0
Discuss the differences and similarities between MPI and PVM Discuss the benefits and?
MPI
(Message
P
assing
In
terface)
is
sp
eciØcation
for
message-passing
libraries
that
can
b
e
used
for
writing
p
ortable
parallel
programs.
What
do
es
MPI
do?
When
w
e
sp
eak
ab
out
parallel
programming
using
MPI,
w
e
imply
that:
≤
A
Øxed
set
of
pro
cesses
is
created
at
program
initialization,
one
pro
cess
is
created
p
er
pro
cessor
≤
Eac
h
pro
cess
kno
ws
its
p
ersonal
n
um
b
er
≤
Eac
h
pro
cess
kno
ws
n
um
b
er
of
all
pro
cesses
≤
Eac
h
pro
cess
can
comm
unicate
with
other
pro
cesses
≤
Pro
cess
can't
create
new
pro
cesses
(in
MPI{1),
the
group
of
pro
cesses
is
static
What
is
PVM?
PVM
(P
arallel
Virtual
Mac
hine)
is
a
soft
w
are
pac
k
age
that
allo
ws
a
heterogeneous
collection
of
w
orkstations
(host
p
o
ol)
to
function
as
a
single
high
p
erformance
parallel
virtual
mac
hine.
PVM,
through
its
virtual
mac
hine,
pro
vides
a
simple
y
et
useful
distributed
op
erating
system.
It
has
daemon
running
on
all
computers
making
up
the
virtual
mac
hine.
PVM
daemon
(p
vmd)
is
UNIX
pro
cess,
whic
h
o
v
ersees
the
op
eration
of
user
pro
cesses
within
a
PVM
application
and
co
ordinates
in
ter-mac
hine
PVM
comm
unications.
Suc
h
p
vmd
serv
es
as
a
message
router
and
con
troller.
One
p
vmd
runs
on
eac
h
host
of
a
virtual
mac
hine,
the
Ørst
p
vmd,
whic
h
is
started
b
y
hand,
is
designated
the
master,
while
the
others,
started
b
y
the
master,
are
called
sla
v
es.
It
means,
that
in
con
trast
to
MPI,
where
master
and
sla
v
es
start
sim
ultaneously
,
in
PVM
master
m
ust
b
e
started
on
our
lo
cal
mac
hine
and
then
it
automatically
starts
daemons
on
all
other
mac
hines.
In
PVM
only
the
master
can
start
new
sla
v
es
and
add
them
to
conØguration
7
or
delete
sla
v
e
hosts
from
the
mac
hine.
Eac
h
daemon
main
tains
a
table
of
conØguration
and
handles
information
relativ
e
to
our
parallel
virtual
mac
hine.
Pro
cesses
comm
unicate
with
eac
h
other
through
the
daemons:
they
talk
to
their
lo
cal
daemon
via
the
library
in
terface
routines,
and
lo
cal
daemon
then
sends/receiv
es
messages
to/from
remote
host
daemons.
General
idea
of
using
MPI
and
PVM
is
the
follo
wing:
The
user
writes
his
application
as
a
collection
of
co
op
erating
pro
cesses
(tasks),
that
can
b
e
p
er-
formed
indep
enden
tly
in
diÆeren
t
pro
cessors.
Pro
cesses
access
PVM/MPI
resources
through
a
library
of
standard
in
terface
routines.
These
routines
allo
w
the
initiation
and
termination
of
pro-
cesses
across
the
net
w
ork
as
w
ell
as
comm
unication
b
et
w
een
pro
cesses.
3.3
What
is
not
diÆeren
t?
Despite
their
diÆerences,
PVM
and
MPI
certainly
ha
v
e
features
in
common.
In
this
section
w
e
review
some
of
the
similarities.
3.3.1
P
ortabilit
y
Both
PVM
and
MPI
are
p
ortable;
the
sp
eciØcation
of
eac
h
is
mac
hine
indep
enden
t,
and
im-
plemen
tations
are
a
v
ailable
for
a
wide
v
ariet
y
of
mac
hines.
P
ortabilit
y
means,
that
source
co
de
written
for
one
arc
hitecture
can
b
e
copied
to
a
second
arc
hitecture,
compiled
and
executed
without
mo
diØcation.
3.3.2
MPMD
Both
MPI
and
PVM
p
ermit
diÆeren
t
pro
cesses
of
a
parallel
program
to
execute
diÆeren
t
exe-
cutable
binary
Øles
(This
w
ould
b
e
required
in
a
heterogeneous
implemen
tation,
in
an
y
case).
That
is,
b
oth
PVM
and
MPI
supp
ort
MPMD
programs
as
w
ell
as
SPMD
programs,
although
again
some
implemen
tation
ma
y
not
do
so
(MPICH,
LAM
{
supp
ort).
3.3.3
In
terop
erabilit
y
The
next
issue
is
in
terop
erabilit
y
{
the
abilit
y
of
diÆeren
t
implemen
tations
of
the
same
sp
eciØ-
cation
to
exc
hange
messages.
F
or
b
oth
PVM
and
MPI,
v
ersions
of
the
same
implemen
tation
(Oak
Ridge
PVM,
MPICH,
or
LAM)
are
in
terop
erable.
3.3.4
Heterogeneit
y
The
next
imp
ortan
t
p
oin
t
is
supp
ort
for
heterogeneit
y
.
When
w
e
wish
to
exploit
a
collection
of
net
w
ork
ed
computers,
w
e
ma
y
ha
v
e
to
con
tend
with
sev
eral
diÆeren
t
t
yp
es
of
heterogeneit
y
[GBD
+
94]:
≤
arc
hitecture
The
set
of
computers
a
v
ailable
can
include
a
wide
range
of
arc
hitecture
t
yp
es
suc
h
as
PC
class
mac
hines,
high-p
erformance
w
orkstations,
shared-memory
m
ultipro
cessors,
v
ector
sup
ercom-
puters,
and
ev
en
large
MPPs.
Eac
h
arc
hitecture
t
yp
e
has
its
o
wn
optimal
programming
metho
d.
Ev
en
when
the
arc
hitectures
are
only
serial
w
orkstations,
there
is
still
the
prob-
lem
of
incompatible
binary
formats
and
the
need
to
compile
a
parallel
task
on
eac
h
diÆeren
t
mac
hine.
8
≤
data
format
Data
formats
on
diÆeren
t
computers
are
often
incompatible.
This
incompatibilit
y
is
an
imp
or-
tan
t
p
oin
t
in
distributed
computing
b
ecause
data
sen
t
from
one
computer
ma
y
b
e
unreadable
on
the
receiving
computer.
Message
passing
pac
k
ages
dev
elop
ed
for
heterogeneous
en
viron-
men
ts
m
ust
mak
e
sure
all
the
computers
understand
the
exc
hanged
data;
they
m
ust
include
enough
information
in
the
message
to
enco
de
or
deco
de
it
for
an
y
other
computer.
≤
computational
sp
eed
Ev
en
if
the
set
of
computers
are
all
w
orkstations
with
the
same
data
format,
there
is
still
heterogeneit
y
due
to
diÆeren
t
computational
sp
eeds.
The
problem
of
computational
sp
eeds
can
b
e
v
ery
subtle.
The
programmer
m
ust
b
e
careful
that
one
w
orkstation
do
esn't
sit
idle
w
aiting
for
the
next
data
from
the
other
w
orkstation
b
efore
con
tin
uing.
≤
mac
hine
load
Our
cluster
can
b
e
comp
osed
of
a
set
of
iden
tical
w
orkstations.
But
since
net
w
ork
ed
com-
puters
can
ha
v
e
sev
eral
other
users
on
them
running
a
v
ariet
y
of
jobs,
the
mac
hine
load
can
v
ary
dramatically
.
The
result
is
that
the
eÆectiv
e
computational
p
o
w
er
across
iden
tical
w
orkstations
can
v
ary
b
y
an
order
of
magnitude.
≤
net
w
ork
load
Lik
e
mac
hine
load,
the
time
it
tak
es
to
send
a
message
o
v
er
the
net
w
ork
can
v
ary
dep
ending
on
the
net
w
ork
load
imp
osed
b
y
all
the
other
net
w
ork
users,
who
ma
y
not
ev
en
b
e
using
an
y
of
the
computers
in
v
olv
ed
in
our
computation.
This
sending
time
b
ecomes
imp
ortan
t
when
a
task
is
sitting
idle
w
aiting
for
a
message,
and
it
is
ev
en
more
imp
ortan
t
when
the
parallel
algorithm
is
sensitiv
e
to
message
arriv
al
time.
Th
us,
in
distributed
computing,
heterogeneit
y
can
app
ear
dynamically
in
ev
en
simple
setups.
Both
PVM
and
MPI
pro
vide
supp
ort
for
heterogeneit
y
.
As
for
MPI,
diÆeren
t
datat
yp
es
can
b
e
encapsulated
in
a
single
deriv
ed
t
yp
e,
thereb
y
allo
wing
comm
unication
of
heterogeneous
messages.
In
addition,
data
can
b
e
sen
t
from
one
arc
hitecture
to
another
with
data
con
v
ersion
in
heterogeneous
net
w
orks
(big-endian,
little-endian).
Although
MPI
sp
eciØcation
is
designed
to
encourage
heterogeneous
implemen
tation,
some
implemen
tations
of
MPI
ma
y
not
b
e
used
in
a
heterogeneous
en
vironmen
t.
Both
the
MPICH
and
LAM
are
implemen
tations
of
MPI,
whic
h
supp
ort
heterogeneous
en
vironmen
ts.
The
PVM
system
supp
orts
heterogeneit
y
in
terms
of
mac
hines,
net
w
orks,
and
applications.
With
regard
to
message
passing,
PVM
p
ermits
messages
con
taining
more
than
one
datat
yp
e
to
b
e
exc
hanged
b
et
w
een
mac
hines
ha
ving
diÆeren
t
data
represen
tations.
In
summary
,
b
oth
PVM
and
MPI
are
systems
designed
to
pro
vide
users
with
libraries
for
writing
p
ortable,
heterogeneous,
MPMD
programs.
3.4
DiÆerences
PVM
is
built
around
the
concept
of
a
virtual
mac
hine
whic
h
is
a
dynamic
collection
of
(p
oten-
tially
heterogeneous)
computational
resources
managed
as
a
single
parallel
computer.
The
virtual
mac
hine
concept
is
fundamen
tal
to
the
PVM
p
ersp
ectiv
e
and
pro
vides
the
basis
for
heterogeneit
y
,
p
ortabilit
y
,
and
encapsulation
of
function
that
constitute
PVM.
In
con
trast,
MPI
has
fo
cused
on
message-passing
and
explicitly
states
that
resource
managemen
t
and
the
concept
of
a
virtual
mac
hine
are
outside
the
scop
e
of
the
MPI
(1
and
2)
standard
[GKP96
].
9
3.4.1
Pro
cess
Con
trol
Pro
cess
con
trol
refers
to
the
abilit
y
to
start
and
stop
tasks,
to
Ønd
out
whic
h
tasks
are
running,
and
p
ossibly
where
they
are
running.
PVM
con
tains
all
of
these
capabilities
{
it
can
spa
wn/kill
tasks
dynamically
.
In
con
trast
MPI
{1
has
no
deØned
metho
d
to
start
new
task.
MPI{2
con
tains
functions
to
start
a
group
of
tasks
and
to
send
a
kill
signal
to
a
group
of
tasks
[NS02].
3.4.2
Resource
Con
trol
In
terms
of
resource
managemen
t,
PVM
is
inheren
tly
dynamic
in
nature.
Computing
resources
or
"hosts"
can
b
e
added
and
deleted
at
will,
either
from
a
system
"console"
or
ev
en
from
within
the
user's
application.
Allo
wing
applications
to
in
teract
with
and
manipulate
their
computing
en
vironmen
t
pro
vides
a
p
o
w
erful
paradigm
for
≤
load
balancing
|
when
w
e
w
an
t
to
reduce
idle
time
for
eac
h
mac
hine
in
v
olv
ed
in
computation
≤
task
migration
|
user
can
request
that
certain
tasks
execute
on
mac
hines
with
particular
data
formats,
arc
hitectures,
or
ev
en
on
an
explicitly
named
mac
hine
≤
fault
tolerance
Another
asp
ect
of
virtual
mac
hine
dynamics
relates
to
e±ciency
.
User
applications
can
exhibit
p
oten
tially
c
hanging
computational
needs
o
v
er
the
course
of
their
execution.
F
or
example,
con-
sider
a
t
ypical
application
whic
h
b
egins
and
ends
with
primarily
serial
computations,
but
con
tains
sev
eral
phases
of
hea
vy
parallel
computation.
PVM
pro
vides
∞exible
con
trol
o
v
er
the
amoun
t
of
computational
p
o
w
er
b
eing
utilized.
Additional
hosts
can
b
e
added
just
for
those
p
ortions
when
w
e
need
them.
MPI
lac
ks
suc
h
dynamics
and
is,
in
fact,
sp
eciØcally
designed
to
b
e
static
in
nature
to
impro
v
e
p
erformance.
Because
all
MPI
tasks
are
alw
a
ys
presen
t,
there
is
no
need
for
an
y
time-consuming
lo
okups
for
group
mem
b
ership.
Eac
h
task
already
kno
ws
ab
out
ev
ery
other
task,
and
all
com-
m
unications
can
b
e
made
without
the
explicit
need
for
a
sp
ecial
daemon.
Because
all
p
oten
tial
comm
unication
paths
are
kno
wn
at
startup,
messages
can
also,
where
p
ossible,
b
e
directly
routed
o
v
er
custom
task-to-task
c
hannels.
3.4.3
Virtual
T
op
ology
On
the
other
hand,
although
MPI
do
es
not
ha
v
e
a
concept
of
a
virtual
mac
hine,
MPI
do
es
pro
vide
a
higher
lev
el
of
abstraction
on
top
of
the
computing
resources
in
terms
of
the
message-
passing
top
ology
.
In
MPI
a
group
of
tasks
can
b
e
arranged
in
a
sp
eciØc
logical
in
terconnection
top
ology
[NS02,
F
or94]
.
A
virtual
top
ology
is
a
mec
hanism
for
naming
the
pro
cesses
in
a
group
in
a
w
a
y
that
Øts
the
comm
unication
pattern
b
etter.
The
main
aim
of
this
is
to
mak
e
subsequen
t
co
de
simpler.
It
ma
y
also
pro
vide
hin
ts
to
the
run-time
system
whic
h
allo
w
it
to
optimize
the
comm
unication
or
ev
en
hin
t
to
the
loader
ho
w
to
conØgure
the
pro
cesses.
F
or
example,
if
our
pro
cesses
will
comm
unicate
mainly
with
nearest
neigh
b
ours
after
the
fashion
of
a
t
w
o-dimensional
grid
(see
Figure
3),
w
e
could
create
a
virtual
top
ology
to
re∞ect
this
fact.
What
w
e
gain
from
this
creation
is
access
to
con
v
enien
t
routines
whic
h,
for
example,
compute
the
rank
of
an
y
pro
cess
giv
en
its
co
ordinates
in
the
grid,
taking
prop
er
accoun
t
of
b
oundary
conditions.
In
particular,
there
are
routines
to
compute
the
ranks
of
our
nearest
neigh
b
ours.
The
rank
can
then
b
e
used
as
an
argumen
t
to
message{passing
op
erations.
10
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what are the similarties and differences between the tudors and the Aztecs
Discuss the similarities and differences between Egyptian and other African cultures in history and art?
Egypt was one of the first and oldest civilizations to develop. They developed along with Mesopotamia. The differences were in social culture, architecture, and art.
