Parallel Algorithms (WISM 459), 2014/2015
Teacher
Rob Bisseling
Time and place
Every Wednesday from 10.1513.00 hour at
Utrecht University, campus De Uithof.
Location of lectures: room
079 in the Buys Ballot building.
First lecture: September 10, 2014.
Last lecture: December 17, 2014.
No class on November 12.
Each session consists of 2 times 45 min. lectures and one exercise class
(or computer laboratory class, or question session) of 45 min.
Note that class always starts at 10.15 hour,
because it is part of Mastermath.
On September 17 and November 5, we will start with a lecture in BBG079 and finish
with a computer laboratory in Minnaert 205.
Intended audience
This is a Master's course which is an elective course for students
of the Master's programme Mathematical Sciences
of Utrecht University, part of the Scientific Computing specialisation.
The course is recommended for students in Mathematics or Computer Science
who are interested in scientific computing
and would like to obtain a first `handson experience'
in parallel programming.
It is also highly suitable for the Honours master
programme in Theoretical Physics and Mathematical Sciences
and for students interested in Computational Physics.
If you consider taking this course as part of a Bachelor's degree,
please contact the teacher first about the prerequisites.
The course is part of the Dutch National Master
Mastermath and
hence is open for all interested master students in Mathematics
in the Netherlands.
Registration for everybody is through Mastermath.
Course aim
Students will learn how to design a parallel algorithm for a
problem from the area of scientific computing
and how to write a parallel program that
solves the problem.
Learning goals
After completion of the course, the student is able to

design a parallel algorithm for a problem from the area of scientific computing;

analyse the cost of the algorithm in terms of computing time, communication time, and synchronisation time;
 write a parallel program based on an algorithm that solves the problem;
 write a report on the algorithm, its analysis, and numerical experiments performed, in a concise and readable form.
Credits
You get 8 ECTS credit points.
Contents
Today, parallel computers are appearing on our desktops.
The advent of dualcore and quadcore computers and the expected increase
in the number of cores in the coming years, inevitably will cause a major change
in our approach to software, such as the software we use in scientific computations.
Parallel computers drastically increase our computational capabilities
and thus enable us to model more realistically in many application areas.
To make efficient use of parallel computers, it is necessary to reorganise
the structure of our computational methods.
In particular, attention must be paid to the division of work
among the different processors solving a problem in parallel
and to the communication between them. Suitable parallel algorithms and systems
software are needed to realise the capabilities of parallel computers.
We will discuss extensively the most recent developments in the area
of parallel computers, ranging from multicore desktop PCs,
to clusters of PCs connected by switching devices,
to massively parallel computers with distributed memory
such as our national supercomputer Cartesius at SurfSARA in Amsterdam.
The following subjects will be treated:
 Types of existing parallel computers
 Principles of parallel computation: distributing the work evenly
and avoiding communication
 The Bulk Synchronous Parallel (BSP) model as an idealised model
of a parallel computer
 Use of BSPlib software as the basis for architectureindependent
programs

Parallel algorithms for the following problems:
prime number sieving, LU decomposition,
Fast Fourier Transform,
sparse matrixvector multiplication, iterative solution of
linear systems, graph matching.

Analysis of the computation, communication, and synchronisation
time requirements of these algorithms by the BSP model.
Language
The course will be given in English.
All reading material will be in English.
Prerequisites
Introductory course in linear algebra.
Some knowledge of algorithms and programming is helpful.
The laboratory classes will use the programming language C.
If you don't know C, this may be an opportunity to learn it.
You can also use C++, if you already know that language.
We will make use of the recently released
MulticoreBSP for C software developed by AlbertJan Yzelman.
This sofware runs on sharedmemory multicore PCs, and you can also run
your program without any change on distributedmemory machines
such as Cartesius.
Examination
The examination is in the form of two assignments during the course,
each with a weight of 45%, and three homework exercises
with a total weight of 10%.
The two assignments are carried out in the
exercise/laboratory class and at home.
A written report must be returned before the deadline.
For the two assignments, students can work individually or in pairs (but not in larger teams)
on the computer programs and can hand in a single report,
provided each did a fair share of the work
and can account for that.
Homework must be made individually.
If needed, you will have to explain your answers to the homework exercises.
Students from Utrecht must submit reports for the assignments in hardcopy paper format.
Students from outside Utrecht University may
also submit reports electronically in PDF format by email,
but hardcopy is still preferred.
All students must use LaTeX for the assignments; handwritten is OK for the
homework.
Literature
We closely follow the book
Parallel
Scientific Computation: A Structured Approach using BSP and MPI (PSC),
by Rob H. Bisseling,
Oxford University Press,
March 2004. ISBN 0198529392.
Please note that the book is now
available by the printingondemand system of Oxford University Press,
at the book's OUP website.
Furthermore, our student union Aeskwadraat may still have a limited number of copies.
I am currently working on a second edition,
and some additional material will be given to you
(and will be tested on you!).
The first chapter of the book is freely available from the
publisher's website,
see "Sample material".
Please note, the sample chapter has disappeared, hopefully only temporarily.
In addition, all my transparancies and WWW links to
background material are provided through this course page.
(Pointers to other relevant links are welcome!)
Transparancies
LaTeX sources (in Beamer) and PDFs of all my transparencies
(18 Mbyte in gzipped tar format).
The sources may be of help to other teachers
who want to teach from my book.
Students may find them helpful too.
Last update of the files: October 22, 2014.
The transparancies are complete
and they cover every section of the book.
They have been converted to
the Beamer macros in September 2012, and have been made up to date in the process.
I am adding experimental results on recent computer architectures as we go.
The old version (from 2007) in Prosper can be found
here.
You can also obtain the PDFs separately. Each PDF file represents one
lecture of about 45 minutes.
Chapter 1: sections
2
3
4
57
Chapter 2: sections
12
3
4
56
Chapter 3: sections
12
3
4
5
6
7
Chapter 4: sections
1
2
3
4
5
6
7
8
9
10
Appendix C:
Programming in BSP style using MPI,
1 (MPI1)
2 (MPI2).
Further reading

U. Naumann and O. Schenk (eds), Combinatorial Scientific Computing,
CRC Press, 2012.

J. J. Dongarra, I. S. Duff, D. C. Sorensen, and H. A. van der Vorst,
Numerical Linear Algebra for HighPerformance Computers,
SIAM, 1998.

G. H. Golub and C. F. van Loan, ``Matrix Computations",
Fourth edition, The Johns Hopkins University Press, 2012.

C. F. van Loan, ``Computational Frameworks for the Fast Fourier Transform",
SIAM, 1992.
Some weekly summaries and links
Wednesday September 10, 2014. Room 079 Buys Ballot building
The Bulk Synchronous Parallel model
(PSC section 1.2)
What is a parallel computer? Why do we compute in parallel?
The Bulk Synchronous Parallel (BSP) model by Valiant
comprises an abstract machine architecture, a framework for
developing algorithms,
and a cost function for analysing the run time of algorithms.
The BSP architecture is a set of processormemory pairs
connected by a black box communication network.
BSP algorithms consist of computation supersteps
and communication supersteps.
Communication is costed as hrelations.
The BSP cost of an algorithm is expressed
in machine parameters p, g, l.
The computing rate is r flop/s.
Motivation of the cost model: bottlenecks at the entrance
and exit of the communication network.
Parallel Inner Product Computation
(PSC section 1.3)
My First Parallel Algorithm: the computation
of the inner product of two vectors.
Possible distributions: cyclic and block.
Data distribution leads naturally to work distribution.
Communication is needed to add local inner products into a global
inner product.
Single Program, Multiple Data (SPMD) approach:
we don't want to write 400 different programs for a machine with
400 processors.
Onesided communication is a great invention
(in parallel computing, at least;).
Exercise class
In class: Exercise 1.2. Homework: Exercise 1.1
Please hand in exercise 1.1 (and not more), on September 17, 2014.
Interesting links
Wednesday September 17, 2014.
BSPlib, the Bulk Synchronous Parallel library. Room BBG079
(PSC section 1.4)
Benchmarking
(PSC sections 1.51.7)
Interesting links:
Computer Laboratory in room Minnaert 205
(from 12.0013.00 hour):
starting with BSPlib.
This is your first handson session with BSPlib. You may have to set up your accounts.
Download the latest version of
BSPedupack ,
my package of educational programs that
teaches how to use BSP.
Solve Exercise 1.3: try to run the benchmark,
exploring your parallel environment: your own laptop
and Cartesius. Change puts into gets.
If you own a laptop, bring it to class,
so we can install MulticoreBSP for C and check your software
Wednesday September 24, 2014.
Sequential LU Decomposition
(PSC sections 2.12.2)
Parallel LU Decomposition
(PSC section 2.3)
Exercise class
Discussion of the homework. Answers to Exercise 1.2.
Starting Exercise 1.7 from PSC. Design a parallel algorithm
for prime number generation.
Write your first parallel
program.
This program should generate all prime numbers below 1,000,000 in parallel.
Choosing the right distribution is the name of the game.
Hand in a report on exercise 1.7 before the deadline,
Wednesday October 15, 10.15 hour.
Note that you can work in pairs.
Interesting links:
Wednesday October 1, 2014. Class from 10.1512.15 hour.
TwoPhase Broadcasting
(PSC section 2.4)
Exercise class
Continue work on the prime number sieve. Discuss your approaches with me.
Today's class finishes early, at 12.15 hour.
Wednesday October 8, 2014.
Experiments with bsplu
(PSC sections 2.52.6)
Sequential Recursive Fast Fourier Transform (FFT)
(PSC sections 3.13.2) A wonderful algorithm.
Exercise class
Continue work on the prime number sieve.
 Vimtutor,
found to be useful if you want to learn some vi editor commands
on the Linux system.
Wednesday October 15, 2014.
Sequential Nonrecursive FFT
(PSC section 3.3)
Parallel Fast Fourier Transform
(PSC section 3.4)
Exercise class
Choose a new assignment. Possibilities:
solve Exercise 2.5 on Cholesky factorisation;
solve Exercise 2.7 on Strassen matrix multiplication;
solve Exercise 2.10 on optimising LU decomposition
(try to get Cartesius higher in the Top500,
improving its LINPACK performance);
write a parallel program for KarpSipser maximumcardinality matching in a general graph;
write a parallel program for counting selfavoiding walks (SAWs) on a 2D or 3D lattice.
(The current
world record
for SAWs on the 3D cubic lattice is 36 steps.)
Requests for different topics will be considered.
Please try to discuss the chosen topic with the teacher before midNovember.
Start working on Homework on Chapter 2, Exercise 2.3(a) on optimising the row swap
in LU decomposition.
Hand it in on October 29.
Wednesday October 22, 2014.
Program bspfft
(PSC section 3.6)
Please read section 3.5 yourself as a preparation.
Sequential sparse matrixvector multiplication
(PSC section 4.1)
Exercise class
Work on your new assignment.
Wednesday October 29, 2014
Sparse matrices and their data structures
(PSC section 4.2)
Parallel sparse matrixvector multiplication
(PSC section 4.3)
Exercise class
Discussion of Homework on Chapter 2.
Wednesday November 5, 2014.
Cartesian matrix distributions
(PSC section 4.4)
Mondriaan sparse matrix distribution
(PSC section 4.5)
Computing laboratory (12.0013.00 hour) Minnaert 205
Work on your new assignment.
Wednesday November 12, 2014. No class
because of participation in
Dagstuhl Seminar
Highperformance Graph Algorithms and Applications in Computational Science
Wednesday November 19, 2014.
New developments in 2D sparse matrix distribution
Movies of matrix partitioning, improvements in quality and speed
by the mediumgrain method (Mondriaan 4.0).
Laplacian matrices
(PSC section 4.8)
Exercise class
Work on your assignment.
Interesting links:
Wednesday November 26, 2014.
Sequential graph matching
(new material)
Parallel graph matching
(new material)
Exercise class
Homework on graph matching. Hand in December 3.
Interesting links:

Wired Article on twin primes problem, 22 November 2013.
Twin gap down from 70 million to 600, closing in on 2.
Wednesday December 3, 2014.
Guest lecture by AlbertJan Yzelman, KU Leuven, author of
MulticoreBSP.
Interesting links:

Sinterklaasgedicht door Wiebe Haanstra (in Dutch),
poem about the frustrations of parallel programming
Wednesday December 10, 2014. Room BBG 017
Program bspmatch
(new material)
Exprimental results
(new material)
Exercise class
Discussion of homework on graph matching.
Interesting links:
Wednesday December 17, 2011.
Message Passing Interface (MPI1)
(PSC sections C.1, C.2.1C.2.4)
Message Passing Interface (MPI2)
(PSC Section C2.5, C3, C4)
Exercise class
TBA
Please fill out the
Mastermath digital evaluation form.
Deadline second assignment January 15, 2015, 17.00 hour.
Hand in a report before the deadline.
Please use the batch system of Cartesius and not the interactive system
for your test runs. The interactive system is only for development.
Other courses that teach the BSP model (among others)

Parallel computing (BKULH03F9A)
at KU Leuven, Belgium, by AlbertJan Yzelman.

Efficient Parallel Algorithms (CS329)
at University of Warwick, UK,
by Alex Tiskin.

Parallel and Distributed Programming (CS236370)
by Assaf Schuster,
at the Technion, Haifa, Israel.

High Performance and Parallel Computing (22C177)
by Suely Oliveira,
at the University of Iowa.

Parallel computing (in Polish)
by Przemek Stpiczynski at the Maria CurieSklodowska University,
Lublin, Poland.
Last update of this page: October 22, 2014
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