Introduction Computer Science Henri Bal Vrije Universiteit Amsterdam

1. Introduction Computer ScienceHenri BalVrije Universiteit Amsterdam

2. Goals of this course • Understand typical Computer Science topics • Meet with students and some staff members • Develop skills: • Reading (English) scientific literature • Critical/analytical thinking about CS topics • Discussing • Presenting • Scientific writing

3. Structure • Tuesdays: guest lectures • 2 scientific papers provided as context • Questions made up by lecturers beforehand • Thursday/Friday/Monday: working groups • 2 students per group present a paper • Each group discusses both papers + questions

4. Topics (Tuesday lectures) • Intro & high-performance computing(Henri Bal) • Luggage handling at Heathrow Terminal 5 (Huub van der Wouden, with IMM students) • Finding & reading scientific literature (Michel Klein, with LI & IMM students) • Bioinformatics (Jaap Heringa) • Watson (Chris Welty, with LI & IMM students) • e-Science infrastructures (Cees de Laat) • e-Health (Aart van Halteren)

5. Working Groups • Supervised by staff members (instructors) • First meeting: • Instructors will present 1 paper, you do the discussions • Other meetings: • Students present/discuss papers • Course material + working group composition will be made available on Blackboard (bb.vu.nl)

6. Your tasks • Attend Tuesday lectures • Send brief answers to questions + pose 2 new questions per paper before workgroup deadline • Give 1 presentation in a working group • Make slides, talk for 10-15 minutes • Participate in working group discussions • Write 2-page paper on 1 topic of your choice • Use (find!) 2 extra publications in the literature • Grading: • 40% participation, 40% paper, 20% presentation

7. First presentation • My personal view on Computer Science • Why is Computer Science so interesting? • Biased towards my own research area: • High performance distributed computing

8. Computer Science (CS) • CS sits between technology and applications, both of which have turbulent developments • Processors, networks, mobiles, wearables, … • Data explosion in virtually all applications • CS also studies many fundamental problems of its own • Programming languages, security, AI, theory ….

9. Outline • Technology • Computers • Some history • High performance computers • Modern (multicore) PCs • Networks & mobile computing • Applications • Data explosion • Computation demands • Fundamental CS questions

10. Computers • Mainframe: powerful centralized computer • IBM 704 (1964) • Minicomputers: <25K$, for small groups • PDP-8, PDP-11, VAX (1960s-1980s) • Workstations: expensive personalgraphical machine • Xerox Alto (1973) • PCs: inexpensive machine for the masses • IBM PC (1981)

11. High Performance Computers • Computer systems with many processors, all computing in parallel • Paper: “Back to Thin-Core Massively Parallel Processors”

12. Warning • Scientific papers may be overwhelming • Have to learn how to read scientific literature, without understanding every word • ‘’Moreover, smart algorithms that exploit data locality, perform loop unrolling, eliminate iterative loops and recursive algorithms, and use idle-power-friendly programming languages and libraries as well as auto-tuning based on multiversion algorithms can achieve higher-energy-efficiency applications.’’ • (You’re not supposed to understand this yet!)

13. High Performance Computers (1) • Vector machines • Can do vector operations in parallel • A and B: 1-dimensional matrices with 100 elements • Computing A+B (= 100 computations) takes as much time as doing 1 addition on a sequential computer • History • 1970s, 1980s (e.g., Cray) • 2000s (Japanese Earth Simulator) • 2010s (GPUs, Graphical Processing Units)

14. High Performance Computers (2) • Massively parallel machines • 1000s of special processors connected by a special network, all running in parallel, each doing part of the overall computations • E.g., CM-1, CM-5, Intel Paragon, IBM BlueGene • Connection network uses graph theory (math)

15. High Performance Computers (3) • Cluster computers • Parallel machines built from off-the-shelf (commodity) PCs and networks • Excellent price/performance ratio • Exponential performance growth ofprocessor speeds • See http://www.top500.orgfor 500 fastest supercomputers

16. Multicores & Manycores • All PCs now have >1 compute cores • Every PC is a parallel computer! • Some PCs already have 48 cores • Core count will increase to hundreds • Intel Phi (2012): 60 Pentium-1’s on 1 chip, with advanced vector support • Challenge: how to program these things?

17. Thinking in parallel is hard • How to split up the work? • Load balancing • All cores should do the same amount of work • Communication & synchronization • Cores must exchange data (=overhead) • Nondeterminism: • A single processor always gives same outcome • With >1 core the outcome may depend on the order (called a ``race condition’’ bug)

18. Graphics Processing Units (GPUs)

19. CPU: minimize latency of 1 activity (thread) Must be good at everything Big on-chip caches Sophisticated control logic GPU: maximize throughput of all threads usinglarge-scale parallelism 1000’s very simple cores Differences CPUs and GPUs Control ALU ALU ALU ALU Cache

20. Current debates • Should we build chips with: • Very fast/complicated (superscalar) processors? • Hits a ‘’power wall’’, hard to increase clock frequency • Many slower/simpler (thin) processors? • Hard to program • How to deal with energy consumption? • Performance per Watt becomes key factor

21. Networks • Wide area networks (WANs) • Local area networks (LANs) • Mobile networks • Much more inComputer Networks class

22. Wide area networks • ARPANET • First computer network, connecting some US sites (1960s) • Speeds measured in kbit/s • Internet • Based on standardized (IP) protocol suite • Connect everyone/everything (Internet-of-things) • Dedicated optical networks (light paths) • 10 gbit/s, point-to-point

23. Local Area Networks • Ethernet: developed by Xerox PARC (1974) • Speed increased from 10 mbit/s to 100 gbit/s • Cluster computers use Ethernet or faster commodity networks • Myrinet • Infiniband

24. An aside • In Computer Science • k(ilo)=1024 • m(ega)=10242 • g(iga)=10243 • t(era)=10244 • p(eta)=10245 • e(xa)=10246 • All has to do withbinary numbers

25. DAS-5 UvA/MultimediaN (18/31) Dual 8-core Intel E5-2630v3 CPUs FDR InfiniBand OpenFlow switches Various accelerators CentOS Linux Bright Cluster Manager Built by ClusterVision VU (68) SURFnet7 ASTRON (9) 10 Gb/s TU Delft (48) Leiden (24)

26. Mobile computing • Laptops, sensors, smartphones, tablets • Many forms of mobile networks • Wifi (local range) • 3G, 4G (lower bandwidth, high coverage) • BlueTooth (for pairing devices) • Ultimately: ubiquitous computing? • Vision by Mark Weiser (1988) • ‘’machines that fit the human environment instead of forcing humans to enter theirs’’

27. Outline • Technology • Computers • Some history • High performance computers • Modern (multicore) PCs • Networks & mobile computing • Applications • Data explosion • Computation demands • Fundamental CS questions

28. Application developments • There is a ``data explosion’’ in many application areas • Huge amounts of data (up to Petabytes/year) • Very complicated/heterogeneous data • Demand for computing • Model (simulate) designs on a computer

29. Data explosion • Society: • Web, social networks • Industry, economy: • Banks, stock markets • Science • LHC (``Higgs particle’’) • Data stored on world-wide ``grid’’ • Bioinformatics (next generation sequencing) • Astronomy: software telescopes (LOFAR, SKA)

30. Computing demands • Computational science: • Modeling ozone layer, climate, ocean, human brain • Simulating galaxies • Engineering: • Aircraft modeling, designing F1 cars(Virgin VR01) • TVs (mostly software), embedded systems • Games and multimedia: • Computer chess (Deep Blue) • Watson (Jeopardy) • Analyzing multimedia content • Digital forensics • Generating movies

31. Pixar’s ``Up’’ (2009) Whole movie (96 minutes) would take 94 years on 1 PC (4 frames per day; 1 second takes 6 days; 1 minute per year)

32. Some fundamental Computer Science topics (1) • Operating systems: • Windows, Linux, Minix (Andy Tanenbaum) • Programming languages and systems • Fortran, Cobol, C, Java, Python … (thousands) What happens if you ask a computer scientist to solve a problem? He/she will come back 3 months later, with … a new programming language ideally suited for solving your problem

33. Some fundamental Computer Science topics (2) • Security • Preventing/detecting attacks, privacy, etc • (Semantic) web technology • Finding and reasoning about content on the web • Cloud computing • Store data and programs remotely, in the Cloud

34. Some fundamental Computer Science topics (3) • Artificial intelligence • E.g. automatic machine-learning • Databases • Storing and searching huge amounts of data • Logic, modelling, graph theory, complexity • Essential for many applications

35. Conclusion • Modern Computer Science deals with hectic developments in technology and applications • Both provide us many research problems • Application-driven vs technology-driven research • There also are many fundamental CS problems

36. Literature (Context) • Ami Marowka: Back to Thin-Core Massively Parallel Processors, IEEE Computer, December 2011, pp. 49-54

37. QUESTIONS • Explain what ``thin cores’’ are • What are the arguments in favor and against using ‘’thin cores’’ ? • Which role does energy consumption play in this discussion? • Compute the energy efficiency of the current 10 largest supercomputers on www.top500.org • Which type of machine currently is most energy efficient? • Compare the maximum performance of the current #1 against the performance of the #1 of 10 years ago. What is the difference?