►
Description
With Artificial Intelligence and Machine Learning workloads expected to drive demand for the next wave of applications in the data center, Graphics Processing Unit (GPU) deployments using containers within an enterprise Linux environment will become more common. You will want to attend this session if you are a system administrator, developer or just looking to better understand how Red Hat is partnering with Nvidia to improve the developer experience, support containerized applications, and enable GPU and vGPU accelerated workloads to run on Red Hat Enterprise Linux for both bare-metal and hybrid cloud environments.
A
Well,
welcome
everyone
to
the
Nvidia,
Red
Hat
partners
in
AI
ml
and
other
GPU
enabled
deployments.
My
name
is
Andre
Beausoleil
and
I'm.
A
senior
principal
partner
manager
with
red
hat
and
co-presenting
with
me
today
is
Duncan
Poole,
let
Duncan
introduce
himself
and
he
will
be
hosting
the
first
few
slides
and
then
I
will
take
over.
Thank
you.
B
B
So
the
agenda
is
basically
me
to
outline
the
goals
of
the
partnership.
Talk
about
what
Nvidia
brings
to
bear
in
its
tools.
An
ecosystem
talk
a
little
bit
about
our
container
efforts
and
containers
strategy
and
the
open
source
projects
that
we
have
going
on,
that
that
we
support
and
work
on
with
red
hat
and
then
Andre
will
pick
up
and
he'll
talk
about
some
very
specific
components
to
this
that
Red
Hat
has
been
putting
together.
B
For
us.
That
means,
among
other
things,
try
to
simplify
the
install
process
that
we
do,
because
if
you
like,
you
went
back
about
five
years
and
tried
to
install
rel,
you
would
probably
choose
through
a
few
different
versions
before
you
figure
out
the
magic
incantation
that
lets
you
install
it.
Also,
in
the
day
you
used
to
do
this
using
a
dot
run
file,
and
now
invidious
built,
you
know,
rpm
repos,
for
our
for
our
offerings
to
make
it
a
little
easier.
So,
together
we
do
for
each
card.
B
We
release
for
each
OS
of
rel
for
each
CUDA
distribution,
we
release,
we
have
to
do
sort
of
synchronized
OS
qualification,
so
you
can
imagine
that
this
is
quite
the
cadence
of
meetings.
There's
still
a
lot
of
room
for
improvement
beyond
that
in
the
container
space,
where
you
know,
you
really
want
to
wrap
up
an
application
and
deploy
it
in
a
commercial
environment,
it's
been
important
to
get
more
aligned
with
with
Red
Hat
on
how
we
do
that.
So
we'll
talk
about
that
we've.
A
B
Other
open
source
projects,
so
what's
new
so
from
Nvidia,
what's
new
is
if
you
were
to
go
and
try
and
download
our
driver
and
our
CUDA,
you
could
go
and
use
the
RPM
repo.
We
also
have
within
our
free
CUDA
downloads,
several
new
host
compiler
options.
Llvm
is
directly
supported,
yeah
if
you've
never
used,
CUDA
and
I'm
gonna
guess.
Has
anyone
used
CUDA
here?
Okay,
so
CUDA
is
the
compiler
that
lets.
B
So
you
get
all
these
options
so
so
to
go
on
we're
going
to
talk
about
kubernetes
on
GPUs
and
basically
that
the
trick
here
is
in
a
containerized
environment
you
want
to
have
you
want
to
teach
kubernetes,
how
many
GPUs
you
have?
Are
they
busy
or
not?
Are
they
near
a
CPU,
so
CPU
affinity
becomes
important,
and
all
of
that
is
a
contribution
that
NVIDIA
made
called
a
nvidia
plugin
for
kubernetes.
B
Then
we're
going
to
have
andre
to
talk
about
the
KVM
improvements
that
we've
been
doing
in
grid
together
and
a
little
bit
more
about
open
source
collaboration.
So
if
you
want
to
look
at
the
breadth
of
what
is
Nvidia
in
this
market
space,
we
have
well.
We
have
examples
here
of
some
very
high
performance,
computational
chemistry,
very
long,
running
apps
that
are
well
tuned
for
running
on
GPUs,
and
these
ones
are
all
now
containerized.
So
you
can
go
out
and
you
can
launch
those
apps
with
a
container.
B
So
you
don't
have
to
think
so
hard
about
compiling
it
out
what
are
the
library
dependencies
and
so
on
to
make
it
run.
We
also
have
intimate
relationships
with
all
the
various
frameworks
developers
so
and
if
you're
interested
in
detail
on
this
have
a
an
AI
learning
environment
online.
So
you
can
go
off
and
you
can.
You
can
pick
up
a
framework
and
run
your
own
self-paced
learning
for
this.
B
So
the
the
frameworks
are
great
and
you
can
quickly
become
kind
of
an
AI
expert.
But
if
you
want
to
go
down
the
path,
the
traditional
programming,
we
provide
libraries
that
are
already
ported
to
using
GPUs.
So
if
you're
familiar
with
la
pack
or
any
of
the
standard,
math
heavy
libraries
they're
directly
available
there,
so
you
don't
actually
have
to
you
just
call
them
with
your
CPU
code.
B
You
don't
have
to
write
any
code
at
all
or
you
can
dig
down
and
you
can
use
one
of
a
series
of
standards
like
Python
or
thrust,
and
these
ones
are
also
all
GPU
aware
and
then
finally,
we
have
our
own
specialized
language.
A
free
Fortran,
free,
C
compiler
that
can
be
used,
works
on
the
CPU,
but
it
also
works
in
the
GPU,
and
you
can
blow
your
code
out
directly
to
it.
Okay,.
B
So,
with
Red
Hat
on
rel,
we
now
provide
a
faster
release
of
CUDA,
and
this
in
part,
is
because
we
have
rpm
distribution
model
working
now,
so
we're
gonna
release
four
releases
of
CUDA
per
year
and
then,
as
I
was
saying,
the
math
libraries,
especially
around
AI,
are
moving
even
faster.
So
you
can
pick
up
the
improvements
that
we
have
for
each
new
GPU
that
comes
out
from
Nvidia
directly
every
month
and
because
that
sounds
like
a
nightmare
for
a
developer.
B
So
you
see
you
know,
memory
checkers,
visual,
debuggers,
visual
profilers,
in
a
series
of
libraries
of
various
useful
kinds
for
AI
and
for
math
and
then
finally,
your
standard
languages.
Okay,
on
the
kubernetes
alignment
front,
we
recently
had
our
own
developer
conference,
where
we're
jensen
wong,
our
CEO
demonstrated
containerized
app
running
on
the
show
floor
and
then
failing
over
to
run
on
the
Amazon
Cloud.
So
the
fault,
tolerant
aspect
of
of
our
containerization
strategy
is
starting
to
come
into
play,
just
schoesser
the
robustness
feature
here
and
and
actually
on
nvidia.
B
If
you,
if
you
go
register
as
a
developer,
you
can
download
our
pre-built
containers
that
include
all
these
various
frameworks
already
tuned
up
to
run
on
our
devices.
That's
the
whole
point
of
it.
Okay
and
the
benefits
of
containers
are
obviously
a
stable,
a
stable
environment
for
install
dependencies
not
having
to
resolve
between
the
developer
and
the
user
what
they
actually
are
running
on,
so
it
just.
It
simplifies
the
whole
process
and,
as
anyone
of
us
probably
knows
getting
the
install
dependencies
right
on
linux
is
sometimes
a
bit
about
chasing
your
tail
game.
B
B
Can
you
open
MP
and
that
library
is
actually
a
common
runtime
library
for
people
that
want
to
use
directives?
And
that's
basically
a
comment
that
goes
around
a
block
of
code
saying
run
this
in
parallel.
Openmp
is
the
classic
example
of
this
in
doing
it
for
years,
open
ACC
is
another
one
and
they're
actually
implementing
open
ACC
in
GCC
in
gone,
so
there's
a
fun
little
cooperation.
B
B
So
what
is
header
G's
memory
management?
That's
the
ability
for
malloc
to
work
on
memory
that
sits
on
a
card
and
to
be
able
to
reference
or
dereference
the
pointer,
whether
it's
on
the
card
or
on
the
host
machine
and
think
of
the
underlying
paging
subsystem
for
Linux.
If
it's
on
the
card
and
you're
running
as
a
process
on
the
host,
it'll,
just
page
fault
it
across
and
you'll
run
against
it
on
the
host
or
the
other
way
around.
B
So
if
you
think
about
it,
half
the
problem
with
the
GPU
is
resolving
the
data
dependencies
and
where
they
live,
and
when
you
have
this
feature
like
mouth
this
feature
in
malloc
available,
then
it's
a
huge
developer
simplification.
Now
you
might
sit
there
and
say
yeah,
but
wouldn't
you
have
thrashin
going
on
as
you
reference
these
things
on
either
side?
B
On
the
GPU
side,
it's
not
shown,
but
those
Peas
they
can
be
connected
by
a
env
link
and
indeed,
link
is
this
network
a
very
fast
network
of
up
to
16,
GPUs
and
all
of
this
capability
then
runs
on
all
of
those
and
each
GPU
can
have
32
gigabytes
of
stack
memory,
so
memory
consistency
is
all
supported,
locking
is
all
supported
and
it
just
works.
So
this
is
a
new
feature
that
we're
putting
together
right
now
and
with
that
I'll
pass
it
over
to
hunting
Thank.
A
You
Duncan,
ok,
we
just
got
a
couple
more
slides
to
go
through
and
I
would
like
to
talk
a
little
bit
more
about
the
partnership
that
we
have
with
Nvidia
with
regards
to
their
grid.
That's
the
V
GPU
offering
with
Red
Hat
virtualization.
So
a
couple
of
things
going
on
here
back
in
oh
I,
think
it
was
2015.
We
started
working
with
Nvidia
on
on
providing
the
enablement
for
V
GPUs,
and
that
was
that
was
quite
a
project.
A
It
required
us
promoting
an
upstream
package
which
was
mediated
devices
mediated
devices
is
the
key
enabler
to
allow
you
to
set
up
V
GPUs
right,
in
other
words,
take
a
an
Nvidia,
Tesla
or
Maxwell
GPU
and
then
deviated
out
similar
to
what
you
would
do
for
virtualization
of
CPUs
right.
So
we
were
able
to
get
that
upstream
and
accepted
in
2016,
and
then
afterwards
we
worked
on
getting
that
support
in
Rev,
right,
Red,
Hat
virtualization,
as
well
as
support
added
in
Red
Hat
Enterprise
Linux.
So
they
were
the
upstream
components
which
Nvidia
worked
on.
A
A
Okay,
just
to
give
you
an
idea
of
some
of
the
open
and
closed
source
aspects
of
the
vgpu
again,
their
grid
support,
if
you
notice
we're
actually
installing
the
grid
software
on
the
KVM
host
and
then
what
the
mediator
devices
will
provide,
support
for
or
we'll
need
to
install
the
drivers
on
each
of
the
VM
gas
right.
That's
that's
critical
again!
The
you
know
the
the
V
GPU
will
view
the
GPU
as
if
it's
a
dedicated
GPU,
so
there's
no
need
to
worry
about
additional
management.
A
A
Okay,
another
aspect
of
our
partnership
is
that
last
year
we
were
able
to
collaborate.
We
worked
with
NVIDIA,
we
worked
with
the
HPE
on
a
benchmark.
This
is
the
stack
a
to
benchmark
stack
for
those
who
are
not
familiar.
It's
a
security
consortium
way.
They
tend
to
run
very
high
CPU
a
high
memory
type
benchmarks,
which
lends
well
to
the
HPC
market.
With
our
collaboration,
we
were
able
to
have
a
configuration
of
Nvidia
v100,
that's
their
Volta
GPUs
at
the
time
those
were
the
fastest
available
GPU.
A
So
we
had
8
8,
V
GPUs
and
an
HPE
ProLiant
server
running
Ralph
Red,
Hat
Enterprise
Linux
right.
We
were
able
to
break
a
number
of
records,
both
benchmarks
around
throughput,
as
well
as
any
energy
efficiency
for
those
who
are
interested
in
the
specifics
of
this
benchmark.
We
have
a
couple
of
blogs
that
are
available
and
you
can
get
more
details
with
regards
to
the
configuration
what
this
speaks
to
is.
Essentially,
the
partnership
is
leveraging
two
aspects.
One
is
our
ability
to
provide.
A
Okay,
something
new
that
just
we
just
announced
around
the
time
of
the
Nvidia
GT
C
conference
just
over
a
month
ago,
was
the
availability
of
the
GPU
device
plug-in
right.
This
is
something
that
we
worked
when,
with
the
kubernetes
resource
management
team,
to
get
the
ability
to
provide
support
for
managing
GPU
workloads
and
a
containerized
environment.
This
feature
is
supported
in
OpenShift
3.9
and
the
feature
is
available
as
a
technical
preview
feature,
which
means
that
it's
not
production
supported.
B
A
A
Okay,
the
other
thing
that
is
important
to
our
mutual
customers
is
to
ensure
that
we
are
staying
ahead
of
the
security
vulnerabilities
and,
of
course,
to
this
point,
and
everyone
here
is
probably
familiar
with
spectral
meltdown.
It's
something
that
we
all
have
to
deal
with
at
the
beginning
of
the
year
and
with
NVIDIA
we
were
able
to
provide
some
of
the
patches
to
them
so
that
they
can
test
it
and
and
and
validate
that
we
had
mitigated
any
exposure
that
resulted
from
spectral
meltdown.
So
that's
that's
a
value
of
the
partnership.
A
All
right
last
but
not
least,
I'd
like
to
talk
about
our
demo,
we're
going
to
be
having
nvidia
in
our
booth
at
booth
number
725.
That's
the
AI
IOT
AI
partner,
ecosystem
booth,
it's
just
on
the
other
side.
They'll
be
running
demos
and
we'll
also
have
representatives
from
our
product
management
team,
as
well
as
technical
staff
to
address
any
questions
or
if
you
want
to
have
any
side
meetings
will
be
available
to
to
address
them
there
as
well.