A METRICS System for Design Process OptimizationA 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 METRICSPurpose 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

METRICS System ArchitectureMETRICS System Architecture

Inter/Intra-net

DBMetrics Data Warehouse

WebServer

JavaApplets

DataMining

Reporting

Transmitter Transmitterwrapper

Tool Tool Tool

TransmitterAPI

XML

XML ExampleXML 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>

Transmitter ExamplesTransmitter Examples

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;

}

Example ReportsExample Reports

hen 95%

rat 1% bull 2%

donkey 2%

% aborted per machine

% aborted per task

BA 8%

ATPG 22%

synthesis 20%

physical18%

postSyntTA13%

placedTA7%

funcSim7%

LVS 5%

CPU_TIME = 12 + 0.027 NUM_CELLS

Correlation = 0.93

METRICS ServerMETRICS Server

Oracle 8i

Transmitter

Servlets

Reporting

Servlets

Apache

Requests

Reports

Report

EJB

DB XFace

EJBX’Mit

EJB

Open Source ArchitectureOpen 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

METRICS StandardsMETRICS 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

Generic and Specific Tool MetricsGeneric and Specific Tool Metrics

tool_name stringtool_version stringtool_vendor stringcompiled_date mm/dd/yyyystart_time hh:mm:ssend_time hh:mm:sstool_user stringhost_name stringhost_id stringcpu_type stringos_name stringos_version stringcpu_time hh:mm:ss

Generic Tool Metricsnum_cells integernum_nets integerlayout_size doublerow_utilization doublewirelength doubleweighted_wl double

num_layers integernum_violations integernum_vias integerwirelength doublewrong-way_wl doublemax_congestion double

Placement Tool Metrics

Routing Tool Metrics

Partial list of metrics now being collected in Oracle8i

Flow MetricsFlow 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

Flow ExampleFlow Example

S

T1

T2

T3

T4

F

Optional task

Task sequence: T1, T2, T1, T2, T3, T3, T3, T4, T2, T1, T2, T4

S

T1

T2

F

T1

T2

T3 T3 T3

T4

T2

T1

T2

T4

Flow TrackingFlow Tracking

Run Current FLOW_SEQUENCENo Task T1 T2 T3 T41 T1 1 - - - 12 T2 1 1 - - 1/13 T1 2 - - - 24 T2 2 1 - - 2/15 T3 2 1 1 - 2/1/16 T3 2 1 2 - 2/1/27 T3 2 1 3 - 2/1/38 T4 2 1 3 1 2/1/3/19 T2 2 2 - - 2/210 T1 3 - - - 311 T2 3 1 - - 3/112 T4 3 1 - 1 3/1/0/1

TASK_NOS

T1

T2

F

T1

T2

T3 T3 T3

T4

T2

T1

T2

T4

Task sequence: T1, T2, T1, T2, T3, T3, T3, T4, T2, T1, T2, T4

Chip Design Flow ExampleChip 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

ST1

T2

T3

T4

F

T5

T7

T8

T6

Optimization of Incremental Multilevel FM PartitioningOptimization 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)

S T1 FT2 T3 Tn...

Flow Optimization ResultsFlow 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

...

Actual CPU Time (secs)

Pre

dict

ed C

PU

Tim

e (s

ecs)

DesignName Optimized Regularibm01 215 217ibm05 1685 1723ibm02 249 269ibm03 618 669ibm06 363 371ibm04 444 488ibm08 1127 1219ibm10 752 773

Num Net Cut

Datamining IntegrationDatamining Integration

DatabaseDatamining

Tool(s)

Datamining

Interface

Java

Servlet

Java

Servlet

SQL

SQL

Tables

Tables Tables

Results

ResultsDM

Requests

Inter-/Intranet

Categories of Data for DataMiningCategories 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.

Possible Usage of DataMiningPossible 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.

DM Results: QPlace CPU TimeDM 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

...

Actual CPU Time (secs)

Pre

dict

ed C

PU

Tim

e (s

ecs)

Testbed: Metricized Cadence PKS FlowTestbed: Metricized Cadence PKS Flow

Synthesis & Tech Map

METRICS

Pre-placement Opt

GRoute

QP

Post-placement Opt

WRoute

BuildGates

NELSIS Flow Manager IntegrationNELSIS Flow Manager Integration

Flow managed by NELSIS

IssuesIssues

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.

ConclusionsConclusions

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