDRs specified in Appendix G within 15 minutes of receipt of mission data. Class 2

    • SYS013235 The HRD Field Terminal software, when installed on NPOESS representative hardware, shall produce Imagery EDRs within 2 minutes and all other EDRs specified in Appendix E, except for EDRs 40.3.1.4, 40.4.10, 40.7.5, and 40.7.8, within 15 minutes of receipt of mission data. Class 2


IDPS NPP Status

  • IDPS NPP Build 1.5

    • 1 orbit NPP processing (101 mins) – 53 mins

      • Meets EDR latencies (117.2 mins for 140 mins requirement)

      • Major speedups in DMS performance

      • Algorithm development & integration “95%complete”

    • Future Builds 1.5.x.1 (3Q 08), B1.5.x.2 (2Q 09).

      • OMPS, NHF, combined Albedo, Bright Pixel

      • Move LSA Granulation out of VIIRS SDR (1.5.x.1) to improve IMG latency

    • ATDS/FTS getting new benchmarks on B1.5 algorithms

      • Faster processing?

      • Less algorithm sensitivity to scene content?


Algorithm Timing & Dependency Simulation Field Terminal Latency Analyses

  • ATDS supports NPP, NPOESS/NPP & NPOESS performance analyses

  • FTS latency simulations differences:

    • Receives C1/C2 LRD or HRD in real time; no stored data

      • Sensors collect at 9.1 & 5.0 Mbps (average day/night)

    • Various FTS locations and weather/terrain conditions

    • Smaller EDR granules (NPP 85.7sec & NPOESS 42.9sec)

    • Processing Architecture -

      • Split SDR - generate IMG sooner, after SDR Cal/Geo, before granulation

      • Pre-load SDR static ancil/aux tiles (TBD) to reduce latency

      • Assume no/minimal cross-granule dependency


VIIRS Cross-Granule Latency Tiers

+3

SDR

+2

+4

+5

+1

+3

+4

+2

+3

+4

+5


FTS Simulation (e.g. Omaha):2 day 19 Passes with NPOESS S/C

Contact Durations:

Max 13.1 mins

Avg 10.5 mins

Min 2 mins?

<4mins 2.3%

13301730

FTS Contacts with NPOESS S/C

(1440 minutes = 1 days)


Scene in VIIRS View

Ocean

Cloudy

Snow/Ice

Orbital Position Defines Dynamic Scene Content in Sensor Data

Orbital Position defines Sensor Nadir

NCEP Weather Data Base

Dynamic Processing


Impact of Weather/Terrain on FTS Data

  • Algorithm loading for Clear-Ocean is heaviest,21% over average.

  • NCEP weather DB for Spring 2003

    • 90-100% ocean – 41%

    • 90-100% clear – 8%

    • Clear & Ocean – 3%

  • User can’t select his weather/terrain

    • ATSD can analyze user FTS locations & helpsize for field conditions

>90% Clear

>90% ocean


Algorithm, Timing & Dependency Simulator:FTS IDPS and Algorithm Models

S/W

Science Algorithms

H/W


Example ATDS Simulation results – Omaha FTS scenario

  • Peak demand (17 CPUs) not equal to CPU requirement.

    • 2.6 GHz CPUs

  • CPU resources driven by contact length & S/C sensors.

    • No ATMS & CrIS on C2


Example ATDS Simulation results – Omaha FTS scenario

  • EDR latencies are dynamic as scene content varies

    • Shows last VIIRS EDR for multiple granules


Example ATDS Simulation results – Omaha FTS scenario

  • Latencies varied 1.5 – 7.7 mins

    • Imagery latency ~3.3 mins

FTS IMG


On-going ATDS/FTS Trades

  • Variable number of CPUs & processor speeds

  • Smaller VIIRS/CrIMSS granules

    • Science implications for processing areas and adjacency.

  • Weather/Terrain impact on IDPS Latency

    • Various FTS locations

    • Various weather & terrain conditions

  • SDR architectural trades

  • Selectable EDR configurations

    • HRD vs LRD algorithms

    • Generate high priority top EDRs only

    • Generate Imagery only


VIIRS HRD vs LRD Algorithm Processing

11%

10%

2%

0.3%

14%/10

2%

26%

9%

1%

5%

5%


Summary

  • Due to algorithm scene sensitivity, highly variable weather/terrain are significant factors for latency and CPUs required.

    • Some new IDPS benchmarks show less than expected sensitivity.

  • Ongoing IDPS algorithm optimization are improving FTS latencies.

    • Improvements to IDPS Infrastructure (DMS) are very good but don’t apply directly to FTS.

  • We continue to add fidelity to our ATDS simulations, bounding nominal performance against worst-case scenarios in order to quantify system processor needs.


Backups

  • 2005 back-to-back S/C contacts and gap analysis


Gap Time Between Contacts

Max gap is 2.1 orbits at equator

Analyzed STK 1330/1730/2130 contact data

Back-to-back S/C Contacts

  • Overlapping S/C contacts don’t occur due to spacecraft orbital phasing.

  • Smallest gap of 10.2 minutes has minimal impact to FTS latency.

  • Above 60N there is a large increase in contacts and EDRs.

60N


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