java environment for parallel realtime development ...2 aicas jeopard — project vision project...
TRANSCRIPT
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Java Environment for Parallel Realtime Development
Technology Vision
Dr. Fridtjof SiebertCTO, aicas GmbH
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aicas JEOPARD — Project Vision
Project Goal
Provide a platform independent software development environment for complex, safe, real-time, multicore systems.
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aicas JEOPARD — Project Vision
Project Work Structure
Multi-Core Java Processor (JOP) Java/FPGA Interfacing
WP 2: Architecture Layer
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aicas JEOPARD — Project Vision
Project Work Structure
Parallel Partitioning RTOS
WP 2: Architecture Layer
WP 3: OS Layer
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aicas JEOPARD — Project Vision
Project Work Structure
Parallel Real-Time JVM Parallel Real-Time GC
WP 2: Architecture Layer
WP 3: OS Layer
WP 4: Virtual Machine Layer
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aicas JEOPARD — Project Vision
Project Work Structure
Multicore APIs Standardization
WP 2: Architecture Layer
WP 3: OS Layer
WP 4: Virtual Machine Layer
WP 5: API Layer
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aicas JEOPARD — Project Vision
Project Work Structure
Static Analysis Unit Testing
WP 2: Architecture Layer
WP 3: OS Layer
WP 4: Virtual Machine Layer
WP 5: API Layer
WP 6: Analysis Tools
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aicas JEOPARD — Project Vision
Project Work Structure
Radar Application SW Radio Aircraft
OperationalCommunications
WP 2: Architecture Layer
WP 3: OS Layer
WP 4: Virtual Machine Layer
WP 5: API Layer
WP 6: Analysis Tools
WP 7: Validation
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aicas JEOPARD — Project Vision
Real-Time Guarantees
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GCcycle 1 cycle 2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GCcycle 1 cycle 2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
Parallel & Concurrent GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1: Application
CPU 2: GC
CPU 3: GC
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
Parallel & Concurrent GC Parallel Incremental GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1: Application
CPU 2: GC
CPU 3: GC
CPU 1
CPU 2
CPU 3
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
Parallel & Concurrent GC Parallel Incremental GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1: Application
CPU 2: GC
CPU 3: GC
CPU 1
CPU 2
CPU 3
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
Parallel & Concurrent GC Parallel Incremental GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1: Application
CPU 2: GC
CPU 3: GC
CPU 1
CPU 2
CPU 3
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
Parallel & Concurrent GC Parallel Incremental GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1: Application
CPU 2: GC
CPU 3: GC
CPU 1
CPU 2
CPU 3
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Real-Time Guarantees
Blocking GC Incremental GC
Concurrent GC Parallel GC
Parallel & Concurrent GC Parallel Incremental GC
cycle 1 cycle 2
CPU 1: Application
CPU 2: GC
CPU 3: Application
CPU 1: Application
CPU 2: GC
CPU 3: GC
CPU 1
CPU 2
CPU 3
CPU 1
CPU 2
CPU 3
cycl1 cycl2
cycl1 cycl2
cycl1 cycl2
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aicas JEOPARD — Project Vision
Scalability
Theoretical Limits
1 2 4 8 16 32 64 128 256
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
idealcheckcompressjessraytracedbjavacmpegaudiomtrtjacknon-parallel
CPUs
Utilization
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aicas JEOPARD — Project Vision
Minimal Added Complexity new APIs, e.g.:
CPU affinity:
AffinitySet.setProcessorAffinity (affinitySet, thread);
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aicas JEOPARD — Project Vision
Find Errors
Classical Race Conditions Thread 1 Thread 2
obj.i++; obj.i++;
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aicas JEOPARD — Project Vision
Find Errors
Classical Race Conditions Thread 1 Thread 2
obj.i++; obj.i++;
Static Analysis
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aicas JEOPARD — Project Vision
Conclusion
JEOPARD enables
real-time applications to profit from upcoming multi-core architectures
Technology now part of aicas' products
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AICAS CASSIDIAN- SYSGO WebinarDecember 8th, 2010
Multi-core and Virtualization:A Safe & Secure Solution with PikeOS
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Agenda
Company Overview PikeOS concept
Safe and Secure Virtualization Multi-core support General benefits
PikeOS and real-time Java Aicas’ Jamaica integration
References CASSIDIAN project
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SYSGO Company Overview
A software technology leader addressing a global market
Founded in 1991 90+ employees
15% growth in 2008, 20% growth in 2009
Continuous product revenue growth since 2001
Offices in Germany (Mainz, Ulm, Rostock), France (Paris), The Czech Republic (Prag) and North America (Chicago)
Distributors in Japan, Korea, Italy, UK, Austria, India,…
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Safe & Secure Virtualization RTOS
Designed from ground-up for safety & security Modularity and compactness MILS compliant architecture
Virtualization for embedded/real-time By design (no hypervisor add-on) From ARINC-653 to full featured Linux
Scalable and flexible Can be used just as small and fast RTOS Widest range of supported personalities in the
market (Posix, Arinc-653, Linux, RTEMS, Ada, RTJava,...)
Hardware independent x86, PPC, ARM, MIPS, SPARC/LEON, SH-4... Not depending on hardware virtualization
Certification for safety & security DO-178B, EN50128, IEC61508, EAL,...
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Modular Virtualization Platform
User Mode
Kernel Mode
up to 62 partitionsGuest
OperatingSystem
GuestRuntime
Environment
PikeOSNative
Architecture SupportPackage (ASP)
Platform SupportPackage (PSP)
PikeOS System Software
PikeOS Separation Microkernel
Hardware
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Resource Partitioning
Static allocation of all system resources
Application has guaranteed access to assigned resources
Applications cannot access resources of other partitions if not explicitly configured otherwise
No error propagation throughout other partitions
Memory protection enforcement using Hardware (MMU)
All partitions execute in user mode
Separated resource partitions
GuestOperating
System
GuestRuntime
Environment
PikeOSNative
ASP PSP
PikeOS System Software
PikeOS Separation Microkernel
Hardware
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Time Partitioning
Static configuration of execution order and duration of partitions
Deterministic hard real-time operation
Advanced features* Multiple resource partitions can be assigned to one
time partition Best possible CPU usage
Dedicated threats can be scheduled whenever the current time partition becomes idle
Shortest response time Optional: Dedicated threads can have superior priority to any normal partition
(if used this will violate strict determinism and might have impact on hard real time behavior of normal partitions)
* using PikeOS’ patented scheduler
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PikeOS Multi-Core Support
Asymmetric Multi Processing (AMP) Adaptations are limited to the PikeOS PSP PikeOS can coexist with other operating
systems or executives Multiple instances of PikeOS can run in parallel
Symmetric Multi Core Support (SMP) Cores are allocated to resource partitions Cores can be shared between partitions One partition can have multiple cores assigned
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Safe & Secure Virtualization Benefits
On a single device the software virtualization platform can ensure certifiable coexistence of any safety critical applications non critical applications hard real-time applications non real-time applications
Modular certification applications can be certified partition wise no need to certify a non-critical application just because it
runs on the same hardware
Future proof long-term solution code can be reused as standard API are available implementation can be vastly independent of hardware
Centralized fault management (Health Monitoring)
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PikeOS Benefits Reduce costs
Decrease hardware deployment costs• Reduce number of computers to be used• Optimize CPU use
Decrease development costs• Allow legacy code reuse• Special tools to manage time & space partitioning
Decrease certification costs• Very modular architecture• ASP and PSP concept
Secure investment Increase hardware independence
• Kernel implementation does not depend on specific hardware
Facilitates new execution environment support• Personality concept allows easy port to new
software/middleware platform
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JEOPARD highlights
SMP built on top of safe and secure POSIX Certifiable POSIX based on core PSE52 UDP certifiable stack and certifiable file system
Jamaica support through POSIX SMP Personality Especially designed and implemented for JEOPARD
Java threads vs PikeOS threads 1 PikeOS thread for n POSIX threads 1 POSIX thread for 1 Java thread
Multi-Core management Static assignment for PikeOS threads Load balancing for POSIX /Java threads
Determinism vs performance Load balancing doesn’t impact real-time behavior Good level of performance despite hard real-time support
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JEOPARD industrial applications
Project objectives System software designed for developing predictable high
performance embedded systems that exploit the power of new multiprocessor platforms and higher level programming languages.
Avionics Application – GMV Skysoft AOC (Airline Operational Centre): IMA application, compliant to
ARINC 653 and developed according to DO-178B/EC-12B Level C. It communicates with a number of on-board & ground systems, like Flight Control, Navigation, Cockpit Display, AGP.
SDR Application - RadioLabs Software Defined Radio application: designed in order to simulate
the operations performed in a UMTS system at the physical layer.
Radar Application – CASSIDIAN (formely EADS Defense & Security) Radar application: based on performance requirements typically
related to Airport Surveillance Radar (ASR). The ASR digital subsystem is called a Radar Processor (RAP).
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SYSGO’s users
37
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Cassidian38
Java Environment for Parallel Real-time Development
Radar Use Case
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Cassidian39
Cassidian
Formerly EADS Defence & Security Revenues of € 5.4 billion in 2009 21000 employees
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Cassidian40
Radar use case
2 Demonstrators: jRSP and jTracker jRSP requires high processing and data throughput jTracker handles a huge amount of objects
Jeopard tools fulfill most of the radar use case evaluation criteria
jRSP was demonstrated at ICT 2010 in Brussels jRSP uses all the Jeopard runtime tools
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Cassidian41
Jeopard Radar Signal Processor
Radar Scenario
Generator Plan Position Indicator(DISPLAY)
Radar Signal Processor
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Cassidian42
Jeopard Radar Signal Processor
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Cassidian43
Jeopard Radar Signal Processoruc Use Cases Ev aluation jRSP
«system»jRSP
receiv e ADC data
detect and output targets
perform high resolution spectrum estimation (HRSE) at
selected position
prov ide HRSE results to LCD Display
«system»Radar Scenario
Generator
«system»PPI Display
«system»LCD Display
User
rad
ar
pic
ture
«flo
w»
HR
SE
po
sitio
n
«flo
w»
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Cassidian44
Jeopard Radar Signal Processor
HW Methods
FPGA FPGA6 Core CPU LCD Display
PC Mainboard
PikeOS/Linux J OP
LCD Display Control
High Resolution Spectrum Estimation
(HRSE)
Pulse Compression
(PC)
CONTROL
J amaica VM / OpenJ DK
HW Methods
HW Methods HW Methods HW Methods
Show HRSEresults
SpectrumEstimation
(SE)
Detection(DET)
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Cassidian45
Radar use case Evaluation criteria (1)
Criteria Expected Result
EV001 Application scalability over all cores
All processing cores have about the same load and processing time inversely proportional to the number of cores
EV002 Minimum input sample rate or input data rate with application running
2.5 MHz10MB/s
EV003 Minimum processing throughput without data loss
60000 detections per scan600 tracks per scan
EV004 CPU idle time More than 25%
EV005 HWMethods overhead (latency)
Less than 0.5ms
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Cassidian46
Radar use case Evaluation criteria (2)
Criteria Expected Result
EV006 Ethernet maximum throughput (TCP) without application running
More than 50 MB/s
EV007 UDP Package loss None for data rates lower than 15MB/s
EV008 Overall application latency Less than 150ms
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Cassidian47
Radar use case
Pulse compression 4 threads
0
1
2
3
4
5
6
7
8
9
0 20 40 60 80 100
batch number (1 batch = 1000 executions)
tim
e [
ms]
jRSP_nRT average jRSP_nRT min jRSP_nRT max
jRSP_RT average jRSP_RT min jRSP_RT max
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Cassidian48
Radar use case What the Jeopard tools have achieved
Show the strength of real-time Java on parallel architectures Predictability Good scalability Data processing performance
Application overall latency can be better with the Jeopard tools than with standard Linux and standard Java. (jRSP)
Application overall execution performance with PikeOS and Jamaica can be very similar to performance on Linux and OpenJDK (jTracker)
Add FPGA power to Java (HWMethods) with minimal added latency
Java-Java information sharing within heterogeneous systems (JOP on FPGA)