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 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

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 personalgraphical 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 ofprocessor speeds

● See http://www.top500.orgfor 500 fastest supercomputers

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)=10242

● g(iga)=10243

● t(era)=10244

● p(eta)=10245

● e(xa)=10246

● All has to do withbinary numbers

DAS-4Dual quad-core Xeon E5620 24-48 GB memory1-10 TB diskInfiniband + 1Gb/s EthernetVarious accelerators (GPUs, multicores, ….)Scientific LinuxBuilt by ClusterVision

VU (74)

TU Delft (32) Leiden (16)

UvA/MultimediaN (16/36)

SURFnet6

10 Gb/s light pathsASTRON (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. 49-54

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?