Fortran 2003/2008 Lectures - Prace Training Portal

Pekka Manninen
Sami Saarinen
David Henty
Fortran 2003/2008
September 11-13, 2012
PRACE Advanced Training Centre
CSC – IT Center for Science Ltd, Finland

All material (C) 2012 by the authors.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0
Unported License, http://creativecommons.org/licenses/by-nc-sa/3.0/

Agenda
Tuesday
Wednesday
9.00-9.45 Getting started with Fortran
10.00-10.45 Arrays in Fortran
11.00-12.00 Exercises
12.00-13.00 Lunch break
13.00-13.45 Procedures & modules
14.00-16.00 Exercises
9.00-9.45 Derived datatypes
10.00-10.45 File I/O
11.00-12.00 Exercises
12.00-13.00 Lunch break
13.00-13.45 Exercises
14.00-14.45 Other handy Fortran features
15.00-16.00 Exercises
Thursday
9.00-9.45
Coarray programming model
and basic syntax
10.00-11.00 Exercises
11.15-12.00 Further coarray features
12.00-13.00 Lunch break
13.00-14.00 Exercises
14.15-15.00 Advanced coarray features
15.15-16.00 Exercises

Web resources
CSC’s Fortran95/2003 Guide (in Finnish) for free
http://www.csc.fi/csc/julkaisut/oppaat
Fortran wiki: a resource hub for all aspects of Fortran programming
http://fortranwiki.org
GNU Fortran online documents
http://gcc.gnu.org/onlinedocs/gcc-4.5.0/gfortran
Code examples
http://www.nag.co.uk/nagware/examples.asp
http://www.personal.psu.edu/jhm/f90/progref.html
Mistakes in Fortran 90 Programs That Might Surprise You
http://www.cs.rpi.edu/~szymansk/OOF90/bugs.html

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25

Programming models for HPC
Parallel Programming
with
Fortran Coarrays
Delivered at PRACE Advanced Training Centre,
CSC IT Center for Science Ltd, Finland,
September 13, 2012
David Henty, Alan Simpson (EPCC)
Harvey Richardson, Bill Long (Cray)
The challenge is to efficiently map a problem to the
architecture we have
Take advantage of all computational resources
Manage distributed memories etc.
Optimal use of any communication networks
The HPC industry has long experience in parallel programming
Vector, threading, data-parallel, message-passing etc.
We would like to have models or combinations that are
efficient
safe
easy to learn and use
5
Tutorial Overview
The Fortran Programming Model in context
Basic coarray features
Practical Session 1
Further coarray features
Practical Session 2
Advanced coarray features
Practical Session 3
Experiences with coarrays
Why consider new programming models?
Next-generation architectures bring new challenges:
Very large numbers of processors with many cores
Complex memory hierarchy
even today (2011) we are at 500k cores
Parallel programming is hard, need to make this simpler
Some of the models we currently use are
bolt-ons to existing languages as APIs or directives
Hard to program for underlying architecture
unable to scale due to overheads
So, is there an alternative to the models prevalent today?
Most popular are OpenMP
2
6
Shared-memory directives and OpenMP
The Fortran
Programming Model
in context
memory
threads
7
Motivation
Fortran now supports parallelism as a full first-class feature of
the language
Changes are minimal
Performance is maintained
Flexibility in expressing communication patterns
OpenMP: work distribution
memory
1-8 9-16 17-24 25-32
!$OMP PARALLEL DO
do i=1,32
a(i)=a(i)*2
end do
threads
4
27
8

OpenMP implementation
MPI
memory
process 0
process 1
process
memory
memory
cpu
cpu
threads
cpus
MPI_Send
MPI_Recv
9
13
Shared Memory Directives
Multiple threads share global memory
Most common variant: OpenMP
Program loop iterations distributed to threads,
more recent task features
Each thread has a means to refer to private objects
within a parallel context
Terminology
Thread, thread team
Implementation
Threads map to user threads running on one SMP node
Extensions to distributed memory not so successful
OpenMP is a good model to use within a node
Message Passing
Participating processes communicate using a message-passing
API
Remote data can only be communicated (sent or received) via
the API
MPI (the Message Passing Interface) is the standard
Implementation:
MPI processes map to processes within one SMP node or
across multiple networked nodes
API provides process numbering, point-to-point and collective
messaging operations
Mostly used in two-sided way, each endpoint coordinates in
sending and receiving
10
14
Cooperating Processes Models
SHMEM
process 0
memory
process 1
memory
PROBLEM
cpu
cpu
processes
shmem_put(a,b
11
15
Message Passing, MPI
SHMEM
process
memory
memory
memory
Participating processes communicate using an API
Fundamental operations are based on one-sided PUT and GET
Need to use symmetric memory locations
Remote side of communication does not participate
Can test for completion
Barriers and collectives
Popular on Cray and SGI hardware, also Blue Gene version
To make sense needs hardware support for low-latency RDMAtype
operations
cpu
cpu
cpu
12
28
16

UPC
Fortran coarray model
thread
thread
thread
process
process
process
memory
memory
memory
memory
memory
memory
cpu
cpu
cpu
cpu
cpu
cpu
17
21
UPC
Fortran coarray model
thread
thread
thread
process
process
process
memory
memory
memory
memory
memory
memory
cpu
cpu
cpu
upc_forall(i=0;i

Coarray Fortran
"Coarrays were designed to answer the question:
What is the smallest change required to convert Fortran
into a robust and efficient parallel language?
The answer: a simple syntactic extension.
It looks and feels like Fortran and requires
Fortran programmers to learn only a few new rules."
Accessing coarrays
integer :: a(4)[*], b(4)[*] !declare coarrays
b(:) = a(:)[n] ! copy
integer arrays a and b declared to be size 4 on all images
copy array a from remote image n into local array b
() for local access [] for remote access
e.g. for two images and n = 2:
image 1 image 2
John Reid,
ISO Fortran Convener
a
b
1 2 3 4 a 2 9 3 7
25 96
37
78
b 10 2 11 9 12 3 13 7
25
29
Some History
Introduced in current form by Numrich and Reid in 1998 as a
simple extension to Fortran 95 for parallel processing
Many years of experience, mainly on Cray hardware
A set of core features are now part of the Fortran standard
ISO/IEC 1539-1:2010
Additional features are expected to be published in a
Technical Specification in due course.
Synchronisation
Be careful when updating coarrays:
If we get remote data was it valid?
Could another process send us data and overwrite
something we have not yet used?
How do we know that data sent to us has arrived?
Fortran provides synchronisation statements
For example, barrier for synchronisation of all images:
sync all
do not make assumptions about execution timing on images
unless executed after synchronisation
Note there is implicit synchronisation at program start
26
30
How Does It Work?
SPMD - Single Program, Multiple Data
single program replicated a fixed number of times
Each replication is called an image
Images are executed asynchronously
execution path may differ from image to image
some situations cause images to synchronize
Images access remote data using coarrays
Normal rules of Fortran apply
Retrieving information about images
Two intrinsics provide index of this image and number of
images
this_image() (image indexes start at 1)
num_images()
real :: x[*]
if(this_image() == 1) then
read *,x
do image = 2,num_images()
x[image] = x
end do
end if
sync all
27
31
What are coarrays?
Arrays or scalars that can be accessed remotely
images can access data objects on any other image
Additional Fortran syntax for coarrays
Specifying a codimension declares a coarray
real, dimension(10), codimension[*]:: x
real :: x(10)[*]
Making remote references
We used a loop over images
do image = 2,num_images()
x[image] = x
end do
Note that array indexing within the coindex is not allowed
so we can not write
these are equivalent declarations of a array x
of size 10 on each image
x is now remotely accessible
coarrays have the same size on each image!
x[2:num_images()] = x
! illegal
28
30
32

Data usage
coarrays have the same size on every image
Declarations:
round brackets () describe rank, shape and extent of local
data
square brackets [] describe layout of images that hold local
data
Many HPC problems have physical quantities mapped to n-
dimensional grids
You need to implement your view of global data from the local
coarrays as Fortran does not provide the global view
You can be flexible with the coindexing (see later)
You can use any access pattern you wish
Example: maximum of array
real :: a(10)
real :: maximum[*]
implicit synchronisation
call random_number(a)
maximum = maxval(a)
ensure all images set local maximum
sync all
if (this_image() == 1) then
do image = 2, num_images()
maximum = max(maximum, maximum[image])
end do
do image = 2, num_images()
maximum[image] = maximum
end do
end if
ensure all images have copy of maximum value
sync all
33
37
Data usage
processors
4 elements per processor = 4 coarrays on 4 images
integer :: ca(4)[*]
do image=1,num_images()
print *,ca(:)[image]
end do
Recap
We now know the basics of coarrays
declarations
references with []
this_image() and num_images()
sync all
A
ca(1:4)[1] ca(1:4)[2] ca(1:4)[3] ca(1:4)[4]
Now consider a full program example...
image 1 image 2 image 3 image 4
34
38
1D cyclic data access
coarray declarations remain unchanged
but we use a cyclic access pattern
integer :: ca(4)[*]
do i=1,4
do image=1,num_images()
print *,ca(i)[image]
end do
end do
A
Example2: Calculate density of primes
program pdensity
implicit none
integer, parameter :: n=10000000, nimages=8
integer start,end,i
integer, dimension(nimages) :: nprimes[*]
real density
start = (this_image()-1) * n/num_images() + 1
end = start + n/num_images() - 1
nprimes(this_image())[1] = num_primes(start,end)
...
...
...
...
sync all
image 1 image 2 image 3 image 4
35
39
Synchronisation
code execution on images is independent
programmer has to control execution using synchronisation
synchronise before accessing coarrays
ensure content is not updated from remote images before
you can use it
synchronise after accessing coarrays
ensure new content is available to all images
implicit synchronisation after variable declarations at first
executable statement
guarantees coarrays exist on all images when your first
program statement is executed
We will revisit this topic later
Example2: Calculate density of primes
if (this_image()==1) then
nprimes(1)=sum(nprimes)
density=real(nprimes(1))/n
print *,"Calculating prime density on", &
& num_images(),"images"
print *,nprimes(1),'primes in',n,'numbers'
write(*,'(" density is ",2Pf0.2,"%")')density
write(*,'(" asymptotic theory gives ", &
& 2Pf0.2,"%")')1.0/(log(real(n))-1.0)
end if
36
31
40

Example2: Calculate density of primes
Calculating prime density on 2 images
664580 primes in 10000000 numbers
density is 6.65%
asymptotic theory gives 6.61%
Backup Slides
HPF model
41
45
Launching a coarray program
The Fortran standard does not specify how a program is
launched
The number of images may be set at compile, link or run-time
A compiler could optimize for a single image
Examples on Linux
Cray XE
aprun n 16000 solver
g95
./solver --g95 -images=2
High Performance Fortran (HPF)
Data Parallel programming model
Single thread of control
Arrays can be distributed and operated on in parallel
Loosely synchronous
Parallelism mainly from Fortran 90 array syntax, FORALL and
intrinsics.
This model popular on SIMD hardware (AMT DAP, Connection
Machines) but extended to clusters where control thread is
replicated
42
46
Observations so far on coarrays
HPF
Natural extension, easy to learn
Makes parallel parts of program obvious (syntax)
Part of Fortran language (type checking, etc)
No mapping of data to buffers (or copying) or creation of
complex types (as we might have with MPI)
Compiler can optimize for communication
memory
pe
memory
pe
memory
pe
memory
pe
More observations later...
memory
cpu
43
47
Exercise Session 1
HPF
Look at the Exercise Notes document for full details
memory
memory
memory
memory
number of images
pe
pe
pe
pe
Extend the simple Fortran code provided in order to perform
operations on parts of a picture using coarrays
memory
A=SQRT(A)
A (distributed)
cpu
44
32
48

2D Data
More Coarray Features
However, images are not restricted to a 1D arrangement
For example, we can arrange images in 2x2 grid
coarrays with 2 codimensions
integer :: ca(4)[2,*]
ca(:)[1,1] ca(:)[1,2]
Parallel Programming with Fortran Coarrays
Delivered at PRACE Advanced Training Centre,
CSC IT Center for Science Ltd, Finland,
September 13, 2012
ca(:)[2,1] ca(:)[2,2]
David Henty, Alan Simpson (EPCC)
Harvey Richardson, Bill Long (Cray)
image 1 image 2 image 3 image 4
5
Overview
2D Local Array on 2D Grid
Multiple Dimensions and Codimensions
ca(1:4,1:4)[1,1] ca(1:4,1:4)[1,2]
Allocatable Coarrays and Components of Coarray
Structures
Coarrays and Procedures
image 1
A
image 3
ca(1:4,1:4)[2,1] ca(1:4,1:4)[2,2]
image 2
global access: ca(3,1)[2,2]
local access: ca(3,1)
image 4
2
6
Mapping data to images
Coarray Subscripts
P(m,n)[*]
PROBLEM
images
Physical quantity
PRESSURE
Variables/arrays
P(m,n)
P(m,n)[k,*]
images
Fortran arrays defined by rank, bounds and shape
integer, dimension(10,4) :: array
rank 2
lower bounds 1, 1; upper bounds 10, 4
shape [10, 4]
Coarray Fortran adds corank, cobounds and coshape
integer :: array(10,4)[3,*]
corank 2
lower cobounds 1, 1; upper cobounds 3, m
coshape [3, m]
m would be ceiling(num_images()/3)
3
7
2D Data
assemble rather than distribute
-
Can assemble a 2D data structure from 1D arrays
integer :: ca(4)[*]
image 1
-
image 3
Multiple Codimensions
Coarrays with multiple Codimensions:
character :: a(4)[2, *] !2D grid of images
for 4 images, grid is 2x2; for 16 images, grid is 2x8
real :: b(8,8,8)[10,5,*] !3D grid of images
8x8x8 local array; with 150 images, grid is 10x5x3
integer :: c(6,5)[0:9,0:*] !2D grid of images
lower cobounds [ 0, 0 ]; upper cobounds [ 9,n]
useful if you want to interface with MPI or want C like coding
Sum of rank and corank should not exceed 15
Flexibility with cobounds
can set all but final upper cobound as required
image 2
image 4
4
8

Codimensions: What They Mean
this_image() & image_index()
for that coarray only
A map so an image can find the coarray on any other image
access the coarray using its grid coordinates
e.g. character a(4)[2, *] on 6 images
gives a 2 x 3 image grid
usual Fortran subscript order to determine image index
Axis 1
Axis 2
1
2
1
image 1
2
image 3
image 2 image 4
a(2)[2,3]
a(4)[1,2]
3
image 5
image 6
this_image()
returns the image index, i.e., number between 1 and
num_images()
this_image(z)
returns the rank-one integer array of cosubscripts for the
calling image corresponding to the coarray z
this_image(z, dim) returns cosubscript of
codimension dim of z
image_index(z, sub)
returns image index with cosubscripts sub for coarray z
sub is a rank-one integer array
9
13
Codimensions and Array-Element Order
Example 1
Storage order for multi-dimensional
Fortran arrays
Ordering of images in multi-dimensional
cogrids
PROGRAM CAF_Intrinsics
this_image() = 2
this_image() = 5
this_image(b) = [1, 2]
image_index(b,[3,2]) = 7
this_image(b) = [2, 1]
real p(2,3,8)
real q(4)[2,3,*]
real :: b(90,96)[4,*]
image_index(b,[3,2]) = 7
1
2
Location Element
1 p(1,1,1)
2 p(2,1,1)
3 p(1,2,1)
4 p(2,2,1)
5 p(1,3,1)
6 p(2,3,1)
7 p(1,1,2)
8 p(2,1,2)
Image Elements
1 q(1:4)[1,1,1]
2 q(1:4)[2,1,1]
3 q(1:4)[1,2,1]
4 q(1:4)[2,2,1]
5 q(1:4)[1,3,1]
6 q(1:4)[2,3,1]
7 q(1:4)[1,1,2]
8 q(1:4)(2,1,2]
this_image()
this_image(b)
image_index(b,[3,2])
Axis 1
Axis 2
1
2
3
4
48 p(2,3,8)
48 q(1:4)[2,3,8]
END PROGRAM CAF_Intrinsics
this_image() = 7
this_image(b) = [3, 2]
10
image_index(b,[3,2]) = 7
14
Multi Codimensions: An Example
Domain Decomposition
() gives local domain size
[] provides image grid and easy access to other images
Example 2
PROGRAM CAF_Intrinsics
this_image() = 96
this_image(c) = [1, 0, 4]
image_index(c,[1,0,4]) = 96
2D domain decomposition of Braveheart
Global data is 360 x 192
real :: c(4,4,4)[5,-1:4,*]
Axis 3
Domain decomposition on 8 images with 4 x 2 grid
local array size: (360 / 4) x (192 / 2) = 90 x 96
declaration = real :: localPic(90,96)[4,*]
this_image()
Axis 1
Axis 2
Axis 2
Axis 1
this_image(c)
360
image_index(c,[1,0,4])
END PROGRAM CAF_Intrinsics
this_image() = 13
this_image(c) = [3, 1, 1]
image_index(c,[1,0,4]) = 96
this_image() = 90
this_image(c) = [5, 4, 3]
image_index(c,[1,0,4]) = 96
192
11
15
Multi Codimensions: An Example
Domain Decomposition
() gives local domain size
[] provides image grid and easy access to other images
2D domain decomposition of Braveheart
Boundary Swapping
PROGRAM CAF_HaloSwap
integer, parameter :: nximages = 4, nyimages = 2
integer, parameter :: nxlocal = 90, nylocal = 96
real :: pic(0:nxlocal+1, 0:nylocal+1)[nximages,*] ! Declare coarray with halos
integer :: myimage(2) ! Array for my row & column coordinates
Global data is 360 x 192
myimage = this_image(pic) ! Find my row & column coordinates
Find cosubscripts
Domain decomposition on 8 images with 4 x 2 grid
local array size: (360 / 4) x (192 / 2) = 90 x 96
declaration = real :: localPic(90,96)[4,*]
image 1
Axis 1
Axis 2
1
1
2
image 5
image 6
! Initialise pic on each image
sync all
Ensures pic initialised before accessed by other images
! Halo swap
if (myimage(1) > 1) &
pic(0,1:nylocal) = pic(nxlocal,1:nylocal)[myimage(1)-1,myimage(2)]
if (myimage(1) < nximages) &
pic(nxlocal+1,1:nylocal) = pic(1,1:nylocal)[myimage(1)+1,myimage(2)]
image 3
90
image 2
2
image 7
if (myimage(2) > 1) &
pic(1:nxlocal,0) = pic(1:nxlocal,nylocal)[myimage(1),myimage(2)-1]
if (myimage(2) < nyimages)
pic(1:nxlocal,nylocal+1) = pic(1:nxlocal,1)[myimage(1),myimage(2)+1]
96
3
sync all
Ensures all images have got old values before pic is updated
image 4
image 8
! Update pic on each image
4
END PROGRAM CAF_HaloSwap
12
16

Allocatable Coarrays
Can have allocatable Coarrays
real, allocatable :: x(:)[:], s[:,:]
n = num_images()
allocate(x(n)[*], s[4,*])
Must specify cobounds in allocate statement
The size and value of each bound and cobound must be same
on all images.
allocate(x(this_image())[*]) ! Not allowed
allocate statement involving coarrays
deallocate statements involving coarrays
Coarrays and Procedures
An explicit interface is required if a dummy argument is a
coarray
Dummy argument associated with coarray, not a copy
avoids synchronisation on entry and return
Other restrictions on passing coarrays are:
the actual argument should be contiguous
a(:,2) is OK, but a(2,:) is not contiguous
or the dummy argument should be assumed shape
... to avoid copying
Function results cannot be coarrays
17
21
21
Differently Sized Coarray Components
A coarray structure component can vary in size per image
Declare a coarray of derived type with a component that is
!Define data type with allocatable component
type diffSize
real, allocatable :: data(:)
end type diffSize
!Declare coarray of type diffSize
type(diffSize) :: x[*]
! Allocate x%data to a different size on each image
allocate(x%data(this_image())
Coarrays as Dummy Arguments
As with standard Fortran arrays, the coarray dummy arguments
in procedures can be:
Explicit shape: each dimension of a coarray declared with explicit value
Assumed shape: extents and bounds determined by actual array
Assumed size: only size determined from actual array
Allocatable: the size and shape can be determined at run-time
subroutine s(n, a, b, c, d)
integer :: n
real :: a(n) [n,*] ! explicit shape - permitted
real :: b(:,:) [*] ! assumed shape - permitted
real :: c(n,*) [*] ! assumed size - permitted
real, allocatable :: d(:) [:,:] ! allocatable - permitted
18
22
22
Pointer Coarray Structure Components
We are allowed to have a coarray that contains components
that are pointers
Note that the pointers have to point to local data
We can then access one of the pointers on a remote image to
get at the data it points to
This technique is useful when adding coarrays into an existing
MPI code
We can insert coarray code deep in call tree without
changing many subroutine argument lists
Example follows...
Assumed Size Coarrays
Allow the coshape to be remapped to corank 1
program cmax
real, codimension[8,*] :: a(100), amax
a = [ (i, i=1,100) ] * this_image() / 100.0
amax = maxval( a )
sync all
amax = AllReduce_max(amax)
...
contains
real function AllReduce_max(r) result(rmax)
real :: r[*]
sync all
rmax = r
do i=1,num_images()
rmax = max( rmax, r[i] )
end do
sync all
end function AllReduce_max
19
23
Pointer Coarray Structure Components...
Existing non-coarray arrays u,v,w
Create a type (coords) to hold pointers (x,y,z) that we use to point
to x,y,z. We can use the vects coarray to access u, v, w.
subroutine calc(u,v,w)
real, intent(in), target, dimension(100) :: u,v,w
type coords
real, pointer, dimension(:) :: x,y,z
end type coords
type(coords), save :: vects[*]
vects%x => u ; vects%y => v ; vects%z => w
sync all
firstx = vects[1]%x(1)
Coarrays Local to a Procedure
Coarrays declared in procedures must have save attribute
unless they are dummy arguments or allocatable
save attribute: retains value between procedure calls
avoids synchronisation on entry and return
Automatic coarrays are not permitted
Automatic array: local array whose size depends on dummy arguments
would require synchronisation for memory allocation and deallocation
would need to ensure coarrays have same size on all images
subroutine t(n)
integer :: n
real :: temp(n)[*] ! automatic - not permitted
integer, save :: x(4)[*] ! coarray with save attribute
integer :: y(4)[*] ! not saved not permitted
20
35
24

Summary
Coarrays with multiple codimensions used to create a grid of
images
() gives local domain information
[] gives an image grid with easy access to other images
Can be used in various ways to assemble a multi-dimensional
data set
this_image() and image_index()
are intrinsic functions that give information about the images in an
multi-codimension grid
Flexibility from non-coarray allocatable and pointer
components of coarray structures
Coarrays can be allocatable, can be passed as arguments
to procedures, and can be dummy arguments
25
36

Synchronisation mistakes
Advanced Features
Parallel Programming with Fortran Coarrays
Delivered at PRACE Advanced Training Centre,
CSC IT Center for Science Ltd, Finland,
September 13, 2012
David Henty, Alan Simpson (EPCC)
Harvey Richardson, Bill Long (Cray)
This code is wrong
subroutine allreduce_max_getput(v,vmax)
double precision, intent(in) :: v[*]
double precision, intent(out) :: vmax[*]
integer i
sync all
vmax=v
if (this_image()==1) then
do i=2,num_images()
vmax=max(vmax,v[i])
end do
do i=2,num_images()
vmax[i]=vmax
end do
end if
sync all
5
Advanced Features: Overview
Execution segments and Synchronisation
Non-global Synchronisation
Critical Sections
Visibility of changes to memory
Other Intrinsics
Miscellaneous features
Future developments
Synchronisation mistakes
It breaks the rules
subroutine allreduce_max_getput(v,vmax)
double precision, intent(in) :: v[*]
double precision, intent(out) :: vmax[*]
integer i
sync all
vmax=v
if (this_image()==1) then
do i=2,num_images()
vmax=max(vmax,v[i])
end do
do i=2,num_images()
vmax[i]=vmax
end do
end if
sync all
2
6
More on Synchronisation
We have to be careful with one-sided updates
If we read remote data, was it valid?
Could another process send us data and overwrite
something we have not yet used?
How do we know when remote data has arrived?
The standard introduces execution segments to deal with this:
segments are bounded by image control statements
The standard can be summarized as follows:
If a variable is defined in a segment, it must not be referenced,
defined, or become undefined in another segment unless the
segments are ordered John Reid
Synchronisation mistakes
This is ok
subroutine allreduce_max_getput(v,vmax)
double precision, intent(in) :: v[*]
double precision, intent(out) :: vmax[*]
integer i
sync all
if (this_image()==1) then
vmax=v
do i=2,num_images()
vmax=max(vmax,v[i])
end do
do i=2,num_images()
vmax[i]=vmax
end do
end if
sync all
3
7
Execution Segments
image 1
program hot
double precision :: a(n)
double precision :: temp(n)[*]
!...
if (this_image() == 1) then
do i=1, num_images()
read *,a
temp(:)[i] = a
end do
end if
temp = temp + 273d0
sync all
call ensemble(temp)
2
image 2
program hot
double precision :: a(n)
double precision :: temp(n)[*]
!...
if (this_image() == 1) then
do i=1, num_images()
read *,a
temp(:)[i] = a
end do
end if
temp = temp + 273d0
sync all
call ensemble(temp)
More about sync all
Usually all images execute the same sync all statement
But this is not a requirement...
Images execute different code with different sync all
statements
All images execute the first sync all they come across
this may match an arbitrary sync all on another image
causing incorrect execution and/or deadlock
ordering
iimage synchronisation points
wrong answers
4
37
8

More about sync all
e.g. Image practical: wrong answer
! Do halo swap, taking care at the upper and lower picture boundaries
if (myimage < numimage) then
oldpic(1:nxlocal, nylocal+1) = oldpic(1:nxlocal, 1)[myimage+1]
sync all
end if
! ... and the same for down halo
! Now update the local values of newpic
...
All images NOT executing this sync all
! Need to synchronise to ensure that all images have finished reading the
! oldpic halo values on this image before overwriting it with newpic
sync all
oldpic(1:nxlocal,1:nylocal) = newpic(1:nxlocal,1:nylocal)
! Need to synchronise to ensure that all images have finished updating
! their oldpic arrays before this image reads any halo data from them
sync all
All images ARE executing this sync all
Other Synchronisation
lock and unlock statements
Control access to data defined or referenced by more than one
image
as opposed to critical which controls access to lines of
code
USE iso_fortran_env module and define coarray of
type(lock_type)
e.g. to lock data on image 2
type(lock_type) :: qLock[*]
lock(qLock[2])
!access data on image 2
unlock(qLock[2])
9
13
More about sync all
sync images(imageList)
Performs a synchronisation of the image executing sync
images with each of the images specified in imageList
imageList can be an array or a scalar
if (myimage < numimage) then
oldpic(1:nxlocal, nylocal+1) = oldpic(1:nxlocal, 1)[myimage+1]
end if
if (myimage > 1) then
oldpic(1:nxlocal, 0) = oldpic(1:nxlocal, nylocal)[myimage-1]
end if
! Now perform local pairwise synchronisations
if (myimage == 1 ) then
sync images( 2 )
else if (myimage == numimage) then
sync images( numimage-1 )
else
sync images( (/ myimage-1, myimage+1 /) )
end if
Other Intrinsic functions
lcobound(z)
Returns lower cobounds of the coarray z
lcobound(z,dim) returns lower cobound for
codimension dim of z
ucobound(z)
Returns upper cobounds of the coarray z
lcobound(z,dim) returns upper cobound for
codimension dim of z
real :: array(10)[4,0:*] on 16 images
lcobound(array) returns [ 1, 0 ]
ucobound(array) returns [ 4, 3 ]
10
14
Other Synchronisation
Critical sections
Limit execution of a piece of code to one image at a time
e.g. calculating global sum on master image
integer :: a(100)[*]
integer :: globalSum[*] = 0, localSum
... ! Initialise a on each image
localSum = SUM(a) !Find localSum of a on each image
critical
globalSum[1] = globalSum[1] + localSum
end critical
More on Cosubscripts
integer :: a[*] on 8 images
cosubscript a[9] is not valid
real :: b(10)[3,*] on 8 images
ucobound(b) returns [ 3, 3 ]
cosubscript b[2,3]
cosubscript b[3,3] is invalid (image 9)
Programmer needs to make sure that cosubscripts are valid
image_index returns 0 for invalid cosubscripts
11
15
Other Synchronisation
sync memory
Coarray data held in caches/registers made visible to all images
requires some other synchronisation to be useful
unlikely to be used in most coarray codes
Example usage: Mixing MPI and coarrays
loop: coarray operations
sync memory
call MPI_Allreduce(...)
sync memory implied for sync all and sync images
Assumed Size Coarrays
Codimensions can be remapped to corank greater than 1
useful for determining optimal extents at runtime
program 2d
real, codimension[*] :: picture(100,100)
integer :: numimage, numimagex, numimagey
numimage = num_images()
call get_best_2d_decomposition(numimage,&
numimagex, numimagey)
! Assume this ensures numimage=numimagex*numimagey
call dothework(picture, numimagex, numimagey)
...
contains
subroutine dothework(array, m, n)
real, codimension[m,*] :: array(100,100)
...
end subroutine dothework
12
38
16

I/O
Each image has its own set of input/output units
units are independent on each image
Default input unit is preconnected on image 1 only
,
Default output unit is available on all images
,
It is expected that the implementation will merge
records from each image into one stream
TS: Teams...
To define a set of images as a team
call form_team(oddteam,[ (i,i=1,n,2) ])
To synchronise the team
sync team(oddteam)
To determine images that constitute a team:
oddimages=team_images(oddteam)
17
21
Program Termination
STOP or END PROGRAM statements initiate normal
termination which includes a synchronisation step
normal termination
Other images can test for this using STAT= specifier to
synchronisation calls or allocate/deallocate
test for STAT_STOPPED_IMAGE (defined in
ISO_FORTRAN_ENV module)
The ERROR STOP statement initiates error
termination and it is expected all images will be
terminated.
TS: Collectives
Collective operations a key part of real codes
broadcast
global sum
...
Supported in other parallel models
OpenMP reductions
MPI_Allreduce
Not currently supported for coarrays
efficient implementation by hand is difficult
calling external MPI routines rather ugly
18
22
Coarray Technical Specification
Additional coarray features may be described in a
Technical Specification (TS)
Developed as part of the official ISO standards process
Work in progress and the areas of discussion are:
image teams
collective intrinsics for coarrays
atomics
TS: Collective intrinsic subroutines
Collectives, with in/out arguments, invoked by same statement
on all images (or team of images)
Routines
CO_BCAST
CO_SUM and other reduction operations
basically reproduce the MPI functionality
Arguments include SOURCE, RESULT, TEAM
Still discussion on need for implicit synchronisation and
argument types (for example non-coarray arguments)
19
23
TS: Teams
Often useful to consider subsets of processes
e.g. MPI communicators
TS: Atomic operations
Critical or lock synchronisation sometimes overkill
counter[1] = counter[1] + 1
Subsets not currently supported in Fortran, e.g.
sync all: all images
sync images: pairwise mutual synchronisation
Extension involves TEAMS of images
user creates teams
specified as an argument to various functions
Simple atomic operations can be optimised
e.g. OpenMP atomic
!$OMP atomic
sharedcounter = sharedcounter + 1
New variable types and operations for coarrays
20
39
24

TS: Atomic variables
Fortran already includes some atomic support (define,ref)
TS expands on this to supports atomic compare and swap,
integer (atomic_int_kind) :: counter[*]
call atomic_define(counter[1],0)
call atomic_add(counter[1],1)
call atomic_ref(countval,counter[1])
25
40

Implementations we have used
Experiences with
Coarrays
Parallel Programming with Fortran Coarrays
Delivered at PRACE Advanced Training Centre,
CSC IT Center for Science Ltd, Finland,
September 13, 2012
David Henty, Alan Simpson (EPCC)
Harvey Richardson, Bill Long (Cray
Cray X1/X2
Hardware supports communication by direct load/store
Very efficient with low overhead
Cray XT
PGAS (UPC,CAF) layered on GASNet/portals (so messaging)
Not that efficient
Cray XE
PGAS layered on DMAPP portable layer over Gemini
network hardware
Intermediate between XT and X1/2
Intel Composer XE 2011
SMP and message-passing runtimes
5
Overview
Implementations
Performance considerations
Where to use the coarray model
Coarray benchmark suite
Examples of coarrays in practice
References
Wrapup
Implementations we have used...
g95 on shared-memory
Using cloned process images on Linux
This is not being actively developed
2
6
Implementation Status
Intel XE on Ubuntu VM
History of coarrays dates back to Cray implementations
Expect support from vendors as part of Fortran 2008
G95 had multi-image support in 2010
has not been updated for some time
gfortran
Introduced single-image support at version 4.6
Intel: multi-process coarray support in Intel Composer XE 2011
(based on Fortran 2008 draft)
Runtimes are SMP, GASNet and compiler/vendor runtimes
GASNet has support for multiple environments
(IB, Myrinet, MPI, UDP and Cray/IBM systems) so
could be an option for new implementations
3
7
Implementation Status (Cray)
Cray has supported coarrays and UPC on various architectures
over the last decade (from T3E)
Full PGAS support on the Cray XT/XE
Cray Compiling Environment 7.0 Dec 2008
Current release is Cray Compiler Environment 7.4
Full Fortran 2008 coarray support
Full Fortran 2003 with some Fortran 2008 features
Fully integrated with the Cray software stack
Same compiler drivers, job launch tools, libraries
Integrated with Craypat Cray performance tools
Can mix MPI and coarrays
When to use coarrays
Two obvious contexts
Complete application using coarrays
Mixed with MPI
As an incremental addition to a (potentially large) serial code
As an incremental addition to an MPI code (allowing reuse of
most of the existing code)
Use coarrays for some of the communication
opportunity to express communication much more simply
opportunity to overlap communication
For subset synchronisation
Work-sharing schemes
4
41
8

Adding coarrays to existing applications
Constrain use of coarrays to part of application
Move relevant data into coarrays
Implement parallel part with coarray syntax
Move data back to original structures
Use coarray structures to contain pointers to existing data
Place relevant arrays in global scope (modules)
avoids multiple declarations
Declare existing arrays as coarrays at top level and through
the complete call tree
(some effort but only requires changes to declarations)
AllReduce (everyone gets, optimized)
All images get data from others simultaneously but this is
optimized so communication is more balanced
!...
sync all
vmax=v
do i=this_image()+1,num_images()
vmax=max(vmax,v[i])
end do
do i=1,this_image()-1
vmax=max(vmax,v[i])
end do
Have seen this much faster
9
13
Performance Considerations
What is the latency?
Do you need to avoid strided transfers?
Is the compiler optimising the communication for target
architecture?
Is it using blocking communication within a segment when
it does no need to?
Is it optimising strided communication?
Can it pattern-match loops to single communication
primitives or collectives?
Synchronization
For some algorithms (finitesync
all
but pairwise synchronization using sync images(image)
10
14
Performance: Communication patterns
Try to avoid creating traffic jams on the network, such as all
images storing to a single image.
Synchronization (one to many)
Often one image will be communicating with a set of images
In general not a good thing to do but assume we are...
The following examples show two ways to implement an
ALLReduce() function using coarrays
Tempting to use sync all
11
15
AllReduce (everyone gets)
All images get data from others simultaneously
function allreduce_max_allget(v) result(vmax)
double precision :: vmax, v[*]
integer i
sync all
vmax=v
do i=1,num_images()
vmax=max(vmax,v[i])
end do
Synchronisation (one to many)
If this is all images then could do
if ( this_image() == 1) then
sync images(*)
else
sync images(1)
end if
Note that sync all is likely to be fast so is an
alternative
12
42
16

Synchronisation (one to many)
For a subset use this
if ( this_image() == image_list(1)) then
sync images(image_list)
else
sync images(image_list(1))
end if
instead of sync images(image_list)
for all of them which is likely to be slower
Coarray Benchmark Suite
Developed by David Henty at EPCC
Aims to test fundamental features of a coarray implementation
We can test basic language syntax for communication of data
and synchronization
Need to choose communication pattern and data access
There is some scope for a given communication pattern:
array syntax, loops over array elements
inline code or use subroutines
Choices can reveal compiler capabilities
17
21
Collective Operations
If you need scalability to a large number of images you may
need to temporarily work around current lack of collectives
Use MPI for the collectives if MPI+coarrays is supported
Implement your own but this might be hard
For reductions of scalars a tree will be the best to try
For reductions of more data you would have to experiment and this may
depend on the topology
Coarrays can be good for collective operations where
there is an unusual communication pattern that does not
match what MPI collectives provide
there is opportunity to overlap communication
with computation
Initial Benchmark
Single contiguous point-to-point read and write
Multiple contiguous point-to-point read and write
Strided point-to-point read and write
All basic synchronization operations
Various representative communication patterns
Halo-swap in multi-dimensional regular domain decomposition
All communications include synchronisation cost
use double precision (8-byte) values as the basic type
use Fortran array syntax
Synchronisations overhead measured separately as
overhead = (delay + sync) delay
18
22
Tools: debugging and profiling
Tool support should improve once coarray takeup increases
Cray Craypat tool supports coarrays
Totalview works with coarray programs on Cray systems
Allinea DDT
support for coarrays and UPC for a number of compilers is
in public beta and will be in DDT 3.1
Scalasca
Currently investigating how PGAS support can be
incorporated.
Platforms
Limited compiler support at present
results presented from Cray systems
Cray Compiler Environment (CCE) 7.4.1
Cray XT6
MPP with dual 12-core Opteron Magny-Cours nodes and Cray
Seastar2+ torus interconnect.
Coarrays implemented using GASNET.
Cray XE6
Same nodes as XT6 but with Gemini torus interconnect which support
RDMA
19
23
Debugging Synchronisation problems
One-sided model is tricky because subtle synchronisation
errors change data
TRY TO GET IT RIGHT FIRST TIME
look carefully at the remote operations in the code
Think about synchronisation of segments
especially look for early arriving communications trashing
your data at the start of loops (this one is easy to miss)
One way to test is to put sleep() calls in the code
Delay one or more images
Delay master image, or other images for some patterns
Pingpong
8000
6000
4000
2000
XE6 put
XT6 put
XT6 MPI
XE6 MPI
Point-to-point comms
0
1 10 100 1000 10000 100000 1000000 10000000
message length (doubles)
20
24

Pingpong (small message regime)
80
XE6 put
XT6 put
XE6 MPI
60
XT6 MPI
40
Point-to-point comms
3D Halo Swap on XE6 (weak scaling V=50^3)
150
put p2p
put all
get p2p
get all
100
20
50
0
1 4 16 64 256 1024 4096
message length (doubles)
sync images put latency MPI latency
XT6 33.1 45.3 7.4
XE6 3.0 3.7 1.6
25
0
8 32 128 512 2048 8192 32768
images
29
Global Synchronisation
3000
sync all
MPI Barrier * 100
2000
XT6 Synchronisation
Examples of coarrays in practice
Puzzles
Distributed Remote Gather
HIMENO Halo-Swap
Gyrokinetic Fusion Code
1000
0
16 32 64 128 256 512 1024 2048
images
XT coarray implementation not keeping up with MPI
26
30
Global Synchronisation
50
XE6 Synchronisation
Solving Sudoku Puzzles
XE6 sync all
40
XE6 MPI_Barrier
30
20
10
0
4 16 64 256 1024 4096 16384
images
Much faster than the previous XT results
27
31
Global Synchronisation
Also measured sync images
various point-to-point patterns
Observed that sync images is usually faster than
sync all on more than 512 images
Going Parallel
Started with serial code
Changed to read in all 125,000 puzzles at start
Choose work-sharing strategy
One image (1) holds a queue of puzzles to solve
Each image picks work from the queue and writes result
back to queue
Arbitrarily decide to parcel work as
blocksize = npuzzles /( 8* num_images() )
28
32

Data Structures
use,intrinsic iso_fortran_env
type puzzle
integer :: input(9,9)
integer :: solution(9,9)
end type puzzle
type queue
type (lock_type) :: lock
integer :: next_available = 1
type(puzzle),allocatable :: puzzles(:)
end type queue
type(queue),save :: workqueue[*]
type(puzzle) :: local_puzzle
integer,save :: npuzzles[*],blocksize[*]
Solve the puzzles and write back
! Solve those puzzles
do i = istart,iend
local_puzzle%input = &
& workqueue[1]%puzzles(i)%input
call sudoku_solve &
& (local_puzzle%input,local_puzzle%solution)
workqueue[1]%puzzles(i)%solution = &
& local_puzzle%solution
end do
33
37
Input
if (this_image() == 1) then
! After file Setup.
inquire (unit=inunit,size=nbytes)
nrecords = nbytes/10
npuzzles = nrecords/9
blocksize = npuzzles / (num_images()*8)
write (*,*) "Found ", npuzzles
allocate (workqueue%puzzles(npuzzles))
do i = 1, npuzzles
call read_puzzles( &
& workqueue%puzzles(i)%input,inunit, &
& error)
end do
Output the solutions
! Need to synchronize puzzle output updates
sync all
if (this_image() == 1) then
open (outunit,file=outfile,iostat=error)
do i = 1, npuzzles
call write_puzzle &
& (workqueue%puzzles(i)%input, &
& workqueue%puzzles(i)%solution,outunit,error)
end do
close(inunit)
34
38
Core program structure
! After coarray data loaded
sync all
blocksize = blocksize[1]
npuzzles = npuzzles[1]
done = .false.
workloop: do
! Acquire lock and claim work
! Solve our puzzles
end do workloop
More on the Locking
We protected access to the queue state by lock and unlock
During this time no other image can acquire the lock
We need to have discipline to only access data within the
window when we have the lock
There is no connection with the lock variable and the other
elements of the queue structure
The unlock is acting like sync memory
If one image executes an unlock...
Another image getting the lock is ordered after the
first image
35
39
Acquire lock and claim work
! Reserve the next block of puzzles
lock (workqueue[1]%lock)
next = workqueue[1]%next_available
if (next

Distributed remote gather
The problem is how to implement the following gather loop on
a distributed memory system
REAL :: table(n), buffer(nelts)
INTEGER :: index(nelts) ! nelts

References
http://lacsi.rice.edu/software/caf/downloads/documentation/
nrRAL98060.pdf- Co-array Fortran for parallel programming,
Numrich and Reid, 1998
ftp://ftp.nag.co.uk/sc22wg5/N1801-N1850/N1824.pdf
Coarrays
Ashby, J.V. and Reid, J.K (2008). Migrating a scientific
application from MPI to coarrays. CUG 2008 Proceedings. RAL-
TR-2008-015
See http://www.numerical.rl.ac.uk/reports/reports.shtml
http://upc.gwu.edu/ - Unified Parallel C at George Washington
University
http://upc.lbl.gov/ - Berkeley Unified Parallel C Project
49
Tutorial Wrapup
Remember our first Motivation slide?
Fortran now supports parallelism as a full first-class feature of
the language
Changes are minimal
Performance is maintained
Flexibility in expressing communication patterns
We hope you learned something and have success
with coarrays in the future
50
Acknowledgements
The material for this tutorial is based on original content
developed by EPCC of The University of Edinburgh for use in
teaching their MSc in High-Performance Computing.
The following people contributed to its development:
Alan Simpson, Michele Weiland, Jim Enright and Paul Graham
The material was subsequently developed by EPCC and Cray to
form this tutorial with contributions from the following people:
David Henty, Alan Simpson ( EPCC)
Harvey Richardson, Bill Long, Roberto Ansaloni,
Jef Dawson, Nathan Wichmann (Cray)
This material is Copyright © 2011
by The University of Edinburgh and Cray Inc.
47

EXERCISE ASSIGNMENTS
49

Practicalities
Computing servers
We will use CSC’s Cray supercomputer Louhi for the exercises. Log onto Louhi using the provided
tnrgXX username and password, e.g.
% ssh –X trng10@louhi.csc.fi
Alteratively, feel free to use the local workstations or your own Linux/Mac laptop and GNU
compiler, but for the Co-Array Fortran features we will need to use the Cray Fortran compiler. To
enable the Cray compiler, we need to do (once on Louhi)
% module swap PrgEnv-pgi PrgEnv-cray
Other compilers (GNU, PGI, Pathscale, and Intel) are also available in CSC’s computing servers.
The compiler can be changed via (for example)
% module swap PrgEnv-cray PrgEnv-gnu
For editing Fortran program files you can use e.g. Emacs editor with or without (the option –nw)
X-Windows:
emacs –nw prog.f90
emacs prog.f90
Also other popular editors (vim, nano) are available.
Simple compilation
Example program test.f90
program test
implicit none
integer, parameter :: result = 42
print *, ‘hello world!’
print *, ‘the result is’, result
end program test
Compilation and execution are done via the ftn wrapper and the aprun scheduler:
% ftn test.f90 -o test
% aprun –n 1 ./test
Hello world!
The result is 42
50

Fortran 95/2003 exercises
1. Playing around with control structures and arrays
a. Declare an integer array of dimensions 100 by 100 and initialize it to zero otherwise but
where the first index is equal to 50 or the second index is equal to 50 the array elements get
a value of 1.
Value 1 on the black rows, value 0
elsewhere
b. Then initialize another array with the same dimensions, but its values are computed from the
first array such that
• The cells with exactly two elements equal to 1 in the first array get the same value in the
new array as in the first array.
• Any cell with exactly three neighbors with value 1 gets the value 1 in the new array.
• Otherwise, i.e. new array cells with less than two or more than three neighbors with
value 1 in the old array get a value 0.
At this stage, just run the indices from 2 to 99, i.e. not referencing to the boundaries at all.
c. Modify the program from exercise 1b such that the array becomes periodic – that is, the
boundaries depend on the cells on the other side of the array. The solutions for all three
items of Exercise 1 is provided in ex1_arrays.f90.
2. Getting acquainted with procedures
a. Modify the program such that you can produce new arrays iteratively, i.e. taking the array
from the previous iteration and obtaining a new array by applying the same rules for it.
b. Modify the exercise 2a so that it uses a function or a subroutine to produce the new array.
c. Change the initialization of the board such that the board starts from a random
configuration. The intrinsic procedure is calledRANDOM_NUMBER. Wrap also this board
initialization into its own procedure. The solutions for all three items of Exercise 2 is provided
in ex2_procedures.f90.
51

Fortran 95/2003 exercises
3. Game of Life
The Game of Life (GoL) is a cellular automaton devised by John Horton Conway in 1970, see
http://en.wikipedia.org/wiki/Conway's_Game_of_Life.
You can compile a reference executable (since the file ex3_gol.f90 will contain the solution)
with
% ftn –o gol gol_io.f90 ex3_gol.f90
Run the program of e.g. 200x200 board for 100 iterations. With the command xview or
eog you can view the images (.pbm) and see how the automaton looks like after those. You
can also animate the board development by first using convert as
% convert -delay 40 -geometry 512x512 life_*.pbm life.gif
(on a single line) and then displaying the animation with
% animate life.gif
a. See the file ex3_gol0.f90, get acquainted with the program and complete the missing parts
of the code (search: “TODO”). Refer back to assignments 1 and 2.
b. Experiment, how the game evolves if you replace the starting pattern (‘plus’, c.f. exercise 1a)
to a random one (c.f. Exercise 2c).
c. Modify the GoL program such that the board is manipulated through a derived datatype
GoL_board, which contains the actual board, its dimensions as well as how many iterations
it has gone through. No solution has been prepared.
d. Now we will examine the I/O module of the Game of Life program. It visualizes the board in
the netpbm image format, see http://en.wikipedia.org/wiki/Netpbm_format. Implement the
writing of the board as pbm images - or in some other image format if you want to go your
own way. Consult the gol_io.f90 when in trouble. Shortcut: study the draw subroutine in
gol_io.f90 and make sure you understand the piece of code.
e. Modify the program such that the user input is read directly from the command line instead
of parsering, i.e. the program is launched as ./gol (# iterations) (board height) (board width),
for example
% ./gol 200 100 100
The answer is provided in gol_io.f90.
52

More bonus exercises
Fortran Quiz
a. Are the following Fortran statements written correctly?
character_string = ’Awake in the morning,
& asleep in the evening.’
x = 0.4-6
answer = ’True & false’
low-limit = 0.0005E10
y = E6
b. Are the following declarations legimate in Fortran?
DOUBLE :: x
CHARACTER(LEN=*), PARAMETER :: "Name"
REAL :: pi = 22/7
REAL :: x = 2., y = -3
REAL :: pii = 22.0/7.0
REAL x = 1.0
c. What are the iteration counts of the following DO loops, the values of loop variable i inside
the loop, and the value of the loop variable after the DO construct?
DO i = 1, 5
DO i = 5, 0, -1
DO i = 10, 1, -2
DO i = 0, 30, 7
DO i = 3, 2, 1
Derived types
a. Declare the derived type which can save the birth date in the form:
21 01 1990
This derived type thus contains three integers, which have different KIND values:
SELECTED_INT_KIND(2) and SELECTED_INT_KIND(4).
b. Add the field the for a name to the derived type. Write a function, which returns the name
and date in a character string in the following form
Charlie Brown (01.01.1999)
Recursion
Write a recursive function, which calculates ”Tribonacci numbers”:
Calculate x 12 . Carry out the computation also using a loop structure.
53

Edge detection and picture reconstruction
Parallel Programming
with Fortran Coarrays:
Overview of Exercises
Delivered at PRACE Advanced Training Centre,
CSC IT Center for Science Ltd, Finland,
September 13, 2012
single pass
hundreds
of iterations
David Henty, Alan Simpson (EPCC)
Harvey Richardson, Bill Long (Cray)
5
Exercise 1
Hello world example
check you can log on, compile, submit and run
Exercise 3 (unlikely to get here today!)
Decompose picture across a 2D grid of images
using multiple codimensions
Writing arrays as pictures
declare and manipulate coarrays
write out arrays in PGM picture format
view them using display from ImageMagick
use both remote reads and remote writes
2
Sample output on 4 images
Documentation
Full instructions in exercise notes
PDF copy in doc/ subdirectory
Go at your own pace
no direct dependencies between practicals & lectures
each exercise follows on from the last
questions then please ask us!
3
Exercise 2
Perform simple edge detection of features in a picture
halo communication between 1D grid of images
Reconstruct picture from supplied edges
an iterative algorithm
computationally intensive so worth parallelising
Terminate based on some stopping criterion
requires global sums
Use global or point-to-point synchronisation
Look at scalability
54