(java2days) Is the Future of Java Cloudy?

© 2015 INTERNATIONAL BUSINESS MACHINES CORPORATION
Java 2 Days
IS THE FUTURE OF
JAVA CLOUDY?
Steve Poole
Developer Advocate
IBM Runtime Technologies
@spoole167
Oct 2017

1992: A momentous year
I joined IBM and took my tie off
Hired because I had deep
knowledge of AS/400
Do you remember when skillsets
were platform oriented?

A few years later I’m sitting
in a Sun office in Cupertino
talking about garbage
collection design
Java is already the platform

’K’ Memory ‘T’
‘midlets’ Display ‘Headless’
For most of 20 years the answer is Java
‘toaster’ Device ‘Mainframe’
‘arm’ Architecture ‘s390’
‘palm’ OS ‘OS390’

Over the last few years
Java’s dominance has
faltered

Partly because squeezing a JVM into
small devices is hard (and we cut
corners)
Partly because Java is seen as ‘old’

Mostly because Java hasn’t evolved
fast enough.
It took 5 years to get Lambdas.
It took 10 years to get Modules

The new requirements of Cloud have
arrived even faster

Is Java going the way of the dodo?
Is James Goslings vision of Java on
every toaster… Toast?

For Java to compete and remain
relevant it has to be the platform of
choice (again)
In three arenas:
Cloud
Data Analytics
Machine Learning

And it has to be selectively better
than the challengers
Node
Swift
Go
Python
Ruby

Let’s have a few races

N-Body benchmark
• 1 CPU
• Model the orbits of Jovian planets,
using the same simple symplectic-
integrator.
• Lots of number crunching
• Quickest to the answer wins
https://en.wikipedia.org/wiki/Outer_planets#/media/File:Gas_giants_in_the_solar_system.jpg

0 5 10 15 20 25 30
Node
Swift
Java
Go
Python3
Ruby

Language seconds
Go 21.47
Swift 21.96
Java 22.56
Node 28.74
Ruby 12 mins
Python3 13 mins

Related statistics (python and ruby times removed)
0
5
10
15
20
25
30
35
40
CPU time (secs) Memory (k)
Java Swift Node Go Python3 Ruby

mandelbrot
• Plot the Mandelbrot set [-1.5-i,0.5+i] on an
16000 -by- 16000 bitmap. Write output
byte-by-byte in portable bitmap format.

Language Score
Swift 3.32
Go 5.46
Java 6.08
Node 19.04
Python3 273.43
Ruby 420

Related statistics (python and ruby times removed)
0
50
100
150
200
250
300
350

Mandlebrot: CPU usage
0
10
20
30
40
50
60
70
80
90
100
CPU %
CPU1 CPU2 CPU3 CPU4

binary tree
A simplistic adaptation of Hans
Boehm's GCBench, which in turn
was adapted from a benchmark by
John Ellis and Pete Kovac.

Language Score
Swift 4.96
Java 8.58
Go 35.18
Node 53.64
Ruby 54.24
Python3 86.1

Related statistics
0
200
400
600
800
1000
1200

Binary Tree: CPU usage
0
10
20
30
40
50
60
70
80
90
100
CPU %
CPU1 CPU2 CPU3 CPU4

Regex-redux
same simple regex patterns and actions
to manipulate FASTA format data
agggtaaa|tttaccct [cgt]gggtaaa|tttaccc[acg]
a[act]ggtaaa|tttacc[agt]t ag[act]gtaaa|tttac[agt]ct
agg[act]taaa|ttta[agt]cct aggg[acg]aaa|ttt[cgt]ccct
agggt[cgt]aa|tt[acg]accct agggta[cgt]a|t[acg]taccct
agggtaa[cgt]|[acg]ttaccct
64-bit Ubuntu quad core java 9 Java(TM) SE Runtime
Environment (build 9+181) Java HotSpot(TM) 64-Bit Server
VM (build 9+181, mixed mode)

Language Score
Node 4.4
Java 10.38
Python3 14.86
Ruby 28.8
Go 29.29
Swift 75.47

Related statistics
0
100
200
300
400
500
600
700

Regex-redux: CPU usage
0
10
20
30
40
50
60
70
80
90
100
CPU %
CPU1 CPU2 CPU3 CPU4

benchmarksgame.alioth.debian.org
‘100 line’ benchmarks
“Non-motivation: We are
profoundly uninterested in claims
that these measurements, of a
few tiny programs,
somehow define the relative
performance of programming
languages.”

Thoughts
Micro benchmarks are fun
But not to be taken
completely seriously
But there are trends …

Node.js is winning the cloud
IO space.
Non blocking workloads run
best with Node.
But don’t do compute
intensive
activities with it

Swift is strong contender for
memory constrained devices
and arenas (like iOS or
Cloud)
But its limited in platform
reach
And does scale as well as

Go looks impressive but
its much more aligned to
C arenas
Maybe one day JVMs will
have Go in them..

Ruby isn’t a challenger
to Java – it’s more
comparable with Node.
And its losing out.
(but I still love it)

Python: why is this a
language of choice for
data scientists and
machine
learning?
Because it has a wealth
of native libraries

IBM is investing in these
runtimes because between
them they cover all the bases –
developer communities and
technical capabilities

But what about
Data Analytics Machine Learning

4
3
Scripting Language Modern Native LanguageRuntime Language

4
4
Type Safe Type Safe, with InferenceDynamically Typed
Type Safe means JIT’s or compilers can optimise code
significantly better than a dynamically type language
Since an int is always an int
In Javascript a int is a thing until the very last minute

4
5
Bytecode: JIT Compiled JIT Compiled Pre-Compiled
JIT compilers can optimize as the workload changes.
Pre compiled code can’t do that

4
6
Garbage Collected Garbage Collected Reference Counted
GC can run in the background at the cost of a buffer of memory. Can be
parallelized more effectively and more accurately (no circular
references)

4
7
Concurrent Threaded Single Thread Concurrent Work Pool
Single threaded means no locking or syncronisation needed. But CPU
workload is a major Achilles heel

4
8
Concurrent Threaded Single Thread Concurrent Work Pool
All Platforms All Platforms Apple Platforms and Linux

4
9
Runtime Language
Type Safe
Bytecode: JIT Compiled
Garbage Collected
Concurrent Threaded
All Platforms
These characteristics let us
take Java anywhere.
No other runtime
environment comes close

Our cunning plan ™

predictable
consistent
cadence
easier migration increased
innovation
Recent changes are a sign of the ecosystem
positioning for a faster pace

Every
development team
has both common
and unique
problems to solve.
Open source is
key to fast
innovation and
adoption
OpenJDK
Eclipse OpenJ9
Open Liberty
Eclipse MicroProfile
Java EE
IBM Cloud
Docker
Kubernetes
Everyone can engage in the future of Java
development.
Why, and How ?

lambda
streams
modules
reactive streams
panama
valhalla
penrose
amber
Giving Java innovation a faster cadence

Tomcat
Glassfish
Open Liberty
OpenJDK +
Hotspot
OpenJDK +
OpenJ9
J2EE
Micro-profile
And a variety of implementations to choose
from

We’re going to take Java to places
its never been before.
And you’re coming too.

Where code goes,
where data flows,
cognition will follow.

CONSIDER:
Cognitive systems can understand the
world through sensing and interaction,
reason using hypotheses and arguments
and learn from experts and through data.
Watson is the most advanced such system.
Today, businesses in
countries across.
There are
Watson ecosystem
partner companies,
with
78%
of business and IT
executives believe
that successful business
will manage employees
alongside intelligent
machines.
On average there are
Among C-Suite executives
familiar with cognitive computing:
96%
84%
94%
89%
in insurance intend to invest in
cognitive capabilities.
in healthcare believe it will play a
disruptive role in the industry, and
60% believe they lack the skilled
professionals and technical
experience to achieve it.
in retail intend to invest in
cognitive capabilities.
in telecommunications believe it
will have a critical impact on the
future of their business.
36
17industries are
applying cognitive
technologies.
350+
100of those have taken their
product to market.
1.3BWatson API calls a month
and growing.

We can now confer on every
digitized object, product,
process and service a kind
of thinking ability.
How, and why now?
Data is
transforming
industries and
professions.
The world
is being
reinvented
in code.
Computing is
entering a new
Cognitive Era.

Worried yet?
Cloud: Makes you think differently
but mostly about footprint, resilience
and scaling
Modularity, Lambdas
J2EE going to Eclipse
That’s not really a big change.
How do you do things like…

Imagine create systems that can
Tailoring responses to the
personalities of your customers
without meeting a single one of
them.
Knowing the latest, most
significant developments in your
profession or industry the moment
they take place
Products and services that
improve themselves over time,
learning from and adapting to the
world around them.
Processes that identify their own
inefficiencies-and address them
automatically-in real time.
Uncovering patterns, resources,
trends and other competitive
advantages invisible to competitors
and their information systems.

The biggest challenge for all of us is
learning to solve problems differently

PROBLEM:
…so lets solve a word search
java
ibm
cognitive
a e k j c b a o a j
e j a x l o n d od n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m

a e k j c b a o a j
e j a x l o n d
o
d
n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m
CLASSIC
SOLUTION:
java
ibm
cognitive

a e k j c b a o a j
e j a x l o n d
o
d
n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m
PARTITIONING:
java
ibm
cognitive
Executor
node
Executor
node
Executor
node
Executor
node

a e k j c b a o a j
e j a x l o n d
o
d
n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m
PARTITIONING: Executor
node
Executor
node
Executor
node
Executor
node
CPU
java
ibm
cognitive

a e k j c b a o a j
e j a x l o n d
o
d
n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m
node
Executor
node
Executor
node
Executor
node
CPU
GPU
java
ibm
cognitive

a e k j c b a o a j
e j a x l o n d
o
d
n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m
node
Executor
node
Executor
node
Executor
node
CPU
GPU
FPGA
ASIC
java
ibm
cognitive

IBM + NVIDIA
Improving Java application
performance with GPU
exploitation is available in IBM
SDK for Java 8 and OpenJDK 9
with Eclipse Open9
Standard SE API optimisation as
well as CUDA4J API for explicit
low level control
+

GPU’s don’t work like CPU’s
They want their data in different forms
They behave differently
You’ll have to think differently too

NEURAL:
a e k j c b a o a j
e j a x l o n d
o
d
n
v n a v a a j v i o
i c
m
e
i e t u p a a
t d a t a j a j k l
i t w i q a y a m h
n u e n x v d v g a
g o d g t f f a u i
o a s o g a g t v c
c i a c h j a i b m
java
ibm
cognitive

SYNAPSE:
A program to develop a
neuromorphic processor that is a
new kind of cognitive computer
Designed to simulate the neurones
and dendrites of the brain for low
power efficient operation

Event driven, Non Von Neumann Neural Network.
Neural Nets want their data in different forms
They behave differently
You’ll have to think differently too

Now it gets even stranger..

SEARCH

SEARCH
In 1996 a search algorithm was
defined by Lov Grover. This
algorithm can transform the
problem into an O(√N) search.
On a traditional computer this
search problem is solved in no
fewer than O(N) evaluations

SEARCH
In 1996 a search algorithm was
defined by Lov Grover. This
algorithm can transform the
problem into an O(√N) search.
On a traditional computer this
search problem is solved in no
fewer than O(N) evaluations
qubitbit
1
0

QUANTUM:
java
ibm
cognitive
a e k j c b a o a j
l g m e e t u p a p
l n a v a a j v i o
p c
m
e
i e t u p a a
r d a t a j a j k l
u t w i q a y a m h
d u e n x v d v g a
j o d g t f f a u i
a a s o g a g t v c
z i a c h j a i b m
www.research.ibm.com/ibm-q/

Quantum Computers can and will solve linear equations
break cryptographic systems or model new medicines
In times that are a fraction of what existing computers can
achieve now.
Word searches are easy. But you present the data differently
and get a statistical response.
No more 0&1’s

You can play battleships with QC’s
But damage is not HIT or MISS
It’s +/- % hit
Maybe you sunk my battleship.
Maybe you didn’t

The JVM design means that we can easily
imagine running Java on new forms of processors
It may not end up being todays Java but it will be close
The challenge is that you have to change how you think
about problems.

OPPORTUNITY:
Data flows from every device,
replacing guessing and
approximations with precise
information. Yet 80% of this
data is unstructured; therefore,
invisible to computers and of
limited use to business.
HEALTHCARE DATA GOVERNMENT & EDUCATION DATA
99% 88% 94% 84%
Healthcare data comes from
sources such as:
Government & education data
comes from sources such as:
Patient
Sensors
Electronic
Medical
Records
Test
Results
Vehicle Fleet
Sensors
Traffic
Sensors
Student
Evaluations
UTILITIES DATA MEDIA DATA
93% 84% 97% 82%
Utilities data comes from sources
such as:
Media data comes from sources
such as:
Utility
Sensors
Employee
Sensors
Location
Data
Video
and Film
Images Audio
By 2020,
of new information will
be created every
second for every
human being on
the planet.
growth by 2017 unstructured growth by 2017 unstructured
1.7 MB growth by 2017 unstructured growth by 2017 unstructured

The world is
being reinvented
in code. Java code.
HOW, AND WHY NOW?

Computing is
entering a new
cognitive era.
What do you Think
when you solve a
problem?
HOW, AND WHY NOW?

HOW DO YOU
THINK?
a e k j c b a o a j
l g m e e t u p a p
l n a v a a j v i o
p c
m
e
i e t u p a a
r d a t a j a j k l
u t w i q a y a m h
d u e n x v d f g a
j o d g t f f a u i
a a s o g a g t v c
z i a c h j a i b m
Executor node
Executor node
Executor
node
Executor
node
CLASSIC
NEURAL
BIG DATA
QUANTUM

Quantum enabled, Neural Networked, Clustered,
Containerized, Analysed, Cloudified, Toaster 4J

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