Introduction Computer Science Henri Bal Vrije Universiteit Amsterdam

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

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

Topics (Tuesday lectures) ● Intro & high-performance computing (Henri Bal) ● Finding & reading scientific literature (Michel Klein, with LI & IMM students) ● e-Science infrastructures (Cees de Laat) ● e-Health (Aart van Halteren) ● Astronomy & manycores (Rob van Nieuwpoort) ● Watson (Lora Aroyo, with LI & IMM students) ● Luggage handling at Heathrow Terminal 5 (Huub van der Wouden, with IMM students)

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)

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 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

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

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 ….

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

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

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

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!)

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)

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)

High Performance Computers (3) ● Cluster computers ● Parallel machines built from off-the-shelf (commodity) PCs and networks ● Excellent price/performance ratio ● Exponential performance growth of processor speeds ● See for 500 fastest supercomputershttp://

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 ● GPUs (manycores): 1000’s very simple cores ● Intel Phi (2012): 60 Pentium-1’s on 1 chip, with advanced vector support ● Challenge: how to program these things?

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)

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

Networks ● Wide area networks (WANs) ● Local area networks (LANs) ● Mobile networks ● Much more in Computer Networks class

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

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

An aside ● In Computer Science ● k(ilo)=1024 ● m(ega)= ● g(iga)= ● t(era)= ● p(eta)= ● e(xa)= ● All has to do with binary numbers

DAS-4 Dual quad-core Xeon E GB memory 1-10 TB disk Infiniband + 1Gb/s Ethernet Various accelerators (GPUs, multicores, ….) Scientific Linux Built by ClusterVision VU (74) TU Delft (32)Leiden (16) UvA/MultimediaN (16/36) SURFnet6 10 Gb/s light paths ASTRON (23)

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’’

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

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

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)

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 ● Generating movies

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)

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

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

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

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

Literature (Context) ● Ami Marowka: Back to Thin-Core Massively Parallel Processors, IEEE Computer, December 2011, pp

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 ● 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?