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From YouTube: NUG meeting 15july2021
Description
Video recording of the NUG monthly meeting, June 15 2021
A
A
Yeah,
okay,
so
we'll
follow
our
normal
format,
which
I
think
people
are
fairly
familiar
with
now.
The
idea
of
this
is
to
be
an
interactive
meeting,
which
is
to
say:
please
participate,
you
can
you
can
either
raise
your
hand
or
or
just
unmute
yourself
and
and
speak
up
when
you've
got
a
question
or
a
comment
to
make
there'll
be
lots
of
opportunities
for
that
and
I
think
we're
you
know:
we've
got
around
about
20
people
at
the
moment,
so
that's
a
comfortable
enough
size.
I
think
that
we
can
just
speak
up.
A
B
A
So
our
agenda
will
follow
kind
of
our
normal
patents,
we'll
start
out
with
a
win
of
the
month
and
the
flip
side
of
today
I
learned:
we've
got
a
few
announcements
to
make
and
there's
opportunity
to.
We
have
for
participants
to
make
announcements
there
too,
and
then
for
our
topic
of
today.
We
have
norm
barasser
from
nursk's
building
infrastructure
group
who's,
a
energy
efficiency
expert
and
has
done
a
lot
around
nes
set
up
for
energy
efficiency
with
with
its
computer
room.
A
So
they'll
talk
us
through
a
little
bit
about
some
of
the
things
that
we're
doing
and
have
a
opportunity
for
discussion
there
and
then
we'll
finish
up
with
some
looking
ahead
to
what's
coming
up
and
a
quick
run
through
our
numbers
last
last
month,
so
starting
out
for
win
of
the
month.
So
the
aim
of
this
segment
is
to
you
know,
basically
share
and
celebrate
the
achievements
in
our
community
and
they
can
be
big
or
small.
Getting
a
paper
accepted
solving
a
challenging
bug.
C
A
A
The
idea
here
is
that
what
yeah
we
can
learn
from
from
each
other
and
and
bounce
ideas
off
each
other
of
how
to
how
to
solve
things
as
well.
It's
also
kind
of
an
opportunity
to
talk
about
a
a
new
tip
for
using
nest
systems
that
you
might
have
come
across
recently
or
just
something
interesting
that
you
learned
or
read
recently
that
might
interest
others.
A
So
now,
for
me,
a
lot
of
the
learning
in
the
last
month
has
been
experiences
using
spac,
using
spec
to
set
up
a
bunch
of
software
environment
which
we
have
both
on
corey
as
well
as
they're
setting
up
perlmutter,
and
it's
a
it's
a
very
powerful
tool.
It
does
a
lot
of
clever
things.
It
could
also
be.
There
can
be
some
challenges,
yeah
working
out,
what
didn't
work
when
a
when
a
build
failed,
saying
it's.
A
Yeah,
it's
it's
an
interesting
experience
of
diving
in
and
looking
at
how
it
you
know
how
it
does
things,
but
something
that's
been
really
helpful.
There
is
the
community
on
on
slack.
So,
if
you're,
if
you're,
using
it
to
install
software,
there's
a
spec
slack,
which
has
quite
a
helpful
group
of
users.
A
A
Back
and
check
that
for
links
to
things
and
so
on
important
things
that
might
that
yeah
that
well,
that
will
affect
people
in
the
latest
maintenance.
We
updated
sloan,
and
there
is
a
slight
change
in
slums
behavior,
which
is
a
dash
dash
overlap
flag
for
when
you're
running,
multiple
s-runs
on
the
same
node,
so
most
kind
of
typical,
you
know
straightforward
use,
isn't
impacted
by
this.
A
Do
one
s
run
put
it
into
the
background.
Do
the
other
restaurant
put
it
in
the
background?
Wait
for
them
all!
You
now
need
to
let
slum
know
that
it
it's
allowed
to
start.
This
runs
on
the
same
node
and
so
there's
a
new
dash
gesture
over
that
flag.
For
that.
So
we
have
some
examples
in
our
docs
of
how
to
use
that,
and
it's
just
a
kind
of
a
short
form
of
what's
what's
changed
there.
A
Some
cfps
coming
up
there
that
we
know
about
the
links
to
these
are
in
the
weekly
email.
If
there's
a
workshop
or
an
accelerated
programming
using
directives,
a
parallel
applications
workshop
for
alternatives
to
mpi
plus
x.
I
think
this
covers
things
like
gas
net
and
I
get
global
array
sort
of
stuff,
also
and
also
at
sc21.
A
Is
the
super
check
workshop
on
checkpointing,
a
few
training
things
coming
up.
The
sap
webinar
series,
if
you
haven't
seen
this,
this
is
there's
some
really
interesting
stuff
there
and
some
good
civilian
little
tips
for
using
hpc
and
for
scientific
computing
and
after
the
webinars
are
complete.
They
upload
links
to
the
recordings
onto
the
website.
A
We
have
tomorrow,
I
believe,
there's
a
training
hosted
by
nvidia,
I
think
about
cuda
multi
multi-threading,
with
streams.
This
is
useful
for
preparing
for
perlmutter
there's
also
in
about
another
month
in
late
august,
a
four
day:
cmake
training,
where
we're
partnering
with
keywear.
So
if
you
develop
or
even
just
sort
of
your
build
and
install
applications,
that
could
be
world
worthwhile.
A
And
one
other
kind
of
interesting
announcement
that
we
have,
if
you
haven't
seen
it
already,
the
e4s,
which
is
the
extreme
scale
scientific
software
stack,
I
think,
which
is
part
of
the
sap
project,
is
updated
version
21.2
just
to
say
february
february,
2021
is
now
available
on
corey.
I
think
everything's
built
for
cory
haswell.
A
I
think
pets
you
might
be
part
of
it
schlepc,
so
so
there's
some
sort
of
good
libraries
available
there
to
get
it
to
sort
of
a
two-step
process.
You'll
need
to
modulate
p4s
stack
first
and
that
will
put
other
modules
in
your
path
with
the
specific
packages.
It
also
sets
up
a
spec
environment
for
building
things.
On
top
of
it,
that's
the
announcements
that
I
have
from
nurse's
side
does
anybody
have
any
other
calls
for
participation
that
they'd
like
to
bring
up.
A
C
C
A
A
C
So
as
yeah
as
I
said,
I'm
my
name
is
norm.
Rasa
a
little
word
on
my
history.
I've
been
an
employee
at
at
lawrence,
berkeley,
national
lab
since
2000.
So
21
years
now,
I
initially
came
to
lawrence
berkeley
lab
after
having
worked
in
an
area
called
energy
engineering,
energy
efficiency
for
commercial
buildings.
C
Go
over
with
the
reasons
for
some
of
that
later,
but
I
want
to
just
talk
first
about
something
that
all
of
the
the
users
here
in
in
our
community
are
probably
pretty
well
aware
of
that.
The
first
level
of
energy
improvement
just
goes
down
to
the
processing
capability
of
these
scientific
computing
platforms,
and
this
is
some
an
aspect
of
the
generational
improvements
of
our
systems
that
that
users
may
not
have
appreciated.
But
if
you
look,
you
know,
let's
just
go
as
far
back
only
as
as
as
edison.
C
If
you
look
at
the
at
its
power
consumption
for
its
compute
throughput,
when
we
deployed
corey,
we
got
roughly
five
edisons
with
really
a
doubling
of
the
power.
So
if
you
think
of
that,
this
is
more.
You
know
law
stuff,
but
that's
energy
efficiency
right
there
that
computational
throughput
for
less
power
consumption
is,
is
energy
efficiency
at
the
first
order
and
we're
getting
the
same
thing
with
perlmutter.
C
C
So
it's
important
to
lose
track
of
that,
but
one
thing
and
you
will
notice
we
are
getting
a
doubling
of
power,
but
a
five
times
improvement
and
it's
going
to
come
in
slightly
under
five
times,
so
we
can
see
the
softening
of
the
moore's
law
and
we're
looking
at
oh
believe
it
or
not.
We're
actually
looking
at
nurse
10
and-
and
it
looks
like
they'll-
even
be
more
of
a
softening
of
that.
But
but
this
is
an
important
aspect
that
our
our
computational
technology
is
providing
core
energy
efficiency
improvement.
C
That's
not
to
negate
the
need
to
make
sure
that
these
infrastructure
and
other
support
aspects
of
our
operational
services
don't
need
to
be
paid
attention
to.
So
I've
got
three
basic
levels
of
energy
efficiency
at
the
support
and
infrastructure
level.
I
don't
want
to
point
out
the
the
next
level
that
I
always
like
to
emphasize
is
once
you
start
a
compute
job.
C
It
should
complete,
because
if
it
doesn't
complete,
if
it
gets
to,
70
percent,
gets
to
30
percent
or
gets
even
before
it
gets
to
100
and
it
crashes.
That's
just
pure
waste
out
the
window,
and
this
is
one
of
the
reasons
why
we
put
so
much
emphasis
on
helping
our
users
with
their
car,
their
code
and
and
we
do
the
best
we
can
to
ensure
that
once
jobs
are
being
deployed,
they
have
a
high
probability
of
succeeding.
C
Then
site-specific
facility
design
is
next
nurse.
We
are
located
here
in
berkeley
and
we
happen
to
have
a
very
mild
climate
and
we're
able
to
produce
an
hvac
system
that
doesn't
use
chillers,
basically
vapor
compression
air
conditioning
which
we're
all
very
familiar
with.
Oh
the
lights
turned
off
on
me,
I'm
the
only
one
on
the
floor
right
here.
C
So
this
is
an
important
aspect
of
being
able
to
just
dissipate
the
heat
to
the
environment
without
very
energy,
intensive
hvac
equipment.
I'll
talk
about
that
later
and
then
the
last
thing
is
once
you
have
that
equipment
deployed
having
high
resolution
monitoring
tools
and
data
analysis
tools
to
to
adequately
adequately
determine
whether
the
those
systems
are
performing
optimally
is
a
a
perennial
challenge.
C
A
nurse
has
invested
a
lot
in
both
in
staff
and
infras
and
and
systems
deployment
to
to
be
able
to
to
monitor
how
our
systems
are
performing
analyze
and
and
improve
them.
This
this,
this
positive
feedback
loop
and
indeed
we're
helping
to
set
the
standard
for
the
state
of
the
art
and
scientific
computing.
It's
with
quick
words
on
our
building,
we're
a
four-story,
150,
000
square
foot,
building,
and
basically
40k
of
that
is
offices.
C
C
Right
are
in
in
berkeley
right
now.
Nurse
or
shy.
Wang
hall
is
approximately
40
of
the
campus
energy
demand.
So
we
are
basically
designated
here
as
the
significant
energy
user
on
on
the
berkeley
lab
campus.
So
that
gives
us
a
lot
of
extra
attention
and
help
from
the
lab
directorate
in
identifying
energy
efficiency
measures
and
I'll
talk
a
little
bit
more
about
that.
C
We
collaborate
deeply
with
my
former
division:
the
energy
technologies
area,
where
there
is
a
data,
center's
efficiency
center
for
general
id
for
the
for
the
private
sector,
and
they
are
their
experts,
come
in
and
help
me.
We
also
have
a
a
specialist
energy
engineering
consultant
called
kw
engineering.
C
This
gentleman
right
here
that
that
comes
in
and
and
does
consulting
for
all
of
the
buildings
on
campus
and
there's
got
a
special
attention
for
for
nurse
because
of
our
high
energy
demand.
What
are
the
targets
that
we
use
to
know
that
we
are
actually
operating
efficiently?
We
have
two
metrics.
We
have
the
power
usage,
effectiveness
and
and
a
subset
of
it,
which
is
it
power,
usage
effectiveness.
I'll
talk
a
little
bit
more
about
that,
basically
pue's
the
facility
wide.
C
C
Over
and
above
the
compute,
I
t
eliminates
some
of
the
of
the
you
know,
non-support
stuff,
that
doesn't
matter
as
much
and
looks
just
in
at
the
I.t
inside
the
cabinet
and
peels
out
the
hvac
and
and
it's
it's
a
way
of
understanding
the
efficiency
of
the
hpc
directly
another
one.
That's
becoming
much
more
important
is
wattage
water
usage
effectiveness,
that
is,
our
cooling.
Our
water
cooling
facilities
use
these
large
cooling
towers,
which
you
can
see
a
picture
of
down
here.
C
Currently,
with
pearl
mudder
were
projected
to
evaporate
somewhere
around
60
million
gallons
of
water
a
year.
So
it's
a
lot
of
water.
That
was
a
large
increase
from
the
corey
only
or
the
or
the
corian
edison
era,
when
we
were
down
around
the
12
to
15
million
and
that's
because
pearl
motor,
while
it's
a
more
efficient
system,
it's
using
100,
100
percent,
liquid
cooling
and
so
hits
the
the
the
cooling
towers
harder
and
we're
evaporating
a
lot
of
water.
C
The
preliminary
analysis
numbers
for
nurse
10,
on
the
other
hand,
has
that
water
use
blooming
to
around
180
million.
So
we
are
in
the
process
of
really
really
seriously
looking
at
our
water
usage
effectiveness
and
we're
starting
to
evaluate
new
technologies
for
the
nurse
10
era
that
will
use
a
lot
less
water.
C
Water
usage
effectiveness
is
a
different
type
of
a
metric.
It's
not
unitless,
like
the
other
two,
we
we
look
at
the
amount
of
water
and
the
cooling
plant
energy
and
you
end
up
with
the
liters
per
kilowatt
hour
hour.
We
have
been
monitoring
that
metric
for
a
little
bit
over
a
year
now,
and
this
is
site
specific.
So
you
really
can't
compare
one
site
to
another
and
we're
in
the
process
right
now
of
determining
for
our
current
cooling
plant
with
pearl
mudder.
C
C
How
do
we?
I
mention
that
in
a
third
budget,
a
bullet?
How
do
we
pay
attention
to
how
the
systems
operate?
Well,
we
have
deployed
this
system.
We
call
omni.
This
is
an
old
flow
chart
of
of
everything.
That's
happening.
It's
actually
not
very
current,
no
more,
but
it's
still
illustrative
of
how
detailed
it
is
out
of
all
of
the
hyperscale
dui
data
centers.
C
You
know
a
data
monitoring
plan
is
put
in
the
the
systems
are
deployed,
there's
a
period
of
gathering
data.
Then
we
decide
what
to
do
and
then
a
project
is
designed
and
then
you
know
the
work
is
done
and
then
post
work.
We
look
at
the
data
again
and
see
how
well
we've
improved
things.
Well,
that's
a
lot
of
time
delays
and
in
the
period
when
you're
gathering
the
data
you've
got
inefficient
operation
and
wasted
energy.
C
We
operate
in
a
different
ethic
where
we
say
we
don't
know
what
we
need
to
measure
and
that
time
delay
of
once
we
notice
we
need
to
get
more
eyes
on
the
performance
of
that
equipment.
It's
too
late.
So
we
have
decided
that
we
have
the
the
capability
of
just
gathering
everything
and
when
we
see
that
we
have
a
problem,
we
will
have
that
data
already.
C
We
we
use
the
omni
system
and
data
that
we
have
becomes
this
triumvirate
of
of
support
for
our
our
optimization
of
the
entire
facility,
with
the
sustainable
berkeley
lab.
That's
the
the
ldl
directorate
that
that
helps
leverage
their
resources
to
help
us
improve
nurse,
and
it's
this
a
positive
collaboration
which
is
rapidly
becoming
a
template
for
for
energy
efficiency
improvements
in
the
entire
d.
C
We
indeed
the
the
hyper
scale
scientific
community
in
large
one
example,
that
of
an
area
that's
becoming
emerging
in
all
of
the
large
top
50
type
data
centers
is
this
area
operational
data
analytics
our
omni
platform?
Does
this
is,
is
that
it
co-mingles
hpc,
telemetry
and
hvs
ac
infrastructure
monitor
data
into
a
common
data
base
that
we
can
then
analyze
together
time
synchronize
and
be
able
to
deploy
solutions.
C
This?
This
is
a
very,
very
powerful
tool.
This
tool,
right
here
sky
spark
allows
us
to
look
at
at
all
of
our.
It
basically
has
cray
fan.
Blower
fan
performance
data
from
from
corey
in
this
same
platform
along
with
the
hvac,
and
it
allows
us
to
look
at.
We
establish
a
baseline.
This
is
showing
a
scatter
plot.
That's
showing
the
cooling
plant
performance
in
both
the
baseline
period
and
a
targeted
analysis
period.
So
what
we
have
here
is
this
baseline
fan,
power
and
pumping
power.
C
Scatters
are
are
showing
what
where
we
should
be
performing.
We've
done
settings
changes
in.
In
this
example,
we've
done
settings
changes
and
we're
looking
at
that
analysis
period
versus
the
baseline,
and
this
plot
is
showing
that
that
we
are
actually
burning
a
lot
more
energy
here
in
the
in
the
cooling
tower
fans,
and
so
this
helps
us
zero
in
on
on
the
settings
that
are
in
in
the
cooling
plant,
and
we
can
do
this
real
time
and
we
can
iterate
and
dial
things
in
yes,.
A
See
I
I
noticed
on
it
was
just
this
past
weekend,
this
being
used
in
in
real
life,
in
fact,
and
probably
non-concomi
with
more
detail,
but
so
we
had
a
power
outage
over
the
weekend
for
power,
maintenance
work,
and
so
when,
when
that
happens,
when
we
don't
have
kind
of
the
main
feed
of
power,
we
can
actually
keep
a
fair
bit
of
stuff
running
on
backup
power,
but
corey
computes.
I
just
sort
of
too
much
for
it.
In
fact,
corey.
A
We
think
if
the
forecast
is
right,
that
we
have
enough
cooling
capacity
that
can
be
driven
by
the
backup
generator
to
keep
the
thing
just
to
keep
those
things
running
and
and
cooled,
and
there
was
a
yeah
a
bit
of
chatter.
During
that
time,
you
know
on
the
internal
slack
channel
as
the
operators
were
watching.
What
I
imagined
was
these
charts
that
norm's
showing
right
now
and
watching.
A
C
This
is
the
chart
exactly
and
I
happen
to
have
it
up
here.
These
are
the
environmentals
and
the
racks
that
you
were
talking
about,
and
this
is
what
we
were
chattering
about.
These
are
deployed
sensors
that
show
us
the
air
intake
temperatures
as
a
matter
of
fact
we're
going
to
go
the
last
seven
days
and
we
can
show
everyone
the
outage
that
was
over
the
weekend
right,
so
so
this
period
right,
yeah
these.
C
These
were
the
temperatures
right
in
this
period
here,
where
we
were
just
operating
on
that
one
air
handler-
and
we
were
talking
about
these-
these
temperatures,
as
we
were
down
to
just
the
the
two
backup
air
handlers,
and
we
were
able
to
make
sure
that
that
this
common
area,
air
cooled
equipment
was
operating
correctly.
That's
exactly
right-
and
this
is
the
omni
system
that
was
still
operating
during
the
power
outage.
A
C
Yes,
exactly
right,
yeah,
that's
that's
an
example
and
that
this
this
grafana
here
is
an
example
of
one
of
the
oda
tools
that
that
we
use,
and,
for
example,
I
think
we
got
this
one
right
here.
This
is
this
is
where
I
can
view
the
actual
performance
of
all
my
air
handlers,
and
this
one
shows
me:
here's
the
cooling
distribution
units
for
pearl
motor,
I'm
able
to
see
the
inlet
water
temperature
and
the
outlet
water
temperature.
C
So
we
have
a
whole
host
of
instrumentation
that
allows
us
to
to
watch
the
the
performance
of
these
systems,
and
this
is
one
of
the
other
ones.
So
getting
back
to
the
slide
deck.
I
mentioned
that
that
we
have
the
capability
of
hvac
high
resolution
telemetry
as
well
as
hpc
high
resolution
telemetry,
and
I've
got
one
example
that
I'm
going
to
work
through
that
that
you
might
be
interested
in
this
is
corey
an
xc40,
crazy
system
and
it's
part
of
the
the
cascade
system.
C
This
is
something
that
users
may
not
have
known
about
corey.
It's
it's
a
combination.
It's
a
hybrid
system.
It
uses
cooling
water
for
70
to
80
percent
of
the
heat
extraction
from
the
processors
and
then
the
balance
of
that
at
20
to
30
percent
is
from
air
that
is
blown
through
all
of
the
compute
cabinets
in
a
cascade
fashion,
and
the
way
this
works
is
the.
This
is
why,
if
you've
ever
been
in
the
room
with
corey,
so
a
lot
of
people
like
to
use
earplugs,
it's
a
honking,
noisy
machine.
C
C
Speed
control
feature
where
it
would
monitor
the
processor
temperatures
in
the
row
and
if
there
is
a
one,
hot
spot
node
in
the
row,
with
a
with
a
processor
that
is
running
hotter
than
the
rest
and
for
every
five
degree
c
processor
temperature
up
change
up,
it
would
modulate
up
the
blower
fence
by
150
rpm
up
until
it
got
to
the
4k
maximum.
This
way
it
allowed
some
some
power
responsiveness
demand
on
these
blowers
up
and
down.
That
was
great.
C
It
provided
us
roughly
seven
percent
energy
savings
just
out
of
the
box,
but
because
we're
compressionless
and
our
water
temperature
kind
of
fluctuates
with
the
outside
air
temperature.
What
we
call
wet
bulb
that
dictates
how
the
cooling
towers,
how
cold
water
the
cooling
towers
can
make.
We
found
that
this
static,
cooling,
coil
exiting
air
temperature,
basically
the
the
servo
control
loop
that
says.
Okay,
this
cabinet
air
temperature
should
be
22
degrees.
C.
If
that's
a
static
set
point.
C
C
C
And
this
way
we
were
have
been
able
to
get
a
much
more
agile,
seasonal
performance
of
the
dynamic
fan,
speed
control
feature
in
in
quarry
and
we're
shaving
off
these
points.
Here.
These
represent
a
cooling
water
pump
energy,
and
these
are
really
really
big:
cooling,
water
pumps,
they're
125
horsepower
each,
and
so,
when
we're
circulating
all
this
water
with
those
cooling
water
pumps.
If
we
can,
you
know,
knock
those
cooling
water
pump
speeds
down
by
a
couple
percentage
points.
It
translates
into
some
some
energy
savings
in
a
small
way.
C
C
Delivery
demands
from
jobs
starting
up
in
these
huge
exoscale
systems
are
probably
gonna
demand,
even
more
interactive
communication
between
the
cooling
plants,
the
power
system
deliveries
and
the
hpc
system
within
an
exascale
system
that
starts
up
a
job
say:
that's
going
to
use
75
percent
of
the
system
and
it
may
all
of
a
sudden
in
the
blink
of
an
eye,
demand
five
megawatts
more
of
power,
and
indeed,
that
translates
into
cooling
demand
right
away.
A
cooling
plant
can't
respond
to
that
instantaneously.
C
It
is
going
to
need
some
sort
of
pre-learning
where
you
know
the
job.
Scheduler
says:
okay:
cooling
plant
get
ready,
10
minutes,
we're
gonna,
need
five
megawatts
worth
of
cooling
capability,
and
this
in
a
small
way
kind
of
represents
the
future
world
we're
moving
towards
for
power,
responsive
cooling
plants
just
enclosing
a
couple
words
on
what
the
building
infrastructure
group
does
for
energy
efficiency
in
the
future
for
perlmutter
and
now
nurse
tan.
C
We
interact
heavily
with
the
design
teams
in
making
sure
that
we
incorporate
energy
efficiency
concepts
in
the
actual
design
right
down
to
the
owner's
project
requirements,
as
well
as
all
of
the
the
review
during
construction
and
commissioning,
as
well
as
engaging
with
equipment.
Vendors
where
we
see
where
some
new
technologies
that
might
be
coming
from
from
manufacturers
might
be
four
or
five
years
out
that
we
could
benefit
from.
C
I
mentioned
earlier
in
the
talk
that
nurse
10
could
potentially
raise
our
water
evaporation
up
to
180
million
gallons
a
year
which
actually
exceeds
the
capacity
of
the
pumps
feeding
lawrence
berkeley
lab.
So
we
have
to
find
alternatives.
We
started
engaging
with
what
we
call
dry
coolers.
These
are
just
basically
huge
automotive.
C
Radiators
is
what
they
are:
they're
they're,
big
fans
that
that
blow
through
radiator
arrays
to
help
cool
down
a
closed
loop,
which
then
goes
into
the
cooling
plant.
Now
they
take
more
surface
area,
but
they
don't
evaporate
water
and
so
we're
engaging
with
with
those
manufacturers
to
see
if
we
can
use
that
technology
for
nurse
10
to
help
with
our
water
evaporation
other
nursing
activities
that
are
in
planning.
C
We
are
looking
at
machine
learning
methods
to
help
us
optimize
our
settings,
especially
with
the
the
balance
between
the
feed
of
the
cooling
tower
fans,
our
most
energy
intensive
component
in
our
cooling
systems
versus
the
pumps,
which
are
a
little
bit
more
efficient
right.
Now
we
are
kind
of
hitting
the
fans
more
and
the
settings
between
the
two
are
difficult
to
do
manually,
they're
kind
of
seasonal.
C
A
So
I
thought
that
was
really
interesting,
that
you
found
almost
like
a
law
of
unintended
consequences
there,
where
adjusting
the
fan,
speed
to
improve
the
efficiency
of
corey
kind
of
had
this
like
it
interactive
it
triggered
yeah.
It
treated
an
interaction
with
the
cooling
system
that
then
kind
of
undid,
the
good
that
the
fan
speed
was
doing,
and
so
by
sort
of
coupling
the
information
together.
Basically
you're
able
to
you
get
them
to
cooperate
instead
of
stepping
on
each
other's
work.
C
Yeah
it's
it's
like.
We
have
these
offsetting
savings,
so
we
got
savings
in
quarry,
but
then
we,
when
we
looked
at
it
holistically
with
the
second
order
effects
that
might
be
elsewhere-
and
this
is
very
common
in
building
science,
energy
efficiency,
all
the
time
they're
in
second
order
and
third
order,
interactive
effects,
we
it
looked
like
they
were
roughly
roughly
offsetting
now.
This
is
a
unique
situation
in
our
facility
and
and
every
facility
is
a
prototype.
C
We
are
also
site
specific,
a
lot
of
the
other
cray
xc
deployments
in
in
other
centers.
They
will
have
chillers,
meaning
their
chilled
water.
Loop
has
a
set
point
and
the
the
air
conditioning
equipment
make
sure
that
that
set
point
tightly
stays
within
a
tight
window,
so
they
wouldn't
have
had
this
issue,
because
that
chiller
is
just
going
to
be
using
whatever
energy
it.
They
maintain
that
cooling,
water
temperature.
C
So
in
that
situation
the
dynamic
fan,
speed,
control
out
of
the
box
with
with
the
cray
xc
system,
actually
works
very,
very
good,
but
in
a
situation
where
that
cooling,
water
temperature
diverges
a
lot
due
to
the
outside
conditions
that
static
set
point
cabinet
air
temperatures
that
point
does
get
into
a
situation.
This
was
a
very
agile
solution
and
the
the
the
csg
group
here
worked.
A
Yeah,
so
it's
never
quite
that
simple,
so
the
the
other
thing
that
you
reminded
me
and
and
either
norm
or
possibly
somebody
else
on
a
ss3
is
on
as
well.
I
can
can
clarify
so
for
for
nurse
users,
the
s
account
command,
has
a
output
option
where
you
can
get
consumed
energy
and
for
completed
jobs.
It
shows
a
value
and
I
think
that's
getting
the
energy
from
from
somewhere
in
the
omni
system.
Do
you
know
that
yeah.
B
C
It
that
so,
while
you're
looking
for
that
that
they
basically
for
our
power
numbers,
we've
got
several
flavors
of
them.
I
I
don't
think
my
slide.
Deck
actually
says
that
we've
got.
We've
got
master
meters,
which
we
call
ion
meters
that
that
we
use
for
the
campus
uses
for
our
total
power
consumed.
They
are
revenue
grade
and
highly
accurate
and
they're
at
the
substation
level,
and
then
the
sedc
meters
that
that
look
at
cory
alone
are.
A
B
One
thing
I
just
want
to
make
it
a
point
like
if
you
want
to
do
in-depth
analysis
or
use
that
that
number
may
not
always
be
accurate,
so
it's
okay
to
just
get
a
rough
estimate
of
what
you
are
using.
But
if
you
need
more
information,
I
think
you
can
get
in
touch
with
us
and
one
of
us
will
be
able
to
help
you
get
exactly
job
usage
values
yeah.
You
know
with
with
more
certainty.
C
Yeah
yeah
either
sweetheart
or
I
can
help
you
with
that
and
in
fact
we
we
helped
another
nurse
user.
Who
is
a
graduate
student
at
uc
berkeley
recently
in
that
regard,
that
the
number
does
not
capture
all
of
the
line,
losses
and
stuff.
Those
are
sedc
numbers
they're
on
board
numbers
from
chlorine
right.
So
so
there's
a.
C
A
Sounds
great
and
we'll
we'll
post
that
on
the
website
fairly
soon,
so
we
are
at
the
top
of
the
hour,
and
people
probably
need
to
head
to
the
next
thing,
just
very
quickly
rush
through
the
next
couple
of
items
so
coming
up.
Earcup
season
is
coming
up,
so
we'll
probably
aim
to
have
a
topic
of
the
day
around
ercap,
perhaps
for
the
august
webinar
always
interested
in
topic,
suggestions
and
requests,
and
especially
if
participants
would
like
to
show
off
their
work.