1 / 24

A METRICS System for Design Process Optimization

A METRICS System for Design Process Optimization. Andrew B. Kahng and Stefanus Mantik* UCSD CSE and ECE Depts., La Jolla, CA *UCLA CS Dept., Los Angeles, CA. Purpose of METRICS. Standard infrastructure for the collection and the storage of design process information

marlin
Download Presentation

A METRICS System for Design Process Optimization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A METRICS System for Design Process Optimization Andrew B. Kahng and Stefanus Mantik* UCSD CSE and ECE Depts., La Jolla, CA *UCLA CS Dept., Los Angeles, CA

  2. Purpose of METRICS • Standard infrastructure for the collection and the storage of design process information • Standard list of design metrics and process metrics • Analyses and reports that are useful for design process optimization METRICS allows: Collect, Data-Mine, Measure, Diagnose, then Improve

  3. Tool Tool Transmitter Java Applets wrapper Tool API Transmitter Transmitter XML Inter/Intra-net Web Server Data Mining DB Reporting Metrics Data Warehouse METRICS System Architecture

  4. XML Example <METRICS_LIST> <METRIC PID=“134” FID=“22” TID=“47”> <NAME>TOTAL_WIRELENGTH</NAME> <VALUE>14250347</VALUE> <TYPE>INTEGER</TYPE> <TIMESTAMP>010312:220512</TIMESTAMP> </METRIC> <METRIC PID=“134” FID=“22” TID=“47”> <NAME>TOTAL_CPU_TIME</NAME> <VALUE>2150.28</VALUE> <TYPE>DOUBLE</TYPE> <TIMESTAMP>010312:220514</TIMESTAMP> </METRIC> </METRICS_LIST>

  5. Wrapper-based transmitter #!/usr/local/bin/perl -w $TOOL = $0; $PID = `initProject`; $FID = `initFlow -pid ${PID}`; $TID = `initToolRun -pid ${PID} -fid ${FID}`; system “sendMetrics TOOL_NAME ${TOOL}\ STRING”; … while(<IN>) { … system “sendMetrics ${NAME} ${VALUE}\ ${TYPE}”; … } system “terminateToolRun”; system “terminateFlow -pid ${PID} -fid ${FID}”; system “terminateProject -pid ${PID}”; exit 0; API-based transmitter #include “transmitter.h” int main(int argc, char* argv[]) { Transmitter MTR; MTR.initProject(); MTR.initFlow(); MTR.initToolRun(); MTR.sendMetrics(“TOOL_NAME”, argv[0],\ “STRING”); … MTR.sendMetrics(Name, Value, Type); … MTR.terminateToolRun(); MTR.terminateFlow(); MTR.terminateProject(); exit 0; } Transmitter Examples

  6. donkey 2% rat 1% bull 2% synthesis 20% ATPG 22% postSyntTA 13% BA 8% funcSim 7% hen 95% placedTA 7% physical 18% LVS 5% % aborted per task % aborted per machine Example Reports CPU_TIME = 12 + 0.027 NUM_CELLS Correlation = 0.93

  7. Report EJB DB XFace EJB X’Mit EJB METRICS Server Apache Reports Reporting Servlets Oracle 8i Requests Transmitter Servlets

  8. Open Source Architecture • METRICS components are industry standards • e.g., Oracle 8i, Java servlets, XML, Apache web server, PERL/TCL scripts, etc. • Custom generated codes for wrappers and APIs are publicly available • collaboration in development of wrappers and APIs • porting to different operating systems • Codes are available at: http://vlsicad.cs.ucla.edu/GSRC/METRICS

  9. METRICS Standards • Standard metrics naming across tools • same name «same meaning, independent of tool supplier • generic metrics and tool-specific metrics • no more ad hoc, incomparable log files • Standard schema for metrics database • Standard middleware for database interface • For complete current lists see: http://vlsicad.cs.ucla.edu/GSRC/METRICS

  10. Generic and Specific Tool Metrics Partial list of metrics now being collected in Oracle8i

  11. Flow Metrics • Tool metrics alone are not enough • Design process consists of more than one tool • A given tool can be run multiple times • Design quality depends on the design flow and methodology (the order of the tools and the iteration within the flow) • Flow definition • Directed graph G (V,E) • V T  { S, F } • T  { T1, T2, T3, …, Tn } (a set of tasks) • S  starting node, F  ending node • E  { Es1, E11, E12, …, Exy } (a set of edges) • Exy • x < y  forward path • x = y  self-loop • x > y  backward path

  12. S T1 T1 T1 T2 T2 T2 T2 T3 T3 T3 T4 T4 F Flow Example S T1 T2 T3 Optional task T4 F Task sequence: T1, T2, T1, T2, T3, T3, T3, T4, T2, T1, T2, T4

  13. S T1 T1 T1 T2 T2 T2 T2 T3 T3 T3 T4 T4 F Flow Tracking Task sequence: T1, T2, T1, T2, T3, T3, T3, T4, T2, T1, T2, T4

  14. S T1 T2 T3 T4 T5 T6 T7 T8 F Chip Design Flow Example • Simple chip design flow • T1 = synthesis & technology mapping • T2 = load wireload model (WLM) • T3 = pre-placement optimization • T4 = placement • T5 = post-placement optimization • T6 = global routing • T7 = final routing • T8 = custom WLM generation

  15. ... T1 T2 T3 Tn S F Optimization of Incremental Multilevel FM Partitioning • Motivation: Incremental Netlist Partitioning • Given: initial partitioning solution, CPU budget and instance perturbations (I) • Find: number of parts of incremental partitioning and number of starts • Ti = incremental multilevel FM partitioning • Self-loop  multistart • n  number of breakups (I = 1 + 2 + 3 + ... + n)

  16. Predicted CPU Time (secs) Actual CPU Time (secs) Flow Optimization Results • If (27401 < num edges  34826) and (143.09 < cpu time  165.28) and (perturbation delta  0.1) then num_inc_parts = 4 and num_starts = 3 • If (27401 < num edges  34826) and (85.27 < cpu time  143.09) and (perturbation delta  0.1) then num_inc_parts = 2 and num_starts = 1 • ...

  17. Java Servlet Java Servlet Datamining Integration Inter-/Intranet DM Requests SQL Results Tables Database Datamining Interface Datamining Tool(s) Tables Tables SQL Results

  18. Categories of Data for DataMining • Design instances and design parameters • attributes and metrics of the design instances • e.g., number of gates, target clock frequency, number of metal layers, etc. • CAD tools and invocation options • list of tools and user options that are available • e.g., tool version, optimism level, timing driven option, etc. • Design solutions and result qualities • qualities of the solutions obtained from given tools and design instances • e.g., number of timing violations, total tool runtime, layout area, etc.

  19. Possible Usage of DataMining • Design instances and design parameters • CAD tools and invocation options • Design solutions and result qualities • Given  and , estimate the expected quality of  • e.g., runtime predictions, wirelength estimations, etc. • Given  and , find the appropriate setting of  • e.g., best value for a specific option, etc. • Given  and , identify the subspace of  that is “doable” for the tool • e.g., category of designs that are suitable for the given tools, etc.

  20. Predicted CPU Time (secs) Actual CPU Time (secs) DM Results: QPlace CPU Time • If (num nets  7332) then CPU time = 21.9 + 0.0019 num cells + 0.0005 num nets + 0.07 num pads - 0.0002 num fixed cells • If (num overlap layers = 0) and (num cells  71413) and (TD routing option = false) then CPU time = -15.6 + 0.0888 num nets - 0.0559 num cells - 0.0015 num fixed cells - num routing layer • ...

  21. Synthesis & Tech Map Pre-placement Opt Post-placement Opt QP GRoute WRoute Testbed: Metricized Cadence PKS Flow M E T R I C S BuildGates

  22. NELSIS Flow Manager Integration • Flow managed by NELSIS

  23. Issues • Tool interface: each tool has unique interface • Security: proprietary and confidential information • Standardization: flow, terminology, data management, etc. • Cost of metrics collection: how many data are too many? • Other non-EDA tools: LSF, License Manager, etc. • Social: “big brother”, collection of social metrics, etc. • Bug detection: report the configuration that trigger the bugs, etc.

  24. Conclusions • Metrics collection should be automatic and transparent • API-based transmitter is the “best” approach • Ongoing work with EDA, designer communities to identify tool metrics of interest • users: metrics needed for design process insight, optimization • vendors: implementation of the metrics requested, with standardized naming / semantics

More Related