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From YouTube: Introduction to Perlmutter System
Description
Jay Srinivasan (NERSC)
Introduction to Perlmutter System
A
Thank
you
Jack,
and
thank
you
to
Neil
and
Helen,
and
all
the
other
organizers
for
putting
this
together,
I
think
it's
yeah.
It's
it's
very
interesting
to
to
see
the
progression
of
this.
You
know
we
we
sort
of
put
together
the
idea
for
Paul
Miller
back
in
in
2015.
and
the
the
sort
of
landscape
of
GP
of
nurse.
A
So
at
a
very
high
level,
like
Jack
said
it
is
a
system,
that's
been
optimized
for
for
science
right
and
so
by
that
we
need.
We
don't
just
have
one
kind
of
thing
on
the
system.
We
have
in
fact
many
different
things
and
we
we
make
sure
that
they
try
to
work
well
together
right,
so
we
have
CPU
only
nodes
and
we
have
GPU
accelerated
nodes,
which
I'm
sure
are
very
interesting
to
all
of
you.
We
have
an
all
flash
high
performance,
all
flash
file
system
and
then
from
the
user.
A
You
know
the
first
things
that
the
user
sees
on
on
the
system
or
even
getting
used
to
the
system
or-
or
you
know
things
like-
either
the
login
nodes
or
or
nodes
that
aren't
necessarily
optimized
for
for,
for
you
know,
high
performance
compute,
but
provide
the
basic
foundational
blocks
to
allow
you
to
do
to
compose
your
workflows.
To
do.
A
But
then,
in
addition
to
sort
of
bring
together
the
nurse
environment,
we
we
sort
of
make
sure
that
you
know
external
file
systems
and
networks
can
connect
well
and
have
a
good
path
into
the
system
right
and
so
a
little
bit
more
detail
on
this,
which
I
think
hopefully
should
be
familiar
to
people
who've.
Actually,
you
know
been
on
the
system
now.
A
A
So
slingshot
is
the
high
bandwidth
low
latency
network,
but
hpe
has
put
together
for
this
system,
and
that
is
in
fact
you
know
what
allows
promoter
to
be
and
I'll
show
you
the
differences
from
Quarry,
but
all
of
this
is
under
one
network
for
for
this
system
right.
In
addition,
you
know,
there's
not
a
lot
of
point
in
having
a
system
that
nobody
can
sort
of
get
to
and
do
interesting
things
on.
A
So
we
have
a
very
resilient
and
high
band
with
the
link
to
the
nearest
Network
and
the
world
right.
So
this
this
these
this
picture
here
on
the
lower
left,
shows
you
that
you
know
we
have
we're
promoter
all
of
this
slingshot
Network,
and
then
we
have
a
very
AI
bandwidth
connection
to
the
edge
of
from
under
and
that's
resilient,
and
so
this
is
multi-terabits
per
second
and
then
from
The
Edge
router.
A
A
In
addition
to
actually
drive
the
node
since
the
gpus
are
not
yet
able
to
do
that,
we
have
an
AMD
Milan
to
do
that,
we
also
have
on
the
GPU
nodes
dram
right,
so
we
have
256
gigabytes
of
demamp,
and
you
can
actually
see
that
here
these
between
these
copper
fins
and
then
because
we
have
four
gpus.
We
have
four
slingshot
Nicks
per
node
and
then
on
the
CPU
only
nodes.
A
A
Let
me
just
take
a
minute
here
and
talk
about
the
the
features
Shasta
is
the
system
or
management
framework
stack
that
we
have
on
the
nodes,
and
so
this
is
a
sort
of
a
typical
picture
here
you
can
see,
but
there
are
some
differences
to
standard
sort
of
large
clusters
or
large
supercomputers.
We
have
a
whole
bunch
of
non-compute
nodes
on
the
left,
and
then
we
have
all
the
compute
nodes
on
the
right.
The
non-compute
nodes
in
this
case
are
in
fact
managed
with
this
Cloud
managed
infrastructure
right.
A
So
this
is
very
similar
to
what
the
large
very
large
Cloud
providers
use,
and
there
are
some
certain
benefits
to
to
doing
it
at
this
scale
at
the
scale
we
have
as
well.
It
gives
you
sort
of
the
service
oriented
architecture
that
allows
us
to
use
a
lot
of
the
new
developments
and
sort
of
system,
management,
capabilities,
and
so
on
that
are
that
are
out
there
to
manage
the
system.
The
compute
nodes
themselves
are
about
off
of
bare
metal,
so
there
aren't
any
directly
user
accessible.
A
You
know,
Cloud
managed
so
Cloud
oriented
features
on
there,
but
there
is
a
bunch
of
sort
of
value
that
we
can
leverage
from
this
Cloud
thing
right.
So
we
can.
We
have
a.
We
have
a
system-wide
API
that
users
can
get
access
to
to
help
control
some
aspects
of
of
their
workflows
and
and
then
it
all
of
the
services
themselves
are
resilient
when
we,
when
we
put
them
on
this
on
this
framework
here
right,
so
both
so
at
a
very
high
level.
A
So
in
terms
of
differences
from
Corey
I
mentioned
I,
think
people
are
familiar
with
Corey
again.
The
main
thing
is
all
of
promoter,
which
is
this
middle
tank
here
on
the
left
on
this
slide
is
control
is
under
one
slingshot,
Network
right
so,
but
as
as
compared
to
Corey,
where
you
had
you
know,
you
have
the
two
partitions,
the
Haswell
and
the
p
l
partitions,
those
along
with
the
associated
service
nodes
and
the
you
know
the
booting
nodes
and
as
well
as
the
nodes
that
help
you
get.
A
Access
to
the
file
system
are
all
part
of
the
Aries
network,
but
the
access
nodes,
as
well
as
the
the
storage
itself,
is
on
a
different
network.
So
there's
a
network
translation
happening
at
some
level
for
for
Quarry
and
between,
say
the
the
K
L's
and
the
I
o
nodes
or
kennels,
and
the
paths
to
the
outside
world,
whereas
in
slingshot
everything
is
on
one
network,
and
you
can
see
this
picture
here
on
the
far
right.
That
shows
you.
A
Those
elect
those
network
connections
that
are
there
between
what
we
call
each
each
group
so
largely
speaking.
Each
one
of
these
little
dots
is
is
a
cabinet
on
the
compute
side
and
to
First
approximation
and
then
on
the
rest
of
the
cabinets
and
all
the
service
cabinets
and
so
on,
and
you
can
see
the
network
connections
that
go
between
them,
as
well
as
the
connections
to
the
outside
world.
So
the
sort
of
deceptively
small
connection
here
is,
in
fact
our
our
high
bandwidth
connection
to
to
the
rest
of
nurse.
A
So
in
terms
of
the
software
I
think
you
know,
you'll
you'll
hear
more
about
this
to
sort
of
proxies,
as
proxies
and
the
talks
that
you're
getting.
But
you
know
we
have
a
very
rich
and
robust
programming
environment
as
well
as
support
for
various
programming
models
and
languages,
and
it
won't
go
into
great
detail.
A
But
it's
just
to
note
that
you
know
there's
a
very
good
coverage
of
of
things
that
are
both
formally
supported
by
the
vendor,
but
also
sort
of
nurse
supported
in
the
sense
that
their
staff-
and
this
is
the
the
team
that
you
know,
Jack
and
and
other
groups.
Rebecca
has
group
and
others
all
are
part
of.
Are
you
know
strongly
supporting
all
of
these
programming
environments
that
we
have
on
the
system.
That'll
help
make
the
system
very
productive
as
a
science
teaser.
A
That's
happening
back
doctor
a
little
bit,
ranging
from
so
we've
sort
of
had
these
three
pillars
that
we
tried
to
support
on
promoter.
You
know
traditional
simulation
data
and
machine
learning,
and
so
we've
had
successes
in
all
three
of
these
pillars
at
the
beginning
of
this
rollout
of
permiter-
and
you
can
see
some
of
the
examples
here
on
on
this
slide
and
then,
finally,
in
terms
of
the
future
monster
I'm
out
of
time.
A
So
you
know,
promoter
is,
is
being
just
being
introduced
right,
so
it'll
have
a
long
and
story
life
ahead
of
it,
but
we're
not
sort
of
just
statically
going
to
keep
the
system
here.
There
are
a
whole
bunch
of
improvements
that
systems
folks,
as
well
as
the
the
user
integration
Fork
they're
doing
to
this
system
to
help
make
it
better.
Every
single
you
know
year,
and
so
we
can
sort
of
bucket
those
into
three
large
buckets.
A
One
is
sort
of
all
operational
improvements
that
help
us
keep
the
system
up
and
running
and
without
the
least
amount
of
impact,
and
hopefully
to
give
you
the
most
important
productivity
in
terms
of
the
user
environment.
We're
going
to
be
able
to
start
giving
access
to
container-based
environments
that
allow
users
a
lot
more
control
over
what
they
see
when
they
log
into
the
system
and
the
kinds
of
things
that
they
can
immediately
do,
which
is
a
government
for
productivity
and
then
on
the
access
side.
A
We're
going
to
have
a
whole
bunch
of
API,
driven
interactions
that
we're
going
to
be
enable
we're
going
to
be
able
to
enable
very
soon
including
things
like
new
ways
of
interacting
with
the
workflow
manager
through
the
city
of
restful
interfaces,
management
of
automated
tools,
git
line
Runners
for
CI
CD
stuff,
as
well
as
data
and
Improvement
operations.
So
let
me
stop
there
and
we're
really
thankful
to
the
community
here
of
users
who
who
have
been
able
the
system
to
be.