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From YouTube: Introduction to Perlmutter
Description
Jay Srinivasan of NERSC presents an Introduction to Perlmutter. Recorded live via Zoom at GPUs for Science 2020. https://www.nersc.gov/users/training/gpus-for-science/gpus-for-science-2020/ Session Chair: OisÃn Creaner
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A
B
A
To
give
a
quick
introduction
to
nurse,
I
think
most
people
here
might
be
familiar
with
nurse.
We
are
the
mission
high
performance
computing
facility
for
the
office
of
science,
and
you
know
on
the
left.
You
see
a
bunch
of
statistics
about
nurse.
We
have
thousands
of
users,
hundreds
of
projects
and
a
lot
of
codes,
and
what
that
means
is
really
we
have
a
diverse
workflow
and
that
diverse
workload
you
know
is
characterized
by
simulations
by
data
analysis
and
recently
learning
as
well
right.
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A
So
what
is
promoter?
It's
a
system
that
you
know
we've
right
from
the
get-go.
When
we
started
the
project
we
decided
it
was.
It
was
gonna,
be
a
system
optimized
for
science.
So
what
does
that
mean?
It's
a
system
that
provides
a
substantial
increase
in
performance
over
over
quarry,
which
is
our
current
knl
based
system.
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C
C
A
A
A
And
they'll
have
one
amd
milan
cpu,
which
is
the
next
generation
from
what
people
are
obviously
now
on
the
wrong
line.
We'll
have
over
6
000
ampere
gpus,
the
the
interconnect
as
I've
talked
about.
Is
this
ethernet
compatible
high
performance
interconnect
and
really
we
we
expect
that
we'll
be
able
capable
of
terabit
connections
to
and
from
the
system
right.
A
We're
bringing
this
in
two
phases
right,
so
we'll
bring
in
the
first
phase
late
this
year
and
that'll
consist
of
the
gpu
accelerated
nodes,
all
of
the
storage
and
and
the
all
of
the
associated
nodes
that
will
help
us
run
this
as
a
system
right.
So
that
includes
the
login
nodes,
all
of
the
nodes,
for
you
know
high
memory,
work,
workflows
and
so
forth
and
then,
as
well
as
the
storage
and
access
to
external
storage,
so
it'll
be
fully
integrated
into
nurse.
Just
like
the
systems
we
have.
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A
We
have
activities
in
in
the
areas
of
network
the
storage,
the
application,
readiness
work,
as
well
as
the
system
software
work,
to
ensure
that
this
system
that's
been
put
together
will
work
really
well
for
our
users
and
be
a
productive
system
right,
and
so
you
can
see
from
those
four
areas
that
all
of
those
four
areas
are
things
that
are
are
new
technologies
to
promoter
they're,
not
just
new
to
promoter
they're,
also
new
technologies.
Overall
right,
the
the
high-speed
network
is
a
brand
new
technology
from
craig.
A
You
know
stats
and
speeds
and
feeds
on
there
and
we're
looking
at
things
like
you
know,
almost
20
teraflops
with
the
tensor
core
on
fp64
and
so
forth.
So
the
other
features
that
really
are
of
importance
to,
we
believe
to
our
users
and
in
fact,
that
the
talks
over
the
next
couple
of
days
are
addressing
are
things
like
you
know?
How
are
these
things
connected
together
and
how
are
you
going
to
be
able
to
use?
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A
The
tf32
support-
and
I
think
jack
and
she
talked
about
this-
is
the
the
mixed
there's
going
to
be
a
number
of
talks
on
mixed
precision,
stuff
work
and
that's
going
to
be
very
interesting.
So
I
think
that
technology
is
going
to
be
explored
as
well
over
the
next
couple
of
days
here
for
the
system
that
we
have.
You
know
I
just
wanted
to
touch
on
one
specific
aspect
of
the
all
flash
file
system.
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So
this
one,
obviously
it's
all
flash
it's
luster-based
and
it
has
it's
gonna,
be
fast,
it's
gonna
be
usable
and
it's
optimized
right.
So
it's
fast
across
multiple
dimensions
has
high
bandwidth
because
it's
all
flash
it
has
excellent
higher
iops
performance,
and
it's
able
to
sorry
this
should
say
on
3.2,
with
3.2
million
file
creates
per
second
right.
A
It's
going
to
be
usable
for
our
users,
there's
35
petabytes
of
usable
capacity,
that's
on
essentially
on
the
machine
right.
It's
not
a
remote
file
system
that
you
have
to
access
to
a
small
network
pipe
or
anything.
It's
on
the
machine.
It's
part
of
the
fabric
that
compute
nodes
are
on
and
the
gpu
and
the
cpu,
and
it.
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So
if
people
are
able
to
use
it
and
we're
going
to
have
data
movement
capabilities
that
are
new,
that
allow
people
to
move
data
seamlessly
between
the
tiers
right.
So
what
are
the
tiers
we're
talking
about?
Is
the
storage,
the
external
file
systems
that
we
have
and,
and
obviously
things
like
you
know,
the
archival,
storage
and
so
forth.
So
and
then
finally,
there's
a
number
of
optimizations
that
luster
clearly
works
at
scale
as
people
know
now
on
corey,
but
with
all
flash.
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A
Hopefully
your
lives
easier
when
you
do
use
the
system-
and
that
includes
things
like
system
software
and
scheduling.
How
do
we
schedule
these
multiple
resources
and
even
drilling
down
into
the
node?
How
do
you
make
sure
that
things
like
the
multi-instance
gpu
technology
is
available
in
a
fashion
that
makes
it
useful
for
multiple
users
to
use
the
same
core
gpu
partitioned
into
multiple
instances
and
things
like
that?
A
B
A
A
The
there's
other
work
that
isn't
sort
of
formally
part
of
the
project,
but
that
nursk
is
doing
we're
working
with
you
know,
pgi,
to
enable
openmp,
gpu
acceleration,
and
that's
effort
that
I
think
you're
going
to
hear
about
a
little
bit
where
obviously
performance
portability
is
a
key
aspect
of
making
sure
that
these
systems
are
usable,
are
productive
for
our
users
right,
not
just
on
nurse,
because
nobody
runs
just
owners.
People
run
all
across
resources
in
the
doe
complex
and
so
and
then.
A
A
A
This
set
of
sessions
to
our
users
is
sort
of
testament
to
that.
We
have
a
very
strong
training
effort
in
collaboration
with
nvidia
and
createhpe,
to
give
new
information
about
the
new
technologies
and
promoter
to
our
users,
and
we
really
look
forward
to
working
with
you
all
to
make
promoter
a
very
productive
platform,
the
next
generation
platform
for
our
users
and
I'll
stop
there
happy
to
take
any
questions
that
you
have.
B
A
Yeah,
that's
a
good
question,
so
you
know
promoter
is
really
the
focus,
the
most
important
focus
of
nurse.
I
would
argue
right
and
what
that
means
is
you
know
we
at
nurse.
We
have
a
formal
project
that
says:
okay,
we're
going
to
bring
in
the
next
generation
of
the
system
that
we
have
over
50
percent
of
nurse
staff
are
directly
working
on
the
effort
to
bring
in
per
mutter
into
production
right,
and
so,
when
we've
split
it
up
into
these
different
groups.
Really.