Overview of Purdue
Computer Engineering
Last Update: March 2015
ECE Open House, 2015
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Computer Engineering (CE) at Purdue
• 21 faculty members (~25% of the School of ECE)
• Key Areas:
– Software Systems
• Compilers and High Performance Computing
• Distributed Systems, Networking and Operating Systems
• Dependability and Security
– Computer Architecture and Embedded Systems
• Microarchitectures
• Interconnection networks
• Low power design
– AI, Robotics, and Visualization
• Artificial Intelligence and Machine Learning
• Graphics and Visualization
• Robotics, Vision and Haptics
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Why Purdue Computer Engineering?
• Ranked #9 (US News and World Report, 2014)
– Consistently ranked in the top 10
– Computer Engineering gets a separate ranking from EE
• Strong software focus is unique…
– Many faculty with Ph.Ds from Computer Science Departments
– Areas like OS, Networking, Compilers, Visualization, AI not common in
Computer Engineering Departments
• Strong reputation in hardware
– Architecture, VLSI, Embedded Systems
• Collaborations with sister areas/departments/centers
– Communication & Signal Processing, VLSI, etc.
– Computer Science Department
– CERIAS (Security center), CRI (High Performance Computing)
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Where do CE alumni go?
(in recent years)
PhD
Universities
(professors)
US*
Oversea
MS
8
4
8
Research Labs
Startup
4(2 as founders)
Large Companies
30
* One is a department head.
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13
4
CE Positions
• Only a representative sample
Universities
Research Labs
UNC Charlotte
Microsoft Research
Facebook
Mathworks
U Pittsburgh
PARC
Intel (12)
HP
Oregon State
Sandia
Google (5)
Samsung
U Missouri
MIT Lincoln Labs
IBM (3)
Cisco
U Toronto
AT&T Labs
Microsoft (4) Simulex
Arizona State
Lawrence Livermore
Qualcomm
Cary
MIT
MITRE
VMWare
Schlumberger
Stanford
Adobe
DE Shaw
Bangladesh U
Amazon
Accenture
Korea U
Saavn
Arctic Sand
Cal Poly
JP Chase
SensorHound
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Companies
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Degree Options
•
•
•
•
Lots of flexibility in degree options, course selection
Ph.D (after Masters):
4 courses
Direct Ph.D (after Bachelors): 12 courses
Masters:
– Non-Thesis (10 courses)
– Thesis Option (6 courses)
• Some highlights about our Masters:
– Courses same as Ph.D level courses
– Easy to switch to Ph.D (with support from committee members)
– Thesis option allows large-scale system building, exposure to research etc.
• Details: See Ph.D Handbook:
– https://engineering.purdue.edu/ECE/Academics/Graduates/PHD/DoctoralHa
ndbook.pdf
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Research Areas: Overview
• Compilers: Eigenmann, Midkiff, Kulkarni, Siskind
• Distributed Syst./Networking/Operating Syst: Bagchi, Hu, Rao, Lin
• Dependability/Security: Ghafoor, Bagchi, Lin
• Computer Architecture: Vijaykumar, Thottethodi
• Embedded Systems: Raghunathan, Lu
• Artificial Intelligence/Machine Learning: Givan, Siskind, Kak
• Human Computer Interaction: Ebert, Quinn
• Robotics/Vision: Kak, Lee, Park, Siskind
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Internet Systems Lab (Sanjay Rao)
Current Research Focus: Cloud Computing
 Many Opportunities
 Elasticity, Cost-savings, Geodistribution
 Big Challenges:
 Data Privacy Concerns
 Performance is variable:
 Amazon: every 100ms
latency cost 1% in sales
 Google: 0.5 sec’s delay
 revenue drop by 20%
 Data-center failures and
outages
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ECE Open House, 2015
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ISL (Sanjay Rao)
Hybrid Cloud Architectures
Less
sensitive
data
an ACL
Geo-distributed data storage
Balancing consistency, availability, performance
Cloud
frontend
Internet
(sensitive
databases)
Local Data Center
• How to architect interactive geo-distributed cloud applications?
Adapt to performance variability, high availability
• Mobile Applications and cloud:
• When does cloud make sense for mobile applications?
• Consider both energy efficiency (3G/LTE), and performance
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ISL (Sanjay Rao)
• Other Research Areas:
– Enterprise Network Management
– Software Defined Networks
– Internet Video Delivery (e.g., Youtube)
• Collaborations with AT&T, Microsoft, Google
• Research prototypes deployed in AT&T, Purdue
• More Information:
– URL: http://www.ece.purdue.edu/~isl/
– sanjay@purdue.edu
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Dependable Computing Systems Research (DCSL)
Prof. Saurabh Bagchi
• URL: engineering.purdue.edu/~dcsl
• We need computer systems that we can depend on in the face of
– Naturally occurring faults – hardware malfunction, software bugs
– Malicious intrusions – insider attack or external adversaries
• To build these we need students who are not afraid to make and break
real systems
– Many project ideas and requirements come from industrial sponsors and partners
• Why our students love it?
–
–
–
–
Internships at top industrial places
Multiple job offers from top places
No super-long PhD timelines
Work with industry: Google Faculty Award (2015), …
• Collaborating ECE faculty: Eigenmann, Kulkarni, Lin, Quinn
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Research Projects in DCSL
• • Distributed
IDS,for
based
Framework
distributed intrusion tolerant system
on Bayesian Network – protecting
– How to build an adaptive infrastructure for diagnosing and recovering
Northrop Grumman’s networks
from failures in a distributed platform?
– Application: Web services, DoD missile defense system
D1
D3
D2
D4
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Research Projects in DCSL
• Black-box diagnosis
– How to diagnose source of errors in large scale distributed computing
platforms?
– Application: Distributed web services, Mobile apps (with AT&T),
Supercomputing clusters (with LLNL)
• Dependable Cyber Physical Systems
– How to build dependable system under resource constraints? Under
strict timing constraints?
– Application: Smart power grid monitoring (startup company:
SensorHound Innovations), infrastructureless wireless networks, say in
battlefields or in disaster relief (with USC/ISI)
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Distributed Systems and Networking Lab
(Prof. Y. Charlie Hu) (1/2)
• Smartphone Energy Profiling
– Characterizing and Modeling the Impact of Wireless Signal Strength on
Smartphone Battery Drain (Sigmetrics 2013)
– Where is the energy spent inside my app? Fine Grained Energy Accounting
on Smartphones with Eprof (EuroSys 2012, Best Student Paper Award)
– Fine-Grained Power Modeling for Smartphones Using System Call Tracing
(EuroSys 2011)
• Smartphone Energy Debugging (with Prof. Midkiff)
– Hypnos: Understanding and Treating Sleep Conflicts in Smartphones
(Eurosys 2013)
– What is keeping my phone awake? Characterizing and Detecting No-Sleep
Energy Bugs in Smartphone Apps (MobiSys 2012)
– Bootstrapping Energy Debugging on Smartphones: A First Look at Energy
Bugs in Mobile Devices (HotNets 2011)
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Distributed Systems and Networking Lab
(Prof. Y. Charlie Hu) (2/2)
• Data Center Networking
– Duet: Cloud Scale Load Balancing with Hardware and Software
(SIGCOMM 2014)
– The Only Constant is Change: Incorporating Time Varying Network
Reservations in Data Centers (SIGCOMM 2012)
– The TCP Outcast Problem: Exposing Unfairness in Data Center Networks
(NSDI 2012)
– Latency Inflation with MPLS-based Traffic Engineering (IMC 2011)
– Optimizing Cost and Performance in Online Service Provider Networks
(NSDI 2010)
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What is High-Performance Computing (HPC)?
• Systems and application technology that creates and uses the
highest compute power
– Technology that pushes this envelope is clearly HPC
• Today’s performance level: ~100TFlops peak
• (Cray XK7 @ Oak Ridge NL, 560,000 cores, 17590 TFlops peak)
• Classical application areas: climate modeling, computational fluid
dynamics, molecular dynamics, structural analysis, ….
• Hardware and software systems that enable this technology
• The Computing Research Institute is Purdue’s interdisciplinary
Center for High-Performance Computing
• Several CE faculty participate in CRI: Midkiff, Eigenmann,
Ghafoor, Vijaykumar, Hu, Bagchi, Thottethodi, Raghunathan
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Cyberinfrastructures
You can get involved in the largest project in the history of Purdue
NEES: Network for Earthquake Engineering Simulation
nees.org
Research topics:
• Search, analyze, visualize, manipulate large data
• Cloud computing services
• Security
• Collaboration, virtual organizations
• Big Data capture, use, storage, management
Profs Rudi Eigenmann Saurabh Bagchi
Collaborating with Civil Engineering, Mechanical Engineering, Engineering Education and ~20 other Universities
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HELPS Lab (Prof. Yung-Hsiang Lu)
• High-Efficiency, Low-Power Systems
• http://engineering.purdue.edu/helps
• Supercomputer on Your Hand: Combine convenience of mobile
and the resources in cloud servers
• Run complex programs, such as recognition, high-performance
graphics, strategic games… on phone while performing heavy
computing on server (cloud)
• Problems
–
–
–
–
Which parts of programs should run on mobile or cloud?
How to handle intermittent connections?
How many servers should be used?
How to retrieve data from multiple sites (such as
Youtube + Facebook + Picasa)?
– How to protect privacy and data integrity?
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HELPS Lab: Prof. Yung-Hsiang Lu
•
Integration of mobile robots and cloud computing
– Robot mobiles' batteries can last only minutes (for demos).
– Computing takes too long and wastes energy.
– Robots: sensing and control. Cloud: computing.
• Big Data.
– Thousands of cameras streaming video continuously.
– Most videos are not stored or analyzed.
– Analyzing these videos require distributed computing.
MB
b
b/s
Mb/s
Mb/s
data sources
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b/s
storage
distributed computers
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Embedded Systems Lab (ESL) – Vijay
Raghunathan
• Webpage: https://engineering.purdue.edu/ESL
• Embedded Systems: “Computers that are part of larger
systems that you don’t normally think of as computers”
• 98% of all CPUs sold go into embedded systems
• Often very resource constrained, distributed, wireless
71 embedded CPUs
2 million lines of code
BMW 745i
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ESL: HW and SW Systems Built By Us
Power
Converter
On-chip
MOS Caps
MPP
Tracking Unit
I/O Pads
Heliomote solarpowered sensor node
AVEKSHA system for nonintrusive debugging of
embedded systems
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Ultra-low voltage
energy harvesting IC
RF triggered
wakeup system
Super capacitor based highefficiency energy storage system
SPI-SNOOPER reliable
wireless sensor node
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Embedded Systems Lab (ESL)
• Hardware and Software Architectures for Wireless
Embedded Systems
– New low-power hardware architectures
– Environmental energy harvesting
– Embedded systems that enable green computing
• Wireless Sensor Networks
– New programming paradigms
– New techniques for reliable operation
• System-on-Chip Design Methodologies
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Parallelism, Languages and Compilers Lab
(Prof. Milind Kulkarni)
• How do we make it easier for “Joe programmer” to write correct,
efficient parallel and distributed programs?
– Give him an intuitive programming model
• Sequential, or close to it
• Simple abstractions for data structures, algorithms
• Develop languages, compilers and run-time techniques to support
intuitive programming models
– New languages and language features to support simple parallel
programming models
– Compiler techniques to optimize program
• Reduce communication latency, improve locality
– Run-time techniques to optimize execution
• Build efficient run-time systems
• Automatically tune run-time parameters
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PLCL Projects
• Compilers and runtime systems for optimizing irregular
applications
– New transformations and analyses to improve locality, vectorize programs
– Runtime systems to support distributed execution or using GPUs
– Automatic tuning of run-time behavior
• How do you adapt to the input?
• Optimizing scientific applications
– Take advantage of domain-specific properties for optimization
• Algebraic properties, data usage behaviors
• Optimizing and parallelizing Computational Genomics
applications
– Make it easier to write fast genomics applications (joint work with Prof.
Bagchi)
• What are the key kernels that people use in computational genomics?
ECE Open House, 2015
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Selected Compiler Projects
(Prof. Sam Midkiff)
• URL: www.ece.purdue.edu/~smidkiff/research.htm
• Compilation for more efficient, easier, safer programming
– Novel high level programming language design and optimization
• Aspen (With Prof. Vijay Pai)
– Novel models of how threads interact (memory models) to ease of
programming and high performance
– Optimization of programs with high level user-specified parallelism
– Compiler monitoring of programs to detect and characterize errors
• Compilation for high performance
– Hybrid compile-time/run-time analysis and optimization methods
– Optimization of code for multithreaded processors
• Debugging massively parallel programs (Prof. Hu)
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Auto-tuning Compilers: Turning computer
applications into life-long learners
(Prof. Rudi Eigenmann, Prof. Sam Midkiff)
Today:
Write a program
Compile it
Run/use it
Challenges:
• Navigate a huge space of
optimization options
• Dynamically plug-in new code
• Ensure the evolving program
improves
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Goals:
• Programs run “optimally”
• Programs improve with age
• Software evolves
Tomorrow:
Write a program
Compile, try
new options
automatically
Run/use
program as
it evolves
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Prof. Felix Xiaozhu Lin’s Research
• System Software for Personal Computing
– Buttery smooth, “jank-free” interactions
– Energy efficient, cooler devices
– Friendly, foolproof app programming paradigms
• System Software for Heterogeneous Platforms
– Unleashing the power of novel accelerators
– Combating the memory wall
– Securing the use of accelerators
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Prof. T. N. Vijaykumar’s Research (1/2)
• Power- and Reliability-related
– Mitigating power and heat problems
– Data center power and cooling
• Fault Tolerance
– Architecture support for hard errors & soft errors
– Architectures to tolerate process variations
• Network Hardware
– High-speed router design
– Hardware support for network management
– Hardware support for network security
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Prof. T. N. Vijaykumar’s Research (2/2)
• Multicore architectures
– Chip multiprocessor architecture
– Heterogeneous multicores
– Architecture support to make parallel programming easier
• Quantum Computing
– Quantum architectures
• Microfluidics
– Programmable Lab-on-a-Chip
ECE Open House, 2015
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On-Chip & Off-chip Interconnection Networks
(Prof. Mithuna Thottethodi)
Node
Node
Node
Mem
Router
Node
CPUs
R
R
• Computation Issues:
• Communication Issues:
– Architecture support for
management of large scale systems
– Memory hierarchy, interaction with
application
– Congestion control for
communication intensive
applications
– Routing, switching, & arbitration
with worst-case guarantees
• Other Projects: - Storage area networks
- Architectures for biochemical analysis
ECE Open House, 2015
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Purdue University Visual Analytics Center
(PURVAC) – David Ebert
• Visual analytics
(spatial, temporal, predictive, tools)
• Illustrative visualization
• Perceptually-motivated visualization
• Procedural modeling of natural
phenomena
• Rendering (volume, procedural, …)
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Visual Analytic Center of Excellence (VACCINE)
Public Safety & Health Visual Analytics
Research
• Public safety visual analytics
– Coast Guard Search And Rescue
Visual Analytics
– Social Media Visual Analytics
– Crime Visual Analytics
• Public health visual analytics
– Decision support environment for
epidemic modeling and responses
– Pandemic influenza modeling and
visualization tool
– Cancer Care Engineering
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Human-Powered Systems Lab
Prof. Alex Quinn
We develop systems that bring humans and machines together
to automate processes in ways that neither could accomplish alone.
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Human-Powered Systems Lab
Prof. Alex Quinn
‫ترجمة‬
翻
译
idea
generation
computer
vision
natural language
processing
machine + human
translation
Products supported by human-powered systems:
Google Maps
Apple Maps
Amazon
Facebook
Google Translate
Bing
USPS
Twitter
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Recap: Research Areas
Software
Systems
Archiecture
and
Embedded
Systems
AI/Vision/
Robotics
•
Compilers: Eigenmann, Midkiff, Kulkarni, Siskind
•
•
Distributed Syst./Networking/Operating Syst:
Bagchi, Hu, Rao, Lin
Artificial Intelligence/Machine Learning: Givan,
Siskind, Kak
•
Human Computer Interaction: Ebert, Quinn
•
Dependability/Security: Ghafoor, Bagchi, Lin
•
Robotics/Vision: Kak, Lee, Park, Siskind
•
Computer Architecture: Vijaykumar, Thottethodi
•
Embedded Systems: Raghunathan, Lu
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