NVIDIA GH200 Superchip Revolutionizes Apache Spark with Unprecedented Efficiency

As
the
growth
of
generative
AI
continues
to
surge,
IT
leaders
are
seeking
ways
to
optimize
data
center
resources.
According
to
the
NVIDIA
Technical
Blog,
the
newly
introduced
NVIDIA
GH200
Grace
Hopper
Superchip
offers
a
groundbreaking
solution
for
Apache
Spark
users,
promising
substantial
improvements
in
energy
efficiency
and
node
consolidation.

Tackling
Legacy
Bottlenecks
in
CPU-Based
Apache
Spark
Systems

Apache
Spark,
a
multi-language
open-source
system,
has
been
instrumental
in
handling
massive
volumes
of
data
across
various
industries.
Despite
its
advantages,
traditional
CPU-based
systems
encounter
significant
limitations,
leading
to
inefficiencies
in
data
processing
workflows.

Pioneering
a
New
Era
of
Converged
CPU-GPU
Superchips

NVIDIA’s
GH200
Superchip
addresses
these
limitations
by
integrating
the
Arm-based
Grace
CPU
with
the
Hopper
GPU
architecture,
connected
via
NVLink-C2C
technology.
This
integration
offers
up
to
900
GB/s
bandwidth,
significantly
outpacing
the
standard
PCIe
Gen5
lanes
found
in
traditional
systems.

The
GH200’s
architecture
enables
seamless
memory
sharing
between
CPU
and
GPU,
eliminating
the
need
for
data
transfers
and
thus
accelerating
Apache
Spark
workloads
by
up
to
35x.
For
large
clusters
of
over
1,500
nodes,
this
translates
to
a
reduction
of
up
to
22x
in
the
number
of
nodes
and
annual
energy
savings
of
up
to
14
GWh.

NVIDIA
GH200
Sets
New
Highs
in
NDS
Performance
Benchmarks

Performance
benchmarks
using
the
NVIDIA
Decision
Support
(NDS)
benchmark
revealed
that
running
Apache
Spark
on
GH200
is
significantly
faster
compared
to
premium
x86
CPUs.
Specifically,
executing
100+
TPC-DS
SQL
queries
on
a
10
TB
dataset
took
only
6
minutes
with
GH200,
versus
42
minutes
on
x86
CPUs.

Notable
query
accelerations
include:

• Query67:
  36x
  speedup
• Query14:
  10x
  speedup
• Query87:
  9x
  speedup
• Query59:
  9x
  speedup
• Query38:
  8x
  speedup

Reducing
Power
Consumption
and
Cutting
Energy
Costs

The
GH200’s
efficiency
becomes
even
more
apparent
with
larger
datasets.
For
a
100
TB
dataset,
GH200
required
only
40
minutes
on
a
16-node
cluster,
compared
to
the
need
for
344
CPUs
to
achieve
the
same
results
with
traditional
setups.
This
represents
a
22x
reduction
in
nodes
and
12x
in
energy
savings.

Exceptional
SQL
Acceleration
and
Price
Performance

HEAVY.AI
benchmarked
GH200
against
an
8x
NVIDIA
A100
PCIe-based
instance,
reporting
a
5x
speedup
and
16x
cost
savings
for
a
100
TB
dataset.
On
a
larger
200
TB
dataset,
GH200
still
outperformed
with
a
2x
speedup
and
6x
cost
savings.

“Our
customers
make
data-driven,
time-sensitive
decisions
that
have
a
high
impact
on
their
business,”
said
Todd
Mostak,
CTO
and
co-founder
of
HEAVY.AI.
“We’re
excited
about
the
new
business
insights
and
cost
savings
that
GH200
will
unlock
for
our
customers.”

Get
Started
with
Your
GH200
Apache
Spark
Migration

Enterprises
can
leverage
the
RAPIDS
Accelerator
for
Apache
Spark
to
migrate
workloads
seamlessly
to
the
GH200.
This
transition
promises
significant
operational
efficiencies,
with
GH200
already
powering
nine
supercomputers
globally
and
available
through
various
cloud
providers.
For
more
details,
visit
the

NVIDIA
Technical
Blog.

Image
source:
Shutterstock