1. CLAs Reconstruction and Analysis
Physics Data Processing with SOA based Framework
Vardan Gyurjyan on behalf of Clas12 software group

2. Outline Problem statement SOA based framework as a solution
Current status of the ClaRA project
Future plans
Conclusion
A problem statement is a nothing but a concise and precise description of the nature, scope, causes, severity and the effect or impact of a problem. Thus a problem statement is the specific description of a problem which is essential to understand it properly.
V. Gyurjyan
April 24, 2019

3. Computing power V. Gyurjyan April 24, 2019 Network Integration
Node/Rack Integration
CMOS Technology
Single Chip Integration
V. Gyurjyan
April 24, 2019

4. Integration IBM Blue Gene 72 Racks 1PF/s Rack 32 Mode Cards 13.9 TF/s
32 Computer Cards
435 GF/s
Computer Card
1Chip
13.6GF/s
Chip
4 Cores
V. Gyurjyan
April 24, 2019

5. Network Evolution 2003 – CAT6 10Gbps 2007 – CAT7 100Gbps V. Gyurjyan
Network Capacity
2x / 7 months
User Traffic
2x / 12months
Router
Capacity
2.2x / 18months
Moore’s Law
2x / 18 months
CAT4
10Mbps
10base-T
CAT5
100Mbps
100base-T
CAT5e
1Gbps
1000base-T
2003 – CAT6 10Gbps – CAT7 100Gbps
V. Gyurjyan
April 24, 2019

6. High Performance Computing Trends
Exponential growth in processor performance (coming to an end)
Power cost = System cost: invention required
Growth in level of parallelism (near term solution)
V. Gyurjyan
April 24, 2019

7. IBM Approach – Path to Petascale
Multiple modest cores on a single chip rather than one high- performance processor
Watts/FLOP will not improve much from future technologies.
Linux environment and MPI (standard messaging interface)
V. Gyurjyan
April 24, 2019

8. "The Network is the Computer."
John Gage
V. Gyurjyan
April 24, 2019

9. Specifics of the Offline Software
Lifetime of the software >= lifetime of the experiment.
Collaborative nature of the development.
Coexistence of parallel running applications for the single experiment.
Unprecedented scale and complexity of the physics computing environment
Physics computing environment must keep up with fast growing computing technologies
Large worldwide user base.
V. Gyurjyan
April 24, 2019

10. PDP (Physics Data Processing) Application Conventional vs
PDP (Physics Data Processing) Application Conventional vs. parallel/distributed
V. Gyurjyan
April 24, 2019

11. Running Conventional Software Application
Copy
checkout
Configure
Compile
Fix errors no
yes
Give up ok
Run
Complain no
yes
Modified?
V. Gyurjyan
April 24, 2019

12. Programming Errors Compile time Program does not compile.
Compiler reports a “best guess” of the problem
Undeclared variables or functions
Missing semicolon or brace
Typos
Missing files or libraries
Type ambiguities
Run time
Executable crashes or has unexpected behavior
May not appear for all conditions or all data sets
Uninitialized variables
Memory errors
Numeric errors
Type errors in print statements
Closing a NULL file pointer
Accessing a NULL pointer
Variables out of scope
V. Gyurjyan
April 24, 2019

13. Challenges of the Conventional Approach
Difficult to organize and coordinate activities
Difficult to maintain
Inevitable fragmentation of the software
Poor scalability
Computing skills are required to use physics data processing applications
V. Gyurjyan
April 24, 2019

14. ABC C B CLAS 6 CLAS 12 A+B << C A B A
C: requires a few or no programming skills
V. Gyurjyan
April 24, 2019

15. One way to eat an elephant
A bite at a time
V. Gyurjyan
April 24, 2019

16. Where we start?
Each bite is a clear, simple, single purpose application, developed by group B member.
Group A, with a tight collaboration with group B and C shall control and manage the process, never loosing maniacal focus on a big picture (elephant).
Define a piece of a big problem
Understand the problem
Distill the problem to its essence
solution
Test
V. Gyurjyan
April 24, 2019

17. “Things should be made as simple as possible, but not simpler
“Things should be made as simple as possible, but not simpler.” Albert Einstein
V. Gyurjyan
April 24, 2019

18. Language and Architecture Evolution
Structured and Procedural
programming
Object Oriented
programming
Assembly Language
Service Oriented
programming
V. Gyurjyan
April 24, 2019

19. SOA
SOA promotes the goal of separating service users from the service implementation.
Style of building reliable systems that deliver functionality as services
Loose coupling between interacting services
Directories and addressing mechanisms are at the center of SOA.
Complex
Program
Arbitrary format
Arbitrary format
Service
Standard format
Standard format
Specialized, simple
V. Gyurjyan
April 24, 2019

20. Attributes of Services
Well defined, easy-to-use, somewhat standardized interface
Self-contained with no visible dependencies to other services
(almost) Always available but idle until requests come
Location transparency
Easily accessible and usable readily, no “integration” required
New services can be offered by combining existing services
Quantifiable quality of service
V. Gyurjyan
April 24, 2019

21. Service Interface Standard message based Highly Polymorphic
Intent is enough
Implementation can be changed in ways that do not break all the service consumers
V. Gyurjyan
April 24, 2019

22. Service Orientation is scalable
End users can consume and combine a lot of services since they don’t have to know or “learn” how the services are made.
Service providers (A+B) can offer their services to a lot more consumers by optimizing
The user interface
Access
Implementations
V. Gyurjyan
April 24, 2019

23. “On Demand” Physics Data Processing
Use software as you need
Much lower setup time, forget about
Installation
Implementation
Training
Maintenance
Scalable and effective usage of resources
Parallelism (CPU, Storage, Bandwidth…)
V. Gyurjyan
April 24, 2019

24. What is ClaRA? Framework that Implements SOA.
Service development environment.
Toolbox of generic physics data processing services.
Network distributed platform.
The “Glue”, binding together services into an algorithmic data analysis application.
V. Gyurjyan
April 24, 2019

25. Design criteria
Framework service shall be simple to use and easy to learn.
Framework service should be customizable to be able to adapt to the different data processing tasks.
Framework shall provide context sensitive help and assistance, with many real world physics data processing application examples.
Framework shall provide ready to use services, encapsulating essential functionalities of the physics data processing system.
Services shall be reusable and easily replaceable.
Physics data processing application design and implementation shall require a few or no programming skills.
Neither specific computing environment, nor compiling shall be necessary to build and run physics data processing application.
Framework shall provide graphical environment for physics data processing application development.
Frameworks platform shall be network distributed, and shall have temporal continuity.
The new system shall provide World Wide Web access to the services for remote configuration and execution of the data processing applications. The necessary security considerations must be addressed.
V. Gyurjyan
April 24, 2019

26. Data and Algorithm Framework advocates clear separation between:
a) data and algorithm
b) transient and persistent data
Methods in the data object will be limited to manipulations of the internal data members only.
Algorithm will process one type of data and generate data objects of a different type.
Algorithm
Data
Data
V. Gyurjyan
April 24, 2019

27. Persistent and Transient Data
Physics algorithm objects should not use data objects directly in the persistent storage.
Transient data storage as a means of communication between physics algorithms.
Two different optimization criteria for applications using persistent and transient data.
Being independent from the persistent storage technology.
V. Gyurjyan
April 24, 2019

28. Data Object categories
Event
Detector
Statistical
V. Gyurjyan
April 24, 2019

29. ClaRA Platform CMSG SOAP Users Service Container node-1
Front-End Container
Normative Service
Service Container
node-1
Service Container node-2
Service Container node-3
Service Container node-N
WWW
Web Service 1
Web Service 2
Web Service 3
Web Service N
cMsg
SOAP
SOAP
SCC
SCC
SOAP
cMsg
SOAP
CMSG
SOAP
Users
V. Gyurjyan
April 24, 2019

30. Current Status ClaRA cMsg Platform Thin Clients
Geometry
Service
Magnetic
Field Map
Service
GEMC
Service
Tracking
Service
bCNU
Service
Event Data
Service
ClaRA cMsg Platform
Thin Clients
ClaRA WebServices Platform
WWW
Probability
Service
Matrices
Service
Geometry
Service
Math
Service
Stat
Service
V. Gyurjyan
April 24, 2019

31. Examples EVIO event producer and EVIO event consumer services (C++).
data producer and data consumer services. C examples use cMsg payload (ASCII).
C++ geometry service client example
Java geometry service client example
Web services JSP clients
V. Gyurjyan
April 24, 2019

32. Tracking composite application
Transient data
Space-point maker
Coarse track finder
Cluster Analyzer
Ambiguity solver
Track fitter
Histogram
builder
Persistent data
ClaRA cMsg Platform
Thin Clients
V. Gyurjyan
April 24, 2019

33. Tracking application service decomposition
Supervisor
start
retrieve
SpacePointFormation
EvtData
TransientEvtData
record
start
CoarseTrackFinder
DetectorData
retrieve
Raw Data
SeadMaker
Space Points
VertexFinder
StatData
record
ClusterAnalyzer
Track candidates
retrieve
start
AmbiguitySolver
TransientStatData
record
Resolved Tracks
start
retrieve
TrackFitter
TransientDetData
Final Tracks
TrackScoring
Transient Storage
Tracking State machine
V. Gyurjyan
April 24, 2019

34. Performance measurements
V. Gyurjyan
April 24, 2019