NAIS
Centre for
Numerical Algorithms &
Intelligent Software
Software Challenges in
Computational Science
Taking Scientific Supercomputing
out of the Stone Age
A D Kennedy
University of Edinburgh
Future Directions in Tensor-Based
Computation and Modelling
NAIS Overview
We contemplate strongly typed, categorical,
efficient, portable, reusable, modular,
robust, architecture-neutral, buzzwordcompliant software and languages for high
performance parallel tensor-based scientific
computing.
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
3
NAIS Problem
 Programs need to run efficiently on multiple architectures
 It must be easy to make high-level algorithmic changes
 Programs must adapt themselves around low-level
assembly routines and data layouts
 Programs should use state-of-the-art numerical methods
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
4
NAIS Tensors?
Physical problems are highly constrained, or
maybe even completely determined, by their
symmetries
Dynamical behaviour realized by multilinear
representations of the symmetry, i.e., tensors
We often need to consider continuous
symmetries (Lie groups) and both finite
dimensional (spin) and infinite dimensional
(space-time) representations
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
5
NAIS Commonality
Many (most?) large-scale physics
programs are built out of a small set of
common numerical “building blocks”
such as
Linear algebra
FFTs
Symplectic integrators
Discrete differential operators
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
6
NAIS Numerical libraries?
Nobody writes their own sqrt or exp routines
Unambiguous standard definitions
IEEE 754 (but who uses unnormalized underflow?)
sin(3.14159e+30) is a random number in [-1,1]
Efficient implementation provided by vendors for
every architecture
So why don’t we always use the nice libraries
that kind numerical analysts write for us?
Why do we keep using our own implementations
of conjugate gradients to solve linear systems?
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
7
NAIS BLAS and all that
Our problems involve huge sparse matrices
Numerical libraries allow us to use our own “black
box” functional linear operators
But even our vectors are large: they may be
distributed over 105 processors
We need implementations of state-of-the-art
linear solvers that will use our implementation
of underlying vector operations
AXPY, ..., inner products, norms, ... BLAS!
But with our own strange vector representations
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
8
NAIS
Why not standard classes?
We must still write our time-critical
kernels in assembler
We even build our own hardware for these
kernels
QCDSP, QCDOC, Blue Gene, …
We mainly optimize data motion
(prefetching, communication, etc.)
Flops are cheap, moving data is expensive
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
9
NAIS Abstraction
Languages and interfaces that allows
abstraction of these building blocks
Higher-level algorithms expressed in terms of
abstract lower-level algebraic structures
Need languages than allow us to describe the
necessary structure
The structure of a Hilbert space is a class of
classes = a category ≈ an interface
Need agreement on standard structures
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
10
NAIS Categories
A category is a class of classes sharing the
same structure
But not necessarily having anything else in
common
SU(2) and SU(3) are both groups, but you can’t
multiply their elements together (except in C++ or
Java)
Structure is defined by
An explicit set of signatures
An implicit set of axioms
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
11
NAIS Algorithmic categories
Follow mathematical structure
Why not write linear system solvers the way they
are expressed in textbooks?
Select appropriate algorithms using type
information
At compile time
E.g., use CG for positive symmetric matrices
Mendacity is sometimes useful
E.g., use CG for non-positive symmetric matrices
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
12
NAIS Compilers & Languages
Syntactic sugar is good
It is easier to avoid bugs if we can write
z ←A*x + y rather than SAXPY(A,x,y,z)
... or was it SAXPY(A,z,x,y)?
Low-level automatic optimization
Compilers allocate registers better than humans do
Automatic parallelization or vectorization
Requires programmers to write “recognizable” patterns
Work-around for lack of standard structures
Automatic data pre-fetching perhaps a difficult but
possible goal
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
13
NAIS Memory management
Memory management for vector temporaries
Not enough memory to allocate them statically
Heap fragmentation
Stacks awkward for common sub-expression
elimination and so forth
New memory management model is needed,
neither universal garbage collection nor complete
user responsibility is good enough
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
14
NAIS Serialization
Portable interchange format for objects
Data grids: exchange everything as serialised data
Many ↔ one instead of many ↔ many
Usually XML-based
XML is verbose, why not use binary format?
XML compresses quite well
Data layout transformations
Redistribute grid points for different number of
processors
FFTs?
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
15
NAIS Solutions?
Standard categories for common structures
Relatively easy in some areas, e.g., linear algebra
Harder to get “correct” abstractions for others
Language bindings
We can’t persuade the world to use a new
language, even if it is better
Type-checking and optimization better for some
languages than others
Implementation of state-of-the-art algorithms
in terms of these categories
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
16
NAIS Obstacles
 Risk: application scientist are not willing to take on
the risk of using experimental software in addition to
the risks of any cutting-edge scientific
supercomputing project
 Careers: developing scientific software does not get
you a permanent academic job
 Expertise: very few applications scientist are familiar
with modern software techniques; most
supercomputer codes are written by graduate
students who believe that documentation is an
unnecessary evil
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
17
NAIS Software strategy
Must be constructed in layers with welldefined categorical interfaces
Low-level machine dependent layers
Intermediate building blocks (e.g., linear algebra)
Top-level application specific
Categorical interface means that functionality
is specified, not implementation or data layout
Layers can be changed independently
Must be well documented
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
18
NAIS Sociological strategy
Must be a cooperative venture of application
scientists, numerical analysts, computer
scientists, vendors, and funders
Needs international cooperation
Need to develop real software to get it
accepted
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
19
NAIS
Funding requirements
Funding to encourage people to work on a
long-term project that will not get them
publications in their own field
Input from experts in several application
areas, numerical analysis, and computer
science
People to write and test prototypes
People to write documentation
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
20
NAIS Conclusions
I’ll still write it
in Fortran
Is it really buzzwordcompliant?
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
21
NAIS
Centre for Numerical Algorithms
and Intelligent Software
 At the end of 2008, the UK’s Engineering and Physical Sciences Research Council
(EPSRC) together with the Scottish Funding Council (SFC) have provided funds to establish
this centre as a joint venture of the University of Edinburgh, Heriot-Watt University and the
University of Strathclyde. The total budget of NAIS is £7.5M and the duration of the grant is
5 years, with first spending from August of 2009.
 What is the purpose of NAIS?

NAIS will bridge the gap between numerical analysts, computer scientists and HPC software developers by developing
new systems of code annotation, new compilers and efficient implementations for application-oriented computational
methods such as adaptive finite elements, multiscale modelling, molecular simulation and optimization.
 Who is involved in NAIS?

NAIS is a partnership of the Schools of Mathematics and Informatics and the Edinburgh Parallel Computing Centre
(EPCC) at the University of Edinburgh, and the Departments of Mathematics at Heriot-Watt and Strathclyde Universities.
In addition, NAIS will include collaborations with researchers in the sciences and engineering at the three universities, and
a network of collaborations with other institutions including, so far, the University of Cambridge, the University of Warwick,
and Wales Institute for Mathematics and Computer Science. A programme with CERFACS in Toulouse will provide for
joint workshop and training activities. Industrial collaborations are to be established with IBM, Schlumberger, D.E. Shaw,
SGI, Cray, Orage/France Telecom, SAS. An international advisory committee will be established including
 Fran Berman, University of California at San Diego
 Andreas Frommer, Wuppertal
 David Keyes, Columbia and Lawrence Livermore National Laboratory
 J. Tinsley Oden, University of Texas at Austin
 Philippe Toint, University of Namur, Belgium
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
22
NAIS
Centre for Numerical Algorithms
and Intelligent Software
 The first director of NAIS is Benedict Leimkuhler (Mathematics, University of Edinburgh) and
the Steering Committee consists of




Mark Ainsworth (Mathematics, Strathclyde University)
Murray Cole (Informatics, University of Edinburgh)
Dugald Duncan (Mathematics, Heriot-Watt University)
Arthur Trew (Edinburgh Parallel Computing Centre).
 What research activities are envisaged in NAIS?


NAIS will operate substantial training, visitor and workshop programmes in all relevant areas of numerical analysis,
computer science and HPC software development. There will be additional activities at the NAIS partners (currently
Cambridge, Warwick and Wales Institute for Mathematical and Computational Science)
What posts are anticipated in NAIS?





6 lectureships (permanent positions) in Mathematics (two each) at Edinburgh, Heriot-Watt and Strathclyde Universities.
A lectureship in Informatics at the University of Edinburgh
10 Postdoctoral Research Assistantships (3 years each)
24 PhD studentships (typically 4 years duration)
The first posts will be advertised in 2009.
 Where can I find out more about NAIS?


See the website at http://www.nais.org.uk or send an email to [email protected]
PhD student applications are welcome at any time and should be sent to the relevant department with a cover letter that
mentions the “Numerical Algorithms and Intelligent Software Science and Innovation Project.”
Saturday, 03 October 2015
A D Kennedy
Future Directions in Tensor-Based
Computation and Modelling
23
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Software Challenges in Computational Science