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)
• 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)

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 lt25K, 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.
• (Youre 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 AB ( 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 gt1 compute cores
• Every PC is a parallel computer!
• Some PCs already have 48 cores
• Core count will increase to hundreds
• GPUs (manycores) 1000s very simple cores
• Intel Phi (2012) 60 Pentium-1s 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 gt1 core the outcome may depend on the order
  (called a race condition bug)

18
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

19
Networks

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

20
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

21
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

22
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

23
DAS-4
UvA/MultimediaN (16/36)
VU (74)
Dual quad-core Xeon E5620 24-48 GB
memory 1-10 TB disk Infiniband 1Gb/s
Ethernet Various accelerators (GPUs, multicores,
.) Scientific Linux Built by ClusterVision
SURFnet6
ASTRON (23)
10 Gb/s light paths
TU Delft (32)
Leiden (16)
24
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

25
Outline

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

26
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

27
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)

28
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

29
Pixars 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)
30
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
31
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

32
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

33
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

34
Literature (Context)

• Ami Marowka Back to Thin-Core Massively Parallel
  Processors, IEEE Computer, December 2011, pp.
  49-54

35
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?