Tool Benchmarking Where are we? Justin E. Harlow III Semiconductor Research Corporation April 9, 2001.

Slides:



Advertisements
Similar presentations
Sampling Design, Spatial Allocation, and Proposed Analyses Don Stevens Department of Statistics Oregon State University.
Advertisements

© 2014 Synopsys. All rights reserved.1 Wheres my glass slipper? TAU 2014 Nanda Gopal Director R&D, Characterization.
Wish Branches Combining Conditional Branching and Predication for Adaptive Predicated Execution The University of Texas at Austin *Oregon Microarchitecture.
Data Mining Methodology 1. Why have a Methodology  Don’t want to learn things that aren’t true May not represent any underlying reality ○ Spurious correlation.
L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK.
G. Alonso, D. Kossmann Systems Group
FastPlace: Efficient Analytical Placement using Cell Shifting, Iterative Local Refinement and a Hybrid Net Model FastPlace: Efficient Analytical Placement.
Variability-Driven Formulation for Simultaneous Gate Sizing and Post-Silicon Tunability Allocation Vishal Khandelwal and Ankur Srivastava Department of.
Project 4 U-Pick – A Project of Your Own Design Proposal Due: April 14 th (earlier ok) Project Due: April 25 th.
Copyright © hutchinson associates 2005 The Knowledge is in the Network Patti Anklam June Holley Valdis Krebs Using Network Analysis to Understand and Improve.
SE 450 Software Processes & Product Metrics Reliability: An Introduction.
4/26/05Han: ELEC72501 Department of Electrical and Computer Engineering Auburn University, AL K.Han Development of Parallel Distributed Computing System.
On the Constancy of Internet Path Properties Yin Zhang, Nick Duffield AT&T Labs Vern Paxson, Scott Shenker ACIRI Internet Measurement Workshop 2001 Presented.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering On-line Alert Systems for Production Plants A Conflict Based Approach.
Statistical Methods in Computer Science Why? Ido Dagan.
Can Recursive Bisection Alone Produce Routable Placements? Andrew E. Caldwell Andrew B. Kahng Igor L. Markov Supported by Cadence.
Analysis of Variance Chapter 3Design & Analysis of Experiments 7E 2009 Montgomery 1.
Modelling Workshop - Some Relevant Questions Prof. David Jones University College London Where are we now? Where are we going? Where should.
Analysing the link structures of the Web sites of national university systems Mike Thelwall Statistical Cybermetrics Research Group University of Wolverhampton,
On Comparing Classifiers: Pitfalls to Avoid and Recommended Approach Published by Steven L. Salzberg Presented by Prakash Tilwani MACS 598 April 25 th.
Advanced EDA Benchmark Program: Status Report Supported by IEEE Circuits and Systems Society Managed by Semiconductor Research Corporation April 19, 1999.
1 CSI5388 Data Sets: Running Proper Comparative Studies with Large Data Repositories [Based on Salzberg, S.L., 1997 “On Comparing Classifiers: Pitfalls.
IE 594 : Research Methodology – Discrete Event Simulation David S. Kim Spring 2009.
Jorge Ortiz.  Metadata verification  Scalable anomaly detection.
Hall D Online Data Acquisition CEBAF provides us with a tremendous scientific opportunity for understanding one of the fundamental forces of nature. 75.
Incident Response Mechanism for Chemical Facilities By Stephen Fortier and Greg Shaw George Washington University, Institute for Crisis, Disaster and Risk.
Crowdsourcing Predictors of Behavioral Outcomes. Abstract Generating models from large data sets—and deter¬mining which subsets of data to mine—is becoming.
1 Validation & Verification Chapter VALIDATION & VERIFICATION Very Difficult Very Important Conceptually distinct, but performed simultaneously.
© 2003, Carla Ellis Experimentation in Computer Systems Research Why: “It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you.
Performance Concepts Mark A. Magumba. Introduction Research done on 1058 correspondents in 2006 found that 75% OF them would not return to a website that.
Designing a Random Assignment Social Experiment In the U.K.; The Employment Retention and Advancement Demonstration (ERA)
Jason Houle Vice President, Travel Operations Lixto Travel Price Intelligence 2.0.
1 Wenguang WangRichard B. Bunt Department of Computer Science University of Saskatchewan November 14, 2000 Simulating DB2 Buffer Pool Management.
1 Modeling Needs and Considerations for Energy Efficiency Ex Ante and Ex Post Savings Estimates Workshop: Energy Modeling Tools and their Applications.
OSG Area Coordinator’s Report: Workload Management April 20 th, 2011 Maxim Potekhin BNL
Statistics (cont.) Psych 231: Research Methods in Psychology.
Inferential Statistics A Closer Look. Analyze Phase2 Nature of Inference in·fer·ence (n.) “The act or process of deriving logical conclusions from premises.
1 Compacting Test Vector Sets via Strategic Use of Implications Kundan Nepal Electrical Engineering Bucknell University Lewisburg, PA Nuno Alves, Jennifer.
1 CMSC 671 Fall 2001 Class #25-26 – Tuesday, November 27 / Thursday, November 29.
Validating an Access Cost Model for Wide Area Applications Louiqa Raschid University of Maryland CoopIS 2001 Co-authors V. Zadorozhny, T. Zhan and L. Bright.
Bilinear Logistic Regression for Factored Diagnosis Problems Sumit Basu 1, John Dunagan 1,2, Kevin Duh 1,3, and Kiran-Kumar Munuswamy-Reddy 1,4 1 Microsoft.
HDM-4 Calibration Henry Kerali Lead Transport Specialist The World Bank.
Chapter 8: Simple Linear Regression Yang Zhenlin.
DFT Applications Technology to calculate observables Global properties Spectroscopy DFT Solvers Functional form Functional optimization Estimation of theoretical.
ECE1770 Course Project: Regulatory Monitoring Patricia Hon, Master of Engineering Student Department of Electrical and Computer Engineering University.
Properties Incompleteness Evaluation by Functional Verification IEEE TRANSACTIONS ON COMPUTERS, VOL. 56, NO. 4, APRIL
LIGO-G Z The Q Pipeline search for gravitational-wave bursts with LIGO Shourov K. Chatterji for the LIGO Scientific Collaboration APS Meeting.
CSE SW Metrics and Quality Engineering Copyright © , Dennis J. Frailey, All Rights Reserved CSE8314M13 8/20/2001Slide 1 SMU CSE 8314 /
Copyright , Dennis J. Frailey CSE Software Measurement and Quality Engineering CSE8314 M00 - Version 7.09 SMU CSE 8314 Software Measurement.
Twitter Community Discovery & Analysis Using Topologies
Experimental Psychology PSY 433 Chapter 5 Research Reports.
Assess usability of a Web site’s information architecture: Approximate people’s information-seeking behavior (Monte Carlo simulation) Output quantitative.
Statistics (cont.) Psych 231: Research Methods in Psychology.
Marco Vieira University of Coimbra Naples, 20th December 2011.
Traffic Simulation L3b – Steps in designing a model Ing. Ondřej Přibyl, Ph.D.
ItemBased Collaborative Filtering Recommendation Algorithms 1.
Applying Combinatorial Testing to Data Mining Algorithms
Closing Remarks and Action Items
The Q Pipeline search for gravitational-wave bursts with LIGO
Intrusion Tolerant Systems Workshop: Anomaly Detection Group
Software Quality Engineering
Teaching Functional Verification
Navigation In Dynamic Environment
Lithography Diagnostics Based on Empirical Modeling
Calibration and Validation
Systems Biology Strikes Gold
Realizing Closed-loop, Online Tuning and Control for Configurable-Cache Embedded Systems: Progress and Challenges Islam S. Badreldin*, Ann Gordon-Ross*,
Two Halves to Statistics
Understanding your machine capability
Presentation transcript:

Tool Benchmarking Where are we? Justin E. Harlow III Semiconductor Research Corporation April 9, 2001

Metrics and Benchmarks: A Proposed Taxonomy Methodology Benchmarking  Assessment of productivity  Prediction of design time  Monitoring of throughput Flow Calibration and Tuning  Monitor active tool and flow performance  Correlate performance with adjustable parameters  Estimate settings for future runs L Tool Benchmarking  Measure tool performance against a standard  Compare performance of tools against each other  Measure progress in algorithm development

How It’s Typically Done... My Tool Your Tool The Job

ICCAD 2000: Typical Results

Predictive Value? Kind of…. It takes more time to detect more faults But sometimes it doesn’t...

Bigger Benchmarks Take Longer Sometimes... S526: 451 detects, 1740 sec S641: 404 detects, 2 sec

What’s Wrong with the way we do it today? Results are not predictive Results are often not repeatable Benchmark sets have unknown properties Comparisons are inconclusive

A Better Way? Design of Experiments Critical properties of equivalence class:  “sufficient” uniformity  “sufficient” size to allow for t-test or similar

Example: Tool Comparison Scalable circuits with known complexity properties Observed differences are statistically significant

Canonical Reference on DoE Tool Benchmark Methodology D. Ghosh. Generation of Tightly Controlled Equivalence Classes for Experimental Design of Heuristics for Graph-Based NP- hard Problems. PhD thesis, Electrical and Computer Engineering, North Carolina State University, Raleigh, N.C., May Also available at 0-Thesis-PhD-Ghosh.

Tool Benchmark Sets ISCAS 85, 89, MCNC workshops etc. ISPD98 Circuit Partitioning Benchmarks ITC Benchmarks Texas Formal Verification Benchmarks NCSU Collaborative Benchmarking Lab

“Large Design Examples” CMU DSP Vertical Benchmark project. The Manchester STEED Project The Hamburg VHDL Archive Wolfgang Mueller's VHDL collection Sun Microsystems Community Source program OpenCores.org Free Model Foundry ….

Summary There are a lot of different activities that we loosely call “benchmarking” At the tool level, we don’t do a very good job Better methods are emerging, but  Good Experimental Design is a LOT of work  You have to deeply understand the properties that are important and design the experimental data Most of the design examples out there are not of much use for tool benchmarking

To Find Out More... Advanced Benchmark Web Site Nope… There’s no “a” in there Talk to Steve “8.3” Grout