Java_the_good_bits.webm

Onto the second talk of the day.

Steve Capper is going to tell us
about the good bits of Java

They do exist

[Audience] Could this have been a
lightening talk? [Audience laughter]

Believe it or not we've got some
good stuff here.

I was as skeptical as you guys
when I first looked.

First many apologies for not attending
the mini-conf last year

I was unfortunately ill on the day
I was due to give this talk.

Let me figure out how to use a computer.

Sorry about this.

There we go; it's because
I've not woken up.

Last year I worked at Linaro in the
Enterprise group and we performed analysis

on so called 'Big Data' application sets.

As many of you know quite a lot of these
big data applications are written in Java.

I'm from ARM and we were very interested
in 64bit ARM support.

So this is mainly AArch64 examples
for things like assembler

but most of the messages are
pertinent for any architecture.

These good bits are shared between
most if not all the architectures.

Whilst trying to optimise a lot of
these big data applications

I stumbled a across quite a few things in
the JVM and I thought

'actually that's really clever;
that's really cool'

So I thought that would make a good
basis for an interesting talk.

This talk is essentially some of the
clever things I found in the

Java Virtual Machine; these
optimisations are in OpenJDK.

Source is available it's all there,
readily available and in play now.

I'm going to finish with some of the
optimisation work we did with Java.

People who know me will know
I'm not a Java zealot.

I don't particularly believe in
programming in a language over another one

So to make it clear from the outset
I'm not attempting to convert

anyone to Java programmers.

I'm just going to highlight a few salient
things in the Java Virtual Machine

which I found to be quite clever and
interesting

and I'll try and talk through them
with my understanding of them.

Let's jump straight in and let's
start with an example.

This is a minimal example for
computing a SHA1 sum of a file.

I've omitted some of the checking in the
beginning of the function see when

command line parsing and that sort of
thing.

I've highlighted the salient
points in red.

Essentially we instantiate a SHA1
crypto message service digest.

And we do the equivalent in
Java of an mmap.

Get it all in memory.

And then we just put this status straight
into the crypto engine.

And eventually at the end of the
program we'll spit out the SHA1 hash.

It's a very simple program.

It's basically mmap, SHA1, output
the hash afterwards.

In order to concentrate on the CPU
aspect rather than worry about IO

I decided to cheat a little bit by
setting this up.

I decided to use a sparse file. As many of
you know a sparse file is a file that not

all the contents are stored necessarily
on disc. The assumption is that the bits

that aren't stored are zero. For instance
on Linux you can create a 20TB sparse file

on a 10MB file system and use it as
normal.

Just don't write too much to it otherwise
you're going to run out of space.

The idea behind using a sparse file is I'm
just focusing on the computational aspects

of the SHA1 sum. I'm not worried about
the file system or anything like that.

I don't want to worry about the IO. I
just want to focus on the actual compute.

In order to set up a sparse file I used
the following runes.

The important point is that you seek
and the other important point

is you set a count otherwise you'll
fill your disc up.

I decided to run this against firstly
let's get the native SHA1 sum command

that's built into Linux and let's
normalise these results and say that's 1.0

I used an older version of the OpenJDK
and ran the Java program

and that's 1.09 times slower than the
reference command. That's quite good.

Then I used the new OpenJDK, this is now
the current JDK as this is a year on.

And 0.21 taken. It's significantly faster.

I've stressed that I've done nothing
surreptitious in the Java program.

It is mmap, compute, spit result out.

But the OpenJDK has essentially got
some more context information.

I'll talk about that as we go through.

Before when I started Java I had a very
simplistic view of Java.

Traditionally Java is taught as a virtual
machine that runs bytecode.

Now when you compile a Java program it
compiles into bytecode.

The older versions of the Java Virtual
Machine would interpret this bytecode

and then run through. Newer versions
would employ a just-in-time engine

and try and compile this bytecode
into native machine code.

That is not the only thing that goes on
when you run a Java program.

There is some extra optimisations as well.
So this alone would not account for

the newer version of the SHA1
sum being significantly faster

than the distro supplied one.

Java knows about context. It has a class
library and these class libraries

have reasonably well defined purposes.

We have classes that provide
crypto services.

We have some misc unsafe that every
single project seems to pull in their

project when they're not supposed to.

These have well defined meanings.

These do not necessarily have to be
written in Java.

They come as Java classes,
they come supplied.

But most JVMs now have a notion
of a virtual machine intrinsic.

And the virtual machine intrinsic says ok
please do a SHA1 in the best possible way

that your implementation allows. This is
something done automatically by the JVM.

You don't ask for it. If the JVM knows
what it's running on and it's reasonably

recent this will just happen
for you for free.

And there's quite a few classes
that do this.

There's quite a few clever things with
atomics, there's crypto,

there's mathematical routines as well.
Most of these routines in the

class library have a well defined notion
of a virtual machine intrinsic

and they do run reasonably optimally.

They are a subject of continuous
optimisation as well.

We've got some runes that are
presented on the slides here.

These are quite useful if you
are interested in

how these intrinsics are made.

You can ask the JVM to print out a lot of
the just-in-time compiled code.

You can ask the JVM to print out the
native methods as well as these intrinsics

and in this particular case after sifting
through about 5MB of text

I've come across this particular SHA1 sum
implementation.

This is AArch64. This is employing the
cryptographic extensions

in the architecture.

So it's essentially using the CPU
instructions which would explain why

it's faster. But again it's done
all this automatically.

This did not require any specific runes
or anything to activate.

We'll see a bit later on how you can
more easily find the hot spots

rather than sifting through a lot
of assembler.

I've mentioned that the cryptographic
engine is employed and again

this routine was generated at run
time as well.

This is one of the important things about
certain execution of amps like Java.

You don't have to know everything at
compile time.

You know a lot more information at
run time and you can use that

in theory to optimise.

You can switch off these clever routines.

For instance I've got a deactivate
here and we get back to the

slower performance we expected.

Again, this particular set of routines is
present in OpenJDK,

I think for all the architectures that
support it.

We get this optimisation for free on X86
and others as well.

It works quite well.

That was one surprise I came across
as the instrinsics.

One thing I thought it would be quite
good to do would be to go through

a slightly more complicated example.
And use this example to explain

a lot of other things that happen
in the JVM as well.

I will spend a bit of time going through
this example

and explain roughly the notion of what
it's supposed to be doing.

This is an imaginary method that I've
contrived to demonstrate a lot of points

in the fewest possible lines of code.

I'll start with what it's meant to do.

This is meant to be a routine that gets a
reference to something and let's you know

whether or not it's an image and in a
hypothetical cache.

I'll start with the important thing
here the weak reference.

In Java and other garbage collected
languages we have the notion of references

Most of the time when you are running a
Java program you have something like a

variable name and that is in the current
execution context that is referred to as a

strong reference to the object. In other
words I can see it. I am using it.

Please don't get rid of it.
Bad things will happen if you do.

So the garbage collector knows
not to get rid of it.

In Java and other languages you also
have the notion of a weak reference.

This is essentially the programmer saying
to the virtual machine

"Look I kinda care about this but
just a little bit."

"If you want to get rid of it feel free
to but please let me know."

This is why this is for a CacheClass.
For instance the JVM in this particular

case could decide that it's running quite
low on memory this particular xMB image

has not been used for a while it can
garbage collect it.

The important thing is how we go about
expressing this in the language.

We can't just have a reference to the
object because that's a strong reference

and the JVM will know it can't get
rid of this because the program

can see it actively.

So we have a level of indirection which
is known as a weak reference.

We have this hypothetical CacheClass
that I've devised.

At this point it is a weak reference.

Then we get it. This is calling the weak
reference routine.

Now it becomes a strong reference so
it's not going to be garbage collected.

When we get to the return path it becomes
a weak reference again

because our strong reference
has disappeared.

The salient points in this example are:

We're employing a method to get
a reference.

We're checking an item to see if
it's null.

So let's say that the JVM decided to
garbage collect this

before we executed the method.

The weak reference class is still valid
because we've got a strong reference to it

but the actual object behind this is gone.

If we're too late and the garbage
collector has killed it

it will be null and we return.

So it's a level of indirection to see
does this still exist

if so can I please have it and then
operate on it as normal

and then return becomes weak
reference again.

This example program is quite useful when
we look at how it's implemented in the JVM

and we'll go through a few things now.

First off we'll go through the bytecode.

The only point of this slide is to
show it's roughly

the same as this.

We get our variable.

We use our getter.

This bit is extra this checkcast.
The reason that bit is extra is

because we're using the equivalent of
a template in Java.

And the way that's implemented in Java is
it just basically casts everything to an

object so that requires extra
compiler information.

And this is the extra check.

The rest of this we load the reference,
we check to see if it is null,

If it's not null we invoke a virtual
function - is it the image?

and we return as normal.

Essentially the point I'm trying to make
is when we compile this to bytecode

this execution happens.

This null check happens.

This execution happens.

And we return.

In the actual Java class files we've not
lost anything.

This is what it looks like when it's
been JIT'd.

Now we've lost lots of things.

The JIT has done quite a few clever things
which I'll talk about.

First off if we look down here there's
a single branch here.

And this is only if our check cast failed

We've got comments on the
right hand side.

Our get method has been inlined so
we're no longer calling.

We seem to have lost our null check,
that's just gone.

And again we've got a get field as well.

That's no longer a method,
that's been inlined as well.

We've also got some other cute things.

Those more familiar with AArch64
will understand

that the pointers we're using
are 32bit not 64bit.

What we're doing is getting a pointer
and shifting it left 3

and widening it to a 64bit pointer.

We've also got 32bit pointers on a
64bit system as well.

So that's saving a reasonable amount
of memory and cache.

To summarise. We don't have any
branches or function calls

and we've got a lot of inlining.

We did have function calls in the
class file so it's the JVM;

it's the JIT that has done this.

We've got no null checks either and I'm
going to talk through this now.

The null check elimination is quite a
clever feature in Java and other programs.

The idea behind null check elimination is

most of the time this object is not
going to be null.

If this object is null the operating
system knows this quite quickly.

So if you try to dereference a null
pointer you'll get either a SIGSEGV or

a SIGBUS depending on a
few circumstances.

That goes straight back to the JVM

and the JVM knows where the null
exception took place.

Because it knows where it took
place it can look this up

and unwind it as part of an exception.

Those null checks just go.
Completely gone.

Most of the time this works and you are
saving a reasonable amount of execution.

I'll talk about when it doesn't work
in a second.

That's reasonably clever. We have similar
programming techniques in other places

even the Linux kernel for instance when
you copy data to and from user space

it does pretty much identical
the same thing.

It has an exception unwind table and it
knows if it catches a page fault on

this particular program counter
it can deal with it because it knows

the program counter and it knows
conceptually what it was doing.

In a similar way the JIT know what its
doing to a reasonable degree.

It can handle the null check elimination.

I mentioned the sneaky one. We've got
essentially 32bit pointers

on a 64bit system.

Most of the time in Java people typically
specify heap size smaller than 32GB.

Which is perfect if you want to use 32bit
pointers and left shift 3.

Because that gives you 32GB of
addressable memory.

That's a significant memory saving because
otherwise a lot of things would double up.

There's a significant number of pointers
in Java.

The one that should make people
jump out of their seat is

the fact that most methods in Java are
actually virtual.

So what the JVM has actually done is
inlined a virtual function.

A virtual function is essentially a
function were you don't know where

you're going until run time.

You can have several different classes
and they share the same virtual function

in the base class and dependent upon
which specific class you're running

different virtual functions will
get executed.

In C++ that will be a read from a V table
and then you know where to go.

The JVM's inlined it.

We've saved a memory load.

We've saved a branch as well

The reason the JVM can inline it is
because the JVM knows

every single class that has been loaded.

So it knows that although this looks
polymorphic to the casual programmer

It actually is monomorphic.
The JVM knows this.

Because it knows this it can be clever.
And this is really clever.

That's a significant cost saving.

This is all great. I've already mentioned
the null check elimination.

We're taking a signal as most of you know
if we do that a lot it's going to be slow.

Jumping into kernel, into user,
bouncing around.

The JVM also has a notion of
'OK I've been a bit too clever now;

I need to back off a bit'

Also there's nothing stopping the user
loading more classes

and rendering the monomorphic
assumption invalid.

So the JVM needs to have a notion of
backpeddling and go

'Ok I've gone to far and need to
deoptimise'

The JVM has the ability to deoptimise.

In other words it essentially knows that
for certain code paths everything's OK.

But for certain new objects it can't get
away with these tricks.

By the time the new objects are executed
they are going to be safe.

There are ramifications for this.
This is the important thing to consider

with something like Java and other
languages and other virtual machines.

If you're trying to profile this it means
there is a very significant ramification.

You can have the same class and
method JIT'd multiple ways

and executed at the same time.

So if you're trying to find a hot spot
the program counter's nodding off.

Because you can refer to the same thing
in several different ways.

This is quite common as well as
deoptimisation does take place.

That's something to bear in mind with JVM
and similar runtime environments.

You can get a notion of what the JVM's
trying to do.

You can ask it nicely and add a print
compilation option

and it will tell you what it's doing.

This is reasonably verbose.

Typically what happens is the JVM gets
excited JIT'ing everything

and optimising everything then
it settles down.

Until you load something new
and it gets excited again.

There's a lot of logs. This is mainly
useful for debugging but

it gives you an appreciation that it's
doing a lot of work.

You can go even further with a log
compilation option.

That produces a lot of XML and that is
useful for people debugging the JVM as well.

It's quite handy to get an idea of
what's going on.

If that is not enough information you
also have the ability to go even further.

This is beyond the limit of my
understanding.

I've gone into this little bit just to
show you what can be done.

You have release builds of OpenJDK
and they have debug builds of OpenJDK.

The release builds will by default turn
off a lot of the diagnostic options.

You can switch them back on again.

When you do you can also gain insight
into the actual, it's colloquially

referred to as the C2 JIT,
the compiler there.

You can see, for instance, objects in
timelines and visualize them

as they're being optimised at various
stages and various things.

So this is based on a masters thesis
by Thomas Würthinger.

This is something you can play with as
well and see how far the optimiser goes.

And it's also good for people hacking
with the JVM.

I'll move onto some stuff we did.

Last year we were working on the
big data. Relatively new architecture

ARM64, it's called AArch64 in OpenJDK
land but ARM64 in Debian land.

We were a bit concerned because
everything's all shiny and new.

Has it been optimised correctly?

Are there any obvious things
we need to optimise?

And we're also interested because
everything was so shiny and new

in the whole system.

Not just the JVM but the glibc and
the kernel as well.

So how do we get a view of all of this?

I gave a quick talk before at the Debian
mini-conf before last [2014] about perf

so decided we could try and do some
clever things with Linux perf

and see if we could get some actual useful
debugging information out.

We have the flame graphs that are quite
well known.

We also have some previous work, Johannes
had a special perf map agent that

could basically hook into perf and it
would give you a nice way of running

perf-top for want of a better expression
and viewing the top Java function names.

This is really good work and it's really
good for a particular use case

if you just want to do a quick snap shot
once and see in that snap shot

where the hotspots where.

For a prolonged work load with all
the functions being JIT'd multiple ways

with the optimisation going on and
everything moving around

it require a little bit more information
to be captured.

I decided to do a little bit of work on a
very similar thing to perf-map-agent

but an agent that would capture it over
a prolonged period of time.

Here's an example Flame graph, these are
all over the internet.

This is the SHA1 computation example that
I gave at the beginning.

As expected the VM intrinsic SHA1 is the
top one.

Not expected by me was this quite
significant chunk of CPU execution time.

And there was a significant amount of
time being spent copying memory

from the mmapped memory
region into a heap

and then that was passed to
the crypto engine.

So we're doing a ton of memory copies for
no good reason.

That essentially highlighted an example.

That was an assumption I made about Java
to begin with which was if you do

the equivalent of mmap it should just
work like mmap right?

You should just be able to address the
memory. That is not the case.

If you've got a file mapping object and
you try to address it it has to be copied

into safe heap memory first. And that is
what was slowing down the programs.

If that was omitted you could make
the SHA1 computation even quicker.

So that would be the logical target you
would want to optimise.

I wanted to extend Johannes' work
with something called a

Java Virtual Machine Tools Interface
profiling agent.

This is part of the Java Virtual Machine
standard as you can make a special library

and then hook this into the JVM.

And the JVM can expose quite a few
things to the library.

It exposes a reasonable amount of
information as well.

Perf as well has the ability to look
at map files natively.

If you are profiling JavaScript, or
something similar, I think the

Google V8 JavaScript engine will write
out a special map file that says

these program counter addresses correspond
to these function names.

I decided to use that in a similar way to
what Johannes did for the extended

profiling agent but I also decided to
capture some more information as well.

I decided to capture the disassembly
so when we run perf annotate

we can see the actual JVM bytecode
in our annotation.

We can see how it was JIT'd at the
time when it was JIT'd.

We can see where the hotspots where.

And that's good. But we can go
even better.

We can run an annotated trace that
contains the Java class,

the Java method and the bytecode all in
one place at the same time.

You can see everything from the JVM
at the same place.

This works reasonably well because the
perf interface is extremely extensible.

And again we can do entire
system optimisation.

The bits in red here are the Linux kernel.

Then we got into libraries.

And then we got into Java and more
libraries as well.

So we can see everything from top to
bottom in one fell swoop.

This is just a quick slide showing the
mechanisms employed.

Essentially we have this agent which is
a shared object file.

And this will spit out useful files here
in a standard way.

And the Linux perf basically just records
the perf data dump file as normal.

We have 2 sets of recording going on.

To report it it's very easy to do
normal reporting with the PID map.

This is just out of the box, works with
the Google V8 engine as well.

If you want to do very clever annotations
perf has the ability to have

Python scripts passed to it.

So you can craft quite a dodgy Python
script and that can interface

with the perf annotation output.

That's how I was able to get the extra
Java information in the same annotation.

And this is really easy to do; it's quite
easy to knock the script up.

And again the only thing we do for this
profiling is we hook in the profiling

agent which dumps out various things.

We preserve the frame pointer because
that makes things considerably easier

on winding. This will effect
performance a little bit.

And again when we're reporting we just
hook in a Python script.

It's really easy to hook everything in
and get it working.

At the moment we have a JVMTI agent. It's
actually on http://git.linaro.org now.

Since I gave this talk Google have
extended perf anyway so it will do

quite a lot of similar things out of the
box anyway.

It's worth having a look at the
latest perf.

These techniques in this slide deck can be
used obviously in other JITs quite easily.

The fact that perf is so easy to extend
with scripts can be useful

for other things.

And OpenJDK has a significant amount of
cleverness associated with it that

I thought was very surprising and good.
So that's what I covered in the talk.

These are basically references to things
like command line arguments

and the Flame graphs and stuff like that.

If anyone is interested in playing with
OpenJDK on ARM64 I'd suggest going here:

http://openjdk.linaro.org
Where the most recent builds are.

Obviously fixes are going in upstream and
they're going into distributions as well.

They're included in OpenJDK so it should
be good as well.

I've run through quite a few fundamental
things reasonably quickly.

I'd be happy to accept any questions
or comments

And if you want to talk to me privately
about Java afterwards feel free to

when no-one's looking.

[Audience] Applause

[Audience] It's not really a question so
much as a comment.

Last mini-Deb conf we had a talk about
using the JVM with other languages.

And it seems to me that all this would
apply even if you hate Java programming

language and want to write in, I don't
know, lisp or something instead

if you've got a lisp system that can
generate JVM bytecode.

[Presenter] Yeah, totally. And the other
big data language we looked at was Scala.

It uses the JVM back end but a completely
different language on the front.

Cheers guys.