Final apu13 phil-rogers-keynote-21

THE PROGRAMMER’S GUIDE TO REACHING FOR THE CLOUD
PHIL ROGERS, CORPORATE FELLOW, AMD
NOV. 11, 2013

MODERN CLOUD WORKLOADS ARE HETEROGENEOUS
SCALAR CONTENT WITH A GROWING MIX OF PARALLEL CONTENT

 Video is expected to represent two thirds of mobile data traffic by 2017
‒ Video is continuously being captured, uploaded, transcoded and streamed
‒ Video processing is inherently parallel … and can be accelerated

 Big data growing exponentially with Exabytes of data crawled monthly
‒ Indexing the web and extracting high definition information
‒ Map reduce is a heterogeneous workload

 Natural User Interfaces are still in their infancy
‒ Accurate extraction of meaning from gesture and voice
‒ Getting to the fingertips and voice inflections

NEED TO SIMULTANEOUSLY
INCREASE PERFORMANCE AND
REDUCE POWER
2 | APU-13 KEYNOTE | NOVEMBER 11, 2013 | PUBLIC

FUTURE TECHNOLOGY GROWTH WILL ACCELERATE THE TREND
 Rapid growth of Sensor Networks

RAPID GROWTH OF THE NUMBER OF THINGS
CONNECTED TO THE INTERNET

‒ Drives exponential increase in data

 Internet of Everything (IoE) results
in explosion of data sources

“Fixed” Computing
(you go to the device)

Mobility / BYOD
(the device goes with
you)

Internet of Things
(age of devices)

HOW MUCH VALUE IS AT
STAKE IN THE IOE ECONOMY?

Internet of Everything
(people, process, data,
things)

$14.4
trillion

50B

‒ Another exponential growth in data
at local and cloud level

 Context Aware Computing is a
Huge Big-Data Problem

$9.5

$4.9

trillion

‒ Both local and cloud compute must
get faster/lower power
1995

2000

2005

2010

2015

2020

trillion

From
industry-specific
use cases

From
cross-industry
use cases

DRIVING FUTURE DEMAND FOR LOCAL AND CLOUD PARALLEL EFFICIENCY

Source: Cisco IBSG, 2013

HSA APU PROCESSORS OPERATE HARMONIOUSLY AT LOW POWER
EXAMPLE: VIDEO ENHANCEMENT

 Techniques include:
‒ Image Stabilization, Super Resolution, Deblur, Deinterlace, Lighting & Contrast

 Enhancements examine pixels from a large number of video frames
‒ Super-resolution based on information from surrounding frames

 Algorithms can be run on multiple processors in the APU
‒ CPU, GPU, DSPs, Fixed Function Accelerators
‒ Convolutions, motion estimation, histograms,
format conversions, etc.
‒ Processing flows freely between processors
for best efficiency


HETEROGENEOUS PROCESSORS - EVERYWHERE
SMARTPHONES TO SUPER-COMPUTERS

Super computer
Dense Server
Tablet
Phone

Workstation
Notebook

A SINGLE SCALABLE ARCHITECTURE
FOR THE WORLD’S PROGRAMMERS
IS DEMANDED AT THIS POINT

HOW DOES HSA MAKE THIS ALL WORK?
 Enables acceleration of languages like Java, C++ AMP and Python
 All processors use the same addresses, and can share data structures in place
 Heterogeneous computing can use all of virtual and physical memory
 Extends multicore coherency to the GPU and other processors
 Pass work quickly between the processors
 Enables quality of service

HSA FOUNDATION – BUILDING
THE ECOSYSTEM


HSA FOUNDATION AT LAUNCH
BORN IN JUNE 2012

Founders


HSA FOUNDATION TODAY – NOVEMBER 2013
A GROWING AND POWERFUL FAMILY

Founders
Promoters
Supporters
Contributors

TBA at APU-13

Universities


NTHU Programming
Language Lab

NTHU System
Software Lab

COMPUTER SCIENCE

HSA FOUNDATION PROGRESS
WHAT AN AMAZING FIRST YEAR

 Membership growing rapidly
‒ 2-3 new members per month
‒ Universities enrolling

 Four working groups generating specifications
‒ HSA Programmers Reference Manual published
‒ HSA System Architecture spec going to ratification by the
end of the year
‒ Runtime WG and Tools WG will publish early next year

 HSA Development platforms to ship in early 2014


PROGRAMMING LANGUAGES PROLIFERATING ON HSA
OpenCL™
App

Java App

C++ AMP
App

Python
App

OpenCL
Runtime

Java JVM
(Sumatra)

Various
Runtimes

Fabric
Engine RT

HSAIL
HSA
Helper Libraries

HSA Core
Runtime

Kernel Fusion
Driver (KFD)


HSA
Finalizer

HIGH EFFICIENCY VIDEO CODEC – HEVC (H.265)
VALUE PROPOSITION

HEVC VISUAL QUALITY IS
SIGNIFICANTLY BETTER THAN
H.264 AT ANY GIVEN BIT RATE

30% TO 50% MORE EFFICIENT
THAN H.264 AT 1080P RESOLUTION

4K Ultra HDTV
Sony XBR
$4999

H.265 @ 500 kbps

H.264 @ 500 kbps


4K VIDEO BENEFITS ARE EVEN
MORE SIGNIFICANT WITH HEVC

30% to 50%

4K Video Cameras
GoPro
$399

HIGH EFFICIENCY VIDEO CODEC – HEVC (H.265)
WHY HEVC WILL PROLIFERATE
 The next generation MPEG video encoding standard
 Significantly higher efficiency (up to 50% lower bit
rates at given quality) than AVC (H.264)
 Highly beneficial for HD video (1080p or below)
 Especially beneficial for 4K video
 Scales to 8K Ultra High Definition video (up to
8192×4320)
 Computationally complex, but by design easier to
parallelize than H.264

Traffic Share

Mobile Video
Mobile M2M

Exabytes Per Month
12

Mobile Web/Data
Mobile File Sharing

3.5%
5.1%

10

24.9%
8
6
4

66.5%

2

CLOUD VIDEO PROVIDERS NEED THE HIGHER
COMPRESSION FOR QUALITY OF SERVICE

0

2012

2013

2014

2015

2016

2017

Source: Cisco VNI Mobile Forecast, 2013

HEVC (H.265) ACCELERATION
EFFICIENT CLOUD DEPLOYMENT

ALL STAGES OF HEVC ARE
ACCELERATED ON THE APU






Decrypt
Decode and decompress
Scaling and Enhancement
Encode and compress
Encrypt


ENCODE IS THE HEAVIEST
STAGE

H.265 ENCODING IS 5 – 10X MORE
COMPUTATIONALLY COMPLEX THAN H.264

 Leverage point for
compression
 Highly parallel
 Algorithms improve
monthly
 Must stay programmable

 Picture can be divided
into Macroblock
regions with a much
wider range of sizes
and shapes
 Motion vectors have
33 prediction
directions compared
to 8 for H.264

OVERVIEW OF B+ TREES
 B+ Trees are a special case of B Trees

 A B+ Tree …
‒ is a dynamic, multi-level index
‒ Is efficient for retrieval of data, stored in a block-oriented
context

 Fundamental data structure used in several
popular database management systems
‒ SQLite
‒ CouchDB

 Order (b) of a B+ Tree measures the capacity of its nodes

3
2

5

4

6

7

1

2

3

4

5

6

7

8

d1

d2

d3

d4

d5

d6

d7

d8


APPLICATIONS THAT USE B/B+ TREES

primary data store on the clientside

multi-data center key-value store

Mail, Safari, iPhone, iPod, iTunes

market-data framework

Firefox and Thunderbird

large hadron collider

Android, Chrome


http://www.sqlite.org/famous.html

http://wiki.apache.org/couchdb/CouchDB_in_the_wild

HOW WE ACCELERATE
 Utilize coarse-grained parallelism in B+ Tree searches
‒ Perform many queries in parallel
‒ Increase memory bandwidth utilization with parallel reads
‒ Increase throughput (transactions per second for OLTP)

 B+ Tree searches on an HSA enabled APU
‒ Allows much larger B+ Trees to be searched, than traditional GPU compute
‒ Eliminates data-copies since CPU and GPU cores can access the same memory


RESULTS
1M search queries in parallel
7

 Input B+ Tree contains 112 million
keys and uses 6GB of memory

 Software: OpenCL on HSA

5

Speedup

 Hardware: AMD “Kaveri” APU
with Quad Core CPU and 8 GCN
Compute Units at 35W TDP

6

4
3
2
1
0
8

16

32

64

128

Order of B+ Tree

Baseline: 4-core OpenMP + hand-tuned SSE CPU implementation

Results measured in AMD Labs on “Kaveri” APU, 35W TDP, 16GB DRAM

REVERSE TIME MIGRATION (RTM)

Land crews

 A technique for creating images based on
sensor data to improve seismic interpretations
done by geophysicists

Marine crews

 A memory-intensive and highly parallel
algorithm
 RTM is run on massive data sets
 A natural scale out algorithm
 Often run today on 100K node CPU systems
 Bringing this to HSA and APU based
supercomputing will increase performance for
current sensor arrays, and allow more sensors
and accuracy in the future.


HOWEVER, SPEED OF PROCESSING AND
INTERPRETATION IS A CRITICAL
BOTTLENECK IN MAKING FULL USE
OF ACQUISITION ASSETS

TEXT ANALYTICS – HADOOP TERASORT AND BIG DATA SEARCH
MINING BIG DATA
 Multi-stage pipeline or parallel
processing stages
 Traditional GPU Compute is challenged
by copies

Input HDFS
sort
split 0

map

Sort
Compression
Regular expression parsing
CRC generation

 Acceleration of large data search scales
out across the cluster of APU nodes


Output HDFS
merge
reduce

split 1

split 2

part 0

HDFS
Replication

reduce

 APU with HSA accelerates each stage in
place
‒
‒
‒
‒

copy

part 1

HDFS
Replication

map

map

PROGRAMMING MODELS EMBRACING HSAIL AND HSA
THE RIGHT LEVEL OF ABSTRACTION

UNDER DEVELOPMENT





Java: Project Sumatra OpenJDK 9
OpenMP from SuSE
C++ AMP, based on CLANG/LLVM
Python and KL from Fabric Engine


NEXT






DSLs: Halide, Julia, Rust
Fortran
JavaScript
Open Shading Language
R

HSA ENABLES DEVELOPERS TO LEVERAGE HC … EASILY & NATURALLY

PREFERRED PROGRAMMING
LANGUAGES

TRANSPARENT CALLS TO POPULAR
LIBRARIES

 Java, C++, OpenMP, Python *

 OpenCV, SciPy, NumPy,
ImageMagick, Bolt, …

 SVM, Coherence, GPU Enqueue
 OpenJDK/Sumatra, Fabric
Engine

 Arbitrary data structures, SVM,
Coherence, User mode
queueing
 OpenCV API, Bolt STL library

* Java 8, C++ AMP, OpenMP 4.0 next generation standards and extensions

USING CONVENTIONAL
METHODS
 Arbitrary data structures,
malloc, function pointers, callbacks, recursion,
semaphores, atomics
 SVM, Coherence, User-mode
queueing, GPU Enqueue, HSAIL
 Linked-list/tree traversal +
other complex shared host data
structures

C++ AMP ACCELERATION GOES MULTI-PLATFORM
 Herb Sutter Announced C++ AMP for the Windows® Platform at ADS 2011
 We very much liked the single source model of development, and decided to extend it
to be multi-platform
 Today we are announcing C++ AMP is moving beyond Microsoft® Windows to embrace
Linux. We will offer this acceleration on both our APUs and our discrete GPUs
 We are also bringing Bolt STL Library support to C++ AMP

C++AMP


CLANG Front-end

LLVM-IR or
SPIR 1.2

Any HSA
Implementation

SPIR 1.2

AVAILABLE IN
OPEN SOURCE
1H-2014

HSAIL

Any OpenCL™+SPIR
Implementation

LLVM Compiler

HSA ENABLEMENT OF JAVA
JAVA 7 – OpenCL ENABLED APARAPI

JAVA 8 – HSA ENABLED APARAPI

JAVA 9 – HSA ENABLED JAVA (SUMATRA)

 AMD initiated Open Source project

 Java 8 brings Stream + Lambda API.

 Adds native GPU acceleration to Java Virtual
Machine (JVM)

 APIs for data parallel algorithms
‒ GPU accelerate Java applications
‒ No need to learn OpenCL™

 Active community captured mindshare
‒ ~20 contributors
‒ >7000 downloads
‒ ~150 visits per day

‒ More natural way of expressing data parallel
algorithms
‒ Initially targeted at multi-core.

We will provide
HSA Enabled Aparapi on Java 8
 APARAPI will :
to bridge between Aparapi on Java 7
‒ Support Java 8 Lambdas
‒ Dispatch code to HSA enabled devices at 9
and HSA/Sumatra on Java
runtime via HSAIL

Java Application

 Developer uses JDK Lambda, Stream API
 JVM uses GRAAL compiler to generate HSAIL
 JVM decides at runtime to execute on either
CPU or GPU depending on workload
characteristics.

Java Application

Java Application
Java JDK Stream + Lambda API

APARAPI API

APARAPI + Lambda API

OpenCL™
OpenCL™ Compiler
& Runtime

CPU

HSAIL

HSA Finalizer
& Runtime

JVM
CPU ISA

Java GRAAL JIT
backend

HSAIL

HSA Finalizer
& Runtime

JVM
GPU ISA
GPU


CPU ISA
CPU

JVM
GPU ISA
GPU

CPU ISA
CPU

GPU ISA
GPU

JAVA DEMO

WELCOME GARY FROST TO THE STAGE


NBODY REVISTED
 NBody problem:
‒ Calculate the position of ‘N’ bodies in 3D space by computing the gravitational effect each has on all
of the others and updating it’s position.

 A Java sequential NBody implementation would start with an Object for each Body.
public class Body{
// State of object
private float x, y, z, m, vx, vy, vz;
// Method to update position relative to other bodies
void updatePosition(Body[] bodies){ /* code omitted */ }
}

 Then we would iterate over all bodies updating the position of each
for (Body b: bodies) {
b.updatePosition(bodies)
});

 A pre Java 8 Java ‘parallel’ version would not fit so nicely on this slide ;)

JAVA 8’S ‘PROJECT LAMBDA’ SIMPLIFIES PARALLEL PROGRAMMING
 Offers an alternate syntax for processing arrays/collections of data
for (Body b; bodies)
b -> updatePosition(bodies);

Arrays.stream(bodies) // wrap array in a stream
.forEach(b -> b.updatePosition(bodies);

 To process a stream in parallel we just tag the stream with the parallel() modifier
Arrays.stream(bodies) // Wrap an array in a stream
.parallel();
// tag the stream as parallel
.forEach(b -> b.updatePosition(bodies);

 In Java 8 a parallel stream executes across all CPU cores.
 In Java 9 (Sumatra) a parallel stream executes across all CPU and GPU cores


JAVA DEMO


JAVA AND THE CLOUD

THE RIGHT LANGUAGE WITH ACCELERATION ON CLOUD APUS

 Java 8 and Java 9 provide parallel acceleration
 Parallel workloads are proliferating in the cloud
 Hadoop framework for scale out
 HSA APUs provide workload acceleration

DON’T MISS THE KEYNOTE
TOMORROW FROM ORACLE’S
NANDINI RAMANI

“THE ROLE OF JAVA™ IN HETEROGENEOUS
COMPUTING, AND HOW YOU CAN HELP”

ANNOUNCING AMD’S UNIFIED SDK
 Access to AMD APU and GPU programmable
components
 Component installer - choose just what you need

 Initial release includes:
‒ APP SDK v2.9
‒ Media SDK 1.0 Beta

AMD Unified SDK

APP SDK 2.9

MEDIA SDK 1.0 BETA

 Web-based sample browser

 GPU accelerated video pre/post processing library

 Supports programming standards: OpenCL™, C++ AMP

 Leverage AMD's media encode/decode acceleration blocks

 Code samples for accelerated open source libraries:

 Library for low latency video encoding

‒ OpenCV, OpenNI, Bolt, Aparapi

 OpenCL™ source editing plug-in for visual studio
 Now supports Cmake


 Supports both Windows Store and Classic desktop

ANNOUNCING AMD

V1.3

 AMD’s comprehensive heterogeneous
developer tool suite including:
‒ CPU and GPU Profiling
‒ GPU kernel Debugging
‒ GPU kernel analysis

 New features in version 1.3:
‒ Supports Java
‒ Integrated static kernel analysis
‒ Remote debugging/profiling
‒ Supports latest AMD APU and GPU products

CPU PROFILER

GPU PROFILER

GPU DEBUGGER

STATIC KERNEL ANALYZER

 Time-based profiling

 OpenCL™ Application Trace

 Analyze call-chain relationships

 Profile OpenCL kernels

 Compile, analyze and
disassemble OpenCL Kernels

 Java profiling with inline
function support

 Timeline visualization of GPU
counter data

 Real-time OpenCL kernel
debugging with stepping and
variable display

 Cache-line utilization profiling

 Kernel Occupancy Viewer

 Supports latest AMD processors

 Remote GPU Profiling


 OpenCL and OpenGL API
Statistics
 Object visualization
 Remote GPU debugging

 View kernel compilation
errors/warnings
 Estimate kernel performance
 View generated ISA code
 View registers

OPEN SOURCE LIBRARIES ACCELERATED BY AMD

OpenCV

Bolt

clMath

Aparapi

 Most popular computer
vision library

 C++ template library

 AMD released APPML as
open source to create
clMath

 OpenCL™ accelerated Java 7

 Now with many OpenCL™
accelerated functions

 Provides GPU off-load for
common data-parallel
algorithms
 Now with cross-OS support
and improved
performance/functionality


 Accelerated BLAS and FFT
libraries
 Accessible from Fortran, C
and C++

 Java APIs for data parallel
algorithms (no need to
learn OpenCL™

AMD APUS, HSA – CLIENT TO THE CLOUD
A CONVERGENCE AT THE RIGHT TIME

 Parallel workloads are booming
‒ Acceleration where the data is
‒ On the client for a snappy user experience
‒ In the cloud for scalable services

 HSA enabled APUs in the cloud
‒ Big data analytics
‒ Video processing
‒ Science, imaging, genomics
‒ Unleashing the Java development community

 Acceleration at all tiers of the cloud
‒ Data centers, media hubs, cloud periphery


A SPECIAL GUEST

Gary Campbell

Infrastructure Technology Strategy CTO
HP


DISCLAIMER & ATTRIBUTION

The information presented in this document is for informational purposes only and may contain technical inaccuracies, omissions and typographical errors.
The information contained herein is subject to change and may be rendered inaccurate for many reasons, including but not limited to product and roadmap
changes, component and motherboard version changes, new model and/or product releases, product differences between differing manufacturers, software
changes, BIOS flashes, firmware upgrades, or the like. AMD assumes no obligation to update or otherwise correct or revise this information. However, AMD
reserves the right to revise this information and to make changes from time to time to the content hereof without obligation of AMD to notify any person of
such revisions or changes.
AMD MAKES NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE CONTENTS HEREOF AND ASSUMES NO RESPONSIBILITY FOR ANY
INACCURACIES, ERRORS OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION.
AMD SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT WILL AMD BE
LIABLE TO ANY PERSON FOR ANY DIRECT, INDIRECT, SPECIAL OR OTHER CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION
CONTAINED HEREIN, EVEN IF AMD IS EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
ATTRIBUTION
© 2013 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo and combinations thereof are trademarks of Advanced Micro Devices,
Inc. in the United States and/or other jurisdictions. OpenCL is a trademark of Apple Inc. and Microsoft and Windows are trademarks of Microsoft Corp. Other
names are for informational purposes only and may be trademarks of their respective owners.

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