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Overview of Purdue Computer Engineering
Last Update: March 2017
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Computer Engineering (CE) at Purdue
23 faculty members (core), 10 more faculty members (associated) 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 (Engineering), #8 (EE), #9 (CmpE) (US News and World Report, 2016) 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 MS Universities (professors) US* 8 Oversea 4 Research Labs 1 Startup 4(2 as founders) Large Companies 30 13 * One is a department head.
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CE Positions Only a representative sample Universities Research Labs
Companies 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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Degree Options Lots of flexibility in degree options, course selection
Ph.D (after Masters): 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:
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Research Areas: Overview
Compilers: Eigenmann, Midkiff, Kulkarni, Siskind Distributed Syst./Networking/Operating Syst: Bagchi, Hu, Rao, Lin, Zhang Dependability/Security: Ghafoor, Bagchi, Lin, Zhang Computer Architecture: Vijaykumar, Thottethodi, Rogers, Zhang 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, Geo-distribution 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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Internet Systems Lab (Prof. Sanjay Rao)
an ACL Local Data Center Cloud Less sensitive data frontend Internet (sensitive databases) Internet Systems Lab (Prof. Sanjay Rao) Geo-distributed data storage Balancing consistency, availability, performance Hybrid Cloud Architectures 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:
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Purdue University Visual Analytics Center (PURVAC) – Prof. 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
翻 译 ترجمة machine + human translation idea generation computer vision natural language processing Products supported by human-powered systems: Google Maps Apple Maps Amazon Facebook Google Translate Bing USPS Twitter
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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 (2016), … Collaborating ECE faculty: Eigenmann, Kulkarni, Lin, Quinn Build end-to-end dependable platforms addressing node level issues + network once the message is on the wire. We are looking at the gamut of dependability techniques – Our focus is on application level and system level software Animation shows a simulation we have built as part of an ongoing Department of Defense project for missile defense. We are simulating the distributed command and control system for missile defense and how to make it resilient to failures (links being jammed, nodes failing) or cybersecurity attacks. The red lines denotes missiles being launched, the circular regions are command and control or ground stations, and the white lines are the interceptors to bring down the missiles.
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Dependable Computing Systems Research (DCSL) Prof. Saurabh Bagchi
Distributed IDS, based on Bayesian Network – protecting Northrop Grumman’s networks Framework for distributed intrusion tolerant system How to build an adaptive infrastructure for diagnosing and recovering from failures in a distributed platform? Application: Web services, DoD missile defense system A sample distributed application like those at corporate entities. We deploy a plethora of detectors and provide the smart to piece together information received from each of the detectors to give high level actionable knowledge to the sys admins. We also deploy automated response techniques, say to prevent an insider from exfiltrating information. Northrop Grumman is using it to protect their clients’ networks.
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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) Black box diagnosis: rather than cursing when your call gets dropped or data connection gets slow, you can work on solving it – predict when a failure is going to happen based on a machine learning classifier, and then take mitigation action such as, connecting to a different cell tower. Dependable embedded wireless networks: When it is deployed in hard to reach places like a battlefield, how do you tell if someone has tampered with the devices. Our technology has been commercialized through a startup company, which is selling to Department of Defense.
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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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Prof. Felix Xiaozhu Lin’s Research
System Software for Mobile/Wearable Computers Buttery smooth, “jank-free” interactions Energy efficient, cooler devices Friendly, foolproof app programming paradigms System Software for Data Processing in IoT Unleashing the power of novel accelerators Combating the memory wall Securing the use of accelerators
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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 Oak Ridge NL, 560,000 cores, 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 22
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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?
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Selected Compiler Projects (Prof. Sam Midkiff)
URL: 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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(Prof. Rudi Eigenmann, Prof. Sam Midkiff)
Auto-tuning Compilers: Turning computer applications into life-long learners (Prof. Rudi Eigenmann, Prof. Sam Midkiff) Today: Goals: Programs run “optimally” Programs improve with age Software evolves Write a program Compile it Run/use it Tomorrow: Challenges: Navigate a huge space of optimization options Dynamically plug-in new code Ensure the evolving program improves Write a program Compile, try new options automatically Run/use program as it evolves
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Prof. Xiaokang Qiu’s Research
Computer-Aided Programming Research mission: Make software development easier, more reliable, and more productive. Program Verification How to verify a program? How to verify a large program? How to verify an intricate program? How to locate a bug? Program Synthesis How to automatically optimize a program? How to automatically update my app for the latest version of Android? How to automatically repair a buggy program? Automatic grading for MOOC programming courses? no yes Program Correct?
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HELPS Lab (Prof. Yung-Hsiang Lu)
High-Efficiency, Low-Power Systems 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 Mb/s b/s Mb/s b/s storage data sources distributed computers
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Embedded Systems Lab (ESL) – Vijay Raghunathan
Webpage: 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
Heliomote solar-powered sensor node Ultra-low voltage energy harvesting IC Super capacitor based high-efficiency energy storage system AVEKSHA system for non-intrusive debugging of embedded systems RF triggered wakeup 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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Overview of Research in Assistive Robotics Technology Lab (ARTLab)
Prof. C. S. George Lee Understanding: Perception & Cognition Mobility: Locomotion & Control Collaboration & Interaction: HRI Mobile Robots Building Better Future with Robotics & Machine Learning!
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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
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On-Chip & Off-chip Interconnection Networks (Prof. Mithuna Thottethodi)
Node Node Node Node R CPUs Mem R Router Computation Issues: Architecture support for management of large scale systems Memory hierarchy, interaction with application Communication Issues: Congestion control for communication intensive applications Routing, switching, & arbitration with worst-case guarantees Other Projects: - Storage area networks - Architectures for biochemical analysis
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(AKA Wukong Lab) – Building the Next-Generation Datacenters
Faculty: Yiying Zhang Fields Operating Systems Distributed Systems Datacenter Networking Computer Architecture System Security Big Data “I see myself as a generalist -- I am attracted to the biggest problem I can find, regardless of area” (currently in systems) Lab spirit: Fun, Real, Impact
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Selected Projects We are building a new OS, from scratch
For next-generation datacenter hardware architecture and modern big data applications with hardware assist New hardware model that is not monolithic computer and new programming model We are building a new datacenter network system System software stack Network topology, routing, and congestion control We are defining a new memory layer Distributed, shared, persistent Many other exciting projects Security, big data
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Programmable Accelerator Architectures Prof. Tim Rogers
Single Core OoO Superscalar CPU Better (how to get here?) Brawny (OoO) Multicore Wimpy (In-order) Multicore Ease of Programming 16K thread, SIMT Accelerator ASIC Hardware Efficiency
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Programmable Accelerator Architectures Prof. Tim Rogers
Current Focus: GPUs Exploring novel architecture and software to….. Ease of Programming 16K thread, SIMT Accelerator Hardware Efficiency
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Architecture and Embedded Systems
Recap: Research Areas Software Systems Architecture and Embedded Systems AI/Vision/Robotics Compilers: Eigenmann, Midkiff, Kulkarni, Siskind, Qiu Distributed Syst./Networking/Operating Syst: Bagchi, Hu, Rao, Lin, Zhang Dependability/Security: Ghafoor, Bagchi, Lin, Zhang, Qiu Computer Architecture: Vijaykumar, Thottethodi, Zhang 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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