Winning With Java at Market Open

©2013 Azul Systems, Inc.

Winning with Java

At Market Open

Getting Fast & Staying Fast

Gil Tene, CTO & co-Founder, Azul Systems


Are you fast at Market Open?


Java at Market Open
. . .
Market Open


Java’s “Just In Time” Reality


Starts slow, learns fast


Lazy loading & initialization


Aggressively optimized for
the common case


the common case
(temporarily) Reverts to
slower execution to adapt


the common case
. . .


the common case
Warmup
. . .


the common case
Warmup
Deoptimization
. . .

Speculative compiler tricks
JIT compilers can do things that
static compilers can have
a hard time with…


Untaken path example


“Never taken” paths can be optimized away with beneﬁts:
void computeMagnitude(int val) {
if (val > 10) {
bias = computeBias(val);
else {
bias = 1;
}
return Math.log10(bias + 99);
}


“Never taken” paths can be optimized away with beneﬁts:
if (val > 10) {
bias = computeBias(val);
else {
bias = 1;
}
return Math.log10(bias + 99);
}
When all values so far were <= 10 , can be optimized to:
if (val > 10) uncommonTrap();
return 2;
}


Implicit Null Check example


All ﬁeld and array access in Java is null checked
x = foo.x;
is (in equivalent required machine code):
if (foo == null)
throw new NullPointerException();
x = foo.x;


x = foo.x;
if (foo == null)
x = foo.x;
But compiler can “hope” for non-nulls, and handle SEGV
<Point where later SEGV will appear to throw>
x = foo.x;


x = foo.x;
if (foo == null)
x = foo.x;
But compiler can “hope” for non-nulls, and handle SEGV
<Point where later SEGV will appear to throw>
x = foo.x;
This is faster *IF* no nulls are encountered…


Deoptimization:

Adaptive compilation is… adaptive


Deoptimization:

JIT Compilers are adaptive and speculative


Deoptimization:

Leverage observations of behavior


Deoptimization:

Can “hope” about things and can recover if wrong


Deoptimization:

Warming up code is a black art


Deoptimization:

Running code long enough to compile is just the start…


Deoptimization:

Deoptimization can occur at any time


Deoptimization:

often occur after you *think* the code is warmed up.


Deoptimization:

often occur after you *think* the code is warmed up.
Deoptimization has many potential causes


Warmup often doesn’t cut it…


Common Example:

Trading system wants to have the ﬁrst trade be fast

So run 20,000 “fake” messages through the system to warm up

let JIT compilers optimize, learn, and deopt before actual trades


Common Example:

Trading system wants to have the ﬁrst trade be fast

So run 20,000 “fake” messages through the system to warm up

let JIT compilers optimize, learn, and deopt before actual trades
What really happens

Code is written to do different things “if this is a fake message”

e.g. “Don’t send to the exchange if this is a fake message”

JITs optimize for fake path, including speculatively assuming “fake”

First real message through deopts...


A few example causes for
depotimization


depotimization

Newly encountered code breaking prior assumptions


depotimization

First calls to method in an uninitialized class


depotimization

First call to a method in a not-yet-loaded class


depotimization

Dynamic branch elimination hitting an unexpected path


depotimization

Dynamic branch elimination hitting an unexpected path
Implicit Null Check hitting a null




the common case

Warmup
Deoptimization
. . .




the common case

What we have




the common case

What we have What we want




the common case

No Slow Ops or Trades




the common case

No Slow Ops or Trades
ReadyNow!
to the rescue


What can we do about it?


Azul’s Zing JVM has a feature set called “ReadyNow!”

Avoids deoptimization while keeping code fast

Allows code to reach optimized levels without requiring “exercise”




Learning and retaining optimizations

With Zing, your next run can learn from the previous one




Learning and retaining optimizations

With Zing, your next run can learn from the previous one
Fine grained control options

Per method compiler directives

…


Logging & “replaying”optimizations


Zing’s ReadyNow includes optimization logging

Records ongoing optimization decisions and stats

records optimization dependencies

Establishes “stable optimization state” at end of previous run





Zing can then read prior logs at startup

Prime JVM with knowledge of prior stable optimizations

Optimizations are applied as their dependencies get resolved





Zing can then read prior logs at startup

Prime JVM with knowledge of prior stable optimizations

Optimizations are applied as their dependencies get resolved
ReadyNow workﬂow promotes conﬁdence

You’ll know if/when all optimizations have been applied

If some optimization haven’t been applied, you’ll know why…


Java at “Load Start”
. . .
Load Start


. . .
Load Start
Deoptimization


. . .
Load Start
Deoptimization
ReadyNow!
avoids
deoptimization


. . .
Load Start
With Zing & ReadyNow!


. . .
Load Start
With ReadyNow!
Warmup?


. . .
Load Start
With ReadyNow!
Warmup?
Fast From
The Start


. . .
Load Start
With ReadyNow! and
Start Fast & Stay Fast


. . .
Load Start


. . .
Load Start
With
Confidence


One liner takeaway
. . .
Load Start


One liner takeaway
. . .
Zing: A cure for the Java hiccups
Load Start

Winning With Java at Market Open

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