Keynote (Nandini Ramani) - The Role of Java in Heterogeneous Computing & How You Can Help - by Nandini Ramani, VP, Java Platform, Oracle Corporation

The role of Java™ in
Heterogeneous Computing
…and how you can help!

Nandini Ramani
Vice President, Java Platform
@eyeseewaters
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Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

The following is intended to outline our general product direction. It
is intended for information purposes only, and may not be
incorporated into any contract. It is not a commitment to deliver
any material, code, or functionality, and should not be relied upon
in making purchasing decisions.
The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole
discretion of Oracle.
©2013 Oracle Corporation

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Java

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9 Million Java developers worldwide
#1 Choice for developers
#1 Development platform
5 of Top 5 OEMs ship Java ME
3 Billion mobile phones run Java
80% of mobile developers use Java
100% of Blu-Ray players ship with Java
97% of enterprise desktops run Java
89% of desktops in USA run Java
7 Billion Java Cards sold
5 Billion Java Cards in use
115 Million TV devices run Java

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Life of Java Program

Hello.java

5

javac


Hello.class

Hello.jar

Java Virtual
Machine

Oracle Java Platforms Overview
Footprint
100MB+

10MB-100MB

1MB-10MB

50KB-1MB

Java Card
SECURITY

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Java ME Embedded
SMALL EMBEDDED


Java SE Embedded

MEDIUM EMBEDDED

LARGE EMBEDDED

Java SE
DESKTOP

SERVER

Rich Tools
Ecosystem

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Rich Tools Ecosystem
Choice is good
 Java has rich and competitive tools ecosystem
 Ports to many platforms and tool chains

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Java is Open!

9


OpenJDK
In a Nutshell
“The place to collaborate on an open-source implementation of the
Java Platform, Standard Edition, and related projects.”

http://openjdk.java.net

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OpenJDK Virtuous Cycle
Open Community – GPL v2 +
Classpath Exception

Both Gratis and Commercial
offerings (Including Support,
Enterprise tooling, etc, available)
Java 7 RI
Binaries

30+ Projects

Java for IBM
Platforms

Source contributions

Java for SAP
Platforms
Java for Red Hat
Platforms

Companies AND Individuals

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Customized VM for
Internal Use

...and many more

64-bit
Porting and optimizing
Oracle JDK for Linux ARM/V8
GA - H1 2015

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Java and
Heterogeneous
Compute

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“The HSA Foundation goal is to make it
dramatically easier to program
heterogeneous parallel devices.”

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Parallel
Java and Heterogeneous Computing

 Power usage is leading to parallel throughput
 Programmers need to execute a mix of parallel and

sequential tasks
 Java remains the tool of choice as hardware tilts toward
the parallel

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History of Parallelism Support in Java
java.util.concurrent
(jsr166)
Phasers, etc
java.lang.Thread
(jsr166)

1.4
2002
17

5.0
…

2004 …


Fork/Join Framework
(jsr166y)

7 8

6
2006

Project Lambda

….

2011

…

2014

Parallelism Support in Java 8 – Lambdas
Simplifying the code to write parallel actions
// The OLD way. Simplistic, Brittle, and Serialized
// Find Total Salary of all California Employees
public static int californiaSalary(List<Employee> employees, int salary) {
int sum=0;
for (Employee e : employees) {
if (e.getStateCode() == CALIFORNIA)
sum += e.getSalary();
}
return sum;
}

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Parallelism Support in Java 8 – Lambdas
// Total Salary of all California Employees
int sum = employees.parallelStream()
.filter(e -> e.getStateCode() == CALIFORNIA)
.mapToInt(e -> e.getSalary())
.sum();

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Parallelism Support in Java 8 – Arrays
// New Parallel Methods added to Array
// In place cumulation
Array.parallelPrefix(anArray, BinaryOperator)
// Sort
Array.parallelSort(anArray);
// In place mutations
Array.parallelSetAll(anArray, UnaryOperator)
// Special Iterator for decomposition in addition to single-element
Array.spliterator(anArray)

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Heterogeneous

 Future is power-constrained
 Tradeoff is latency vs. throughput
 Typical system will be a few low latency cores, many high

throughput cores, some special accelerators

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New Challenges –
Array-wise programming concepts
Array-wise programming

Exploit the hardware

 Java is a concurrent language with  Java JDK 8 includes parallel

concurrent and parallel
programming support built into the
core

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decomposition and programming
tools out of the box.

New Challenges – “WORA” Becomes Local
 Transitioning from write once, run anywhere “WORA” to a world where

one JVM can access multiple ISAs at once

JVM 1

JVM

OS 1

OS 2

OS

ISA 1

ISA 2

ISA 1

ISA 2

Chip 1

Chip 2

Chip 1

Chip 2

Mem 1
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JVM 2

Mem 2

Mem 1

Mem 2


New Challenges – Route Work to Best Resource
 Some cores optimized for:
– High Throughput

JVM

– Low latency

OS ?

– Low Power

 Where should work be routed?

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ISA 1

ISA 2

Chip 1

Chip 2

Mem 1

– Special Accelerators

Mem 2

New Challenges – Non-Uniform Memory Access
 Heterogeneous systems have

JVM

bumpy costs with non-uniform
memory
– JVM can manage memory on

the fly in response to program
dynamics
– Manage memory access and

data flow across ISAs

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OS
ISA 1

ISA 2

Chip 1

Chip 2

Mem 1 Memory Mem 2

How do we meet these and other challenges?
JVM
OS
ISA 1
Chip 1


Chip 2

Mem 1

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ISA 2

Mem 2

How do we meet these and other challenges?

http://openjdk.java.net/projects/sumatra/

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Project Sumatra
 OpenJDK Project with leadership from AMD and Oracle
 Goal is to simplify Heterogeneous Compute (via multiple ISAs) for

nine million+ Java developers
 Oracle team includes Labs and SPARC expertise
 HSA Standards are important:
– HSAIL helps with common back end notation
– HSA helps with portability

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Gary Frost
Software Fellow

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Sumatra Demo
 Histogram of male/female (5150) names
 Found in selected works of ‘Charles Dickens’
 So conceptually we have sequence of nested loops.
for each selected book{
for each name{
for each occurrence of ‘name’ in ‘book’{
name.count++;
}
}
}

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Can be done
in parallel !

Sumatra Demo
 Here is the actual Java 8 code at the heart of the demo
Arrays.stream(library)
.filter(Book::isSelected)
.forEach(book->
Arrays.stream(names)
.parallel() // <-- Loop over the names in parallel.
.forEach(name ->
// for each occurrence of ‘name’ in ‘book’
//
name.count++;
);
);
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Sumatra Demo
 Java 8 can efficiently dispatch the enclosed lambda across multiple

cores when the stream is marked as parallel()
Arrays.stream(T[] array)
.parallel()
.forEach(t -> /* lambda */)

 A Sumatra JVM will dispatch to CPU and HSA enabled GPU cores.
– JVM converts (JITS) bytecode to HSAIL and executes via HSA Runtime

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HSA enablement of JAVA
Java 7

Java 8

Beyond Java 8

OpenCL enabled Aparapi

HSA enabled Aparapi

Sumatra

• AMD initiated Open Source project

• Java 8 Lambda based API

• Java API for data parallel algorithms

• HSA and SVM enablement

• Adds Native GPU acceleration to JVM
• Java 8 Lambda/Stream API

• No need to learn OpenCL

• JVM generates HSAIL via JIT

• No explicit buffer transfers

• JVM dispatches to CPU or GPU
depending on workload characteristics.

Java Application

Java JDK Stream + Lambda API

APARAPI
API

APARAPI / Lambda
API

OpenCL™

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HSAIL

HSA Finalizer &
Runtime

HSA Finalizer &
Runtime
JVM

JVM

JVM
GPU ISA

CPU

JIT

HSAIIL

OpenCL™ Compiler
and Runtime

CPU ISA

Java Application

Java Application

GPU


GPU ISA

CPU ISA
CPU

GPU

GPU ISA

CPU ISA
CPU

GPU

How you can help!
 Project Sumatra is Open Source
 We can always use help:
– Test builds
– Track progress
– Are you a VM or Compiler super star? We

can always use more help!
http://openjdk.java.net/projects/sumatra/

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Keynote (Nandini Ramani) - The Role of Java in Heterogeneous Computing & How You Can Help - by Nandini Ramani, VP, Java Platform, Oracle Corporation

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Keynote (Nandini Ramani) - The Role of Java in Heterogeneous Computing & How You Can Help - by Nandini Ramani, VP, Java Platform, Oracle Corporation