The convergence of HPC and AI: Driving innovation at speed  

In
today’s
rapidly
changing
landscape,
delivering
higher-quality
products
to
the
market
faster
is
essential
for
success.
Many
industries
rely
on
high-performance
computing
(HPC)
to
achieve
this
goal.
 

Enterprises
are
increasingly
turning
to
generative
artificial
intelligence
(gen
AI)
to
drive
operational
efficiencies,
accelerate
business
decisions
and
foster
growth.
We
believe
that
the
convergence
of
both

HPC
and
artificial
intelligence
(AI)
is
key
for
enterprises
to
remain
competitive.
  

These
innovative
technologies
complement
each
other,
enabling
organizations
to
benefit
from
their
unique
values.
For
example,
HPC
offers
high
levels
of
computational
power
and
scalability,
crucial
for
running
performance-intensive
workloads.
Similarly,
AI
enables
organizations
to
process
workloads
more
efficiently
and
intelligently.
 

In
the
era
of
gen
AI
and
hybrid
cloud,

IBM
Cloud®
HPC
brings
the
computing
power
organizations
need
to
thrive.
As
an
integrated
solution
across
critical
components
of
computing,
network,
storage
and
security,
the
platform
aims
to
assist
enterprises
in
addressing
regulatory
and
efficiency
demands. 

How
AI
and
HPC
deliver
results
faster:
Industry
use
cases

At
the
very
heart
of
this
lies
data,
which
helps
enterprises
gain
valuable
insights
to
accelerate
transformation.
With
data
nearly
everywhere,
organizations
often possess
an
existing
repository
acquired
from
running
traditional
HPC
simulation
and
modeling
workloads.
These
repositories
can
draw
from
a
multitude
of
sources.
By
using
these
sources,
organizations
can
apply

HPC
and
AI
to
the
same
challenges,
enabling
them
to
generate
deeper,
more
valuable
insights
that
drive
innovation
faster.  

AI-guided
HPC
applies
AI
to
streamline
simulations,
known
as
intelligent
simulation.
In
the
automotive
industry,
intelligent
simulation
speeds
up
innovation
in
new
models.
As
vehicle
and
component
designs
often
evolve
from
previous
iterations,
the
modeling
process
undergoes
significant
changes
to
optimize
qualities
like
aerodynamics,
noise
and
vibration.
 

With
millions
of
potential
changes,
assessing
these
qualities
across
different
conditions,
such
as
road
types,
can
greatly
extend
the
time
to
deliver
new
models.
However,
in
today’s
market,
consumers
demand
rapid
releases
of
new
models.
Prolonged
development
cycles
might
harm
automotive
manufacturers’
sales
and
customer
loyalty.
 

Automotive
manufacturers,
having
a
wealth
of
data
related
to
existing
designs,
can
use
these
large
bodies
of
data
to
train
AI
models.
This
enables
them
to
identify
the
best
areas
for
vehicle
optimization,
thereby
reducing
the
problem
space
and
focusing
traditional
HPC
methods
on
more
targeted
areas
of
the
design.
Ultimately,
this
approach
can
help
to
produce
a
better-quality
product
in
a
shorter
amount
of
time.
 

In
electronic
design
automation
(EDA),
AI
and
HPC
drive
innovation.
In
today’s
rapidly
changing
semiconductor
landscape,
billions
of
verification
tests
must
validate
chip
designs.
However,
if
an
error
occurs
during
the
validation
process,
it
is
impractical
to
re-run
the
entire
set
of
verification
tests
due
to
the
resources
and
time
required.
 

For
EDA
companies,
using
AI-infused
HPC
methods
is
important
for
identifying
the
tests
that
need
to
be
re-run.
This
can
save
a
significant
amount
of
compute
cycles
and
help
keep
manufacturing
timelines
on
track,
ultimately
enabling
the
company
to
deliver
semiconductors
to
customers
more
quickly.
 

How
IBM
helps
support
HPC
and
AI
compute-intensive
workloads

IBM
designs
infrastructure
to
deliver
the
flexibility
and
scalability
necessary
to
support
HPC
and
compute-intensive
workloads
like
AI.
For
example,
managing
the
vast
volumes
of
data
involved
in
modern,
high-fidelity
HPC
simulations,
modeling
and
AI
model
training
can
be
critical,
requiring
a
high-performance
storage
solution.
 

IBM
Storage
Scale
is
designed
as
a
high-performance,
highly
available
distributed
file
and
object
storage
system
capable
of
responding
to
the
most
demanding
applications
that
read
or
write
large
amounts
of
data. 

As
organizations
aim
to
scale
their
AI
workloads,

IBM
watsonx™
on

IBM
Cloud®
helps
enterprises
to
train,
validate,
tune
and
deploy
AI
models
while
scaling
workloads.
Also,
IBM
offers
graphics
processing
unit
(GPU)
options
with
NVIDIA
GPUs
on
IBM
Cloud,
providing
innovative
GPU
infrastructure
for
enterprise
AI
workloads.
 

However,
it’s
important
to
note
that
managing
GPUs
remains
necessary.
Workload
schedulers
such
as IBM
Spectrum®
LSF®
efficiently
manage
job
flow
to
GPUs,
while

IBM
Spectrum
Symphony®,
a
 low-latency,
high-performance
scheduler
designed
for
the
financial
services
industry’s
risk
analytics
workloads,
also
supports
GPU
tasks.
 

Regarding
GPUs,
various
industries
requiring
intensive
computing
power
use
them.
For
example,
financial
services
organizations
employ
Monte
Carlo
methods
to
predict
outcomes
in
scenarios
such
as
financial
market
movements
or
instrument
pricing.
 

Monte
Carlo
simulations,
which
can
be
divided
into
thousands
of
independent
tasks
and
run
simultaneously
across
computers,
are
well-suited
for
GPUs.
This
enables
financial
services
organizations
to
run
simulations
repeatedly
and
swiftly.
 

As
enterprises
seek
solutions
for
their
most
complex
challenges,
IBM
is
committed
to
helping
them
overcome
obstacles
and
thrive.
With
security
and
controls
built
into
the
platform,
IBM
Cloud
HPC
allows
clients
across
industries
to
consume
HPC
as
a
fully
managed
service,
addressing
third-party
and
fourth-party
risks.
The
convergence
of
AI
and
HPC
can
generate
intelligence
that
adds
value
and
accelerates
results,
assisting
organizations
in
maintaining
competitiveness. 

Learn
how
IBM
can
help
accelerate
innovation
with
AI
and
HPC