Candidate:
Homa
Aghilinasab
Title:
Dynamic
Memory
Bandwidth Allocation
for
Real-Time
GPU-Based SoC
Platforms
Date:
May
7,
2020
Time:
2:00
PM
Place:
Remote
Supervisor(s):
Pellizzoni,
Rodolfo
Abstract:
Heterogeneous
SoC
platforms,
comprising
both
general
purpose
CPUs
and
accelerators
such
as
a
GPU,
are
becoming
increasingly
attractive
for
real-time
and
mixed-criticality
systems
to
cope
with
the
computational
demand
of
data
parallel
applications.
However,
contention
for
access
to
shared
main
memory
can
lead
to
significant
performance
degradation
on
both
CPU
and
GPU.
Existing
work
has
shown
that
memory
bandwidth
throttling
is
effective
in
protecting
real-time
applications
from
memory-intensive,
best-effort
ones;
however,
due
to
the
inherent
pessimism
involved
in
worst-case
execution
time
estimation,
such
approaches
can
unduly
restrict
the
bandwidth
available
to
best-effort
applications.
In
this
paper,
we
propose
a
novel
memory
bandwidth
allocation
scheme
where
we
dynamically
monitor
the
progress
of
a
real-time
application
and
increase
the
bandwidth
share
of
best-effort
ones
whenever
it
is
safe
to
do
so.
Specifically,
we
demonstrate
our
approach
by
protecting
a
real-time
GPU
kernel
from
best-effort
CPU
tasks.
Based
on
profiling
information,
we
first
build
a
worst
case
execution
time
estimation
model
for
the
GPU
kernel.
Using
such
model,
we
then
show
how
to
dynamically
recompute
on-line
the
maximum
memory
budget
that
can
be
allocated
to
best-effort
tasks
without
exceeding
the
kernel's
assigned
execution
budget.
We
implement
our
proposed
technique
on
NVIDIA
embedded
SoC
and
demonstrate
its
effectiveness
on
a
variety
of
GPU
and
CPU
benchmarks.
Thursday, May 7, 2020 2:00 pm
-
2:00 pm
EDT