Enhancing GPU Performance: Tackling Instruction Cache Misses

GPUs
are
designed
to
process
vast
amounts
of
data
swiftly,
equipped
with
compute
resources
known
as
streaming
multiprocessors
(SMs)
and
various
facilities
to
ensure
a
steady
data
flow.
Despite
these
capabilities,
data
starvation
can
still
occur,
leading
to
performance
bottlenecks.
According
to
the
NVIDIA
Technical
Blog,
a
recent
investigation
highlights
the
impact
of
instruction
cache
misses
on
GPU
performance,
particularly
in
a
genomics
workload
scenario.

Recognizing
the
Problem

The
investigation
centers
on
a
genomics
application
utilizing
the
Smith-Waterman
algorithm,
which
involves
aligning
DNA
samples
with
a
reference
genome.
When
executed
on
an
NVIDIA
H100
Hopper
GPU,
the
application
exhibited
promising
performance
initially.
However,
the
NVIDIA
Nsight
Compute
tool
revealed
that
the
SMs
occasionally
faced
data
starvation,
not
due
to
lack
of
data
but
due
to
instruction
cache
misses.

The
workload,
composed
of
numerous
small
problems,
caused
uneven
distribution
across
the
SMs,
leading
to
idle
periods
for
some
while
others
continued
processing.
This
imbalance,
known
as
the
tail
effect,
was
particularly
noticeable
when
the
workload
size
increased,
resulting
in
significant
instruction
cache
misses
and
performance
degradation.

Addressing
the
Tail
Effect

To
mitigate
the
tail
effect,
the
investigation
suggested
increasing
the
workload
size.
However,
this
approach
led
to
unexpected
performance
deterioration.
The
NVIDIA
Nsight
Compute
report
indicated
that
the
primary
issue
was
the
rapid
increase
in
warp
stalls
due
to
instruction
cache
misses.
The
SMs
could
not
fetch
instructions
quickly
enough,
causing
delays.

Instruction
caches,
designed
to
store
fetched
instructions
close
to
the
SMs,
were
overwhelmed
as
the
number
of
required
instructions
grew
with
the
workload
size.
This
phenomenon
occurs
because
warps,
groups
of
threads,
drift
apart
in
their
execution
over
time,
leading
to
a
diverse
set
of
instructions
that
the
cache
struggles
to
accommodate.

Solving
the
Problem

The
key
to
resolving
this
issue
lies
in
reducing
the
overall
instruction
footprint,
particularly
by
adjusting
loop
unrolling
in
the
code.
Loop
unrolling,
while
beneficial
for
performance
optimization,
increases
the
number
of
instructions
and
register
usage,
potentially
exacerbating
cache
pressure.

The
investigation
experimented
with
varying
levels
of
loop
unrolling
for
the
two
outermost
loops
in
the
kernel.
The
findings
suggested
that
minimal
unrolling,
specifically
unrolling
the
second-level
loop
by
a
factor
of
2
while
avoiding
unrolling
the
top-level
loop,
yielded
the
best
performance.
This
approach
reduced
instruction
cache
misses
and
improved
warp
occupancy,
balancing
performance
across
different
workload
sizes.

Further
analysis
of
the
NVIDIA
Nsight
Compute
reports
confirmed
that
reducing
the
instruction
memory
footprint
in
the
hottest
parts
of
the
code
significantly
alleviated
instruction
cache
pressure.
This
optimized
approach
led
to
better
overall
GPU
performance,
particularly
for
larger
workloads.

Conclusion

Instruction
cache
misses
can
severely
impact
GPU
performance,
especially
in
workloads
with
large
instruction
footprints.
By
experimenting
with
different
compiler
hints
and
loop
unrolling
strategies,
developers
can
achieve
optimal
code
performance
with
reduced
instruction
cache
pressure
and
improved
warp
occupancy.

For
more
details,
visit
the

NVIDIA
Technical
Blog.

Image
source:
Shutterstock