Designing a CODAC for Compass

1
Designing a CODAC for Compass

• Presented by
• André Sancho Duarte

2
Outline

• Introduction to the CODAC concept
• Compass Tokamak
• CODAC in modern fusion experiments
• Issues
• Needs
• Solutions
• CODAC implementations
• Firesignal
• Other examples
• Application to Compass

3
CODAC System

• Control, Data Access and Communications
• System for
• Control
• Experiment configuration
• Support systems configuration
• Data Acquisition and Retrieval
• Communications
• Remote Participation

4
CODAC Diagram for ITER
5
Compass Tokamak

• Major radius 0.56 m
• Minor radius 0.18 0.23 m
• Plasma current lt 350 kA
• Magnetic field 1.2 or 2.1 T
• Triangularity 0.5 - 0.7
• Elongation 1.8
• Pulse length lt 1 s
• PLH, 1.3 GHz lt 0.4 MW
• PNBI 2 0.3 MW

6
CODAC for Compass

• The development of a control and data acquisition
  system for Compass represents an opportunity to
  test ITER relevant solutions
• The following areas are planned to test in
  Compass
• Remote maintenance/upgrade of the control
  software and re-programmable hardware.
• Automatic/interactive installation and deployment
  of instrumentation hardware.
• Formal self-description of plant systems,
  including diagnostic systems, using the XML set
  of technologies.
• Fast, real-time multivariable (MIMO) plasma
  controllers.
• Online data reduction as an option or in parallel
  to raw data storage on large memories.

7
Modern Fusion Experiments

• Pulse duration over 1 second
• Expectation of human intervention
• Around 50 diagnostics, some very complex
• Over 100 MB/s of data per diagnostic
• Example Rogowsky coils in Compass can produce
  256 MB/s (32 channels of 4 bytes _at_ 2 Msamples/s)
• Small number of pulses during a campaign
• Constant monitoring of the machine and its
  envolving

8
Typical Experiment Flow Chart
9
Desired Experimental Chart
10
Issues- Collected Data (1/3)

• The size of the data collected can cause data
  transport and storage issues and increment of the
  operation cycle-time beyond the machine
  constrains
• Implement faster data transport to comply with
  machine cycle-time (use of new generation faster
  data transport networks)
• Higher-speed real-time pulse processing both
  during and after shot?
• Implement event-driven data acquisition operation
• Data is acquired or actions performed (e.g.
  change acquisition rate) only when relevant
  events occur
• Provide data compression capability into the
  diagnostics (less data to store and faster data
  transfer)

11
Issues- Collected Data (2/3)

• Some diagnostics require high sampling
  frequencies current technical capabilities may
  be exceeded when operating for large periods
• Use of standards-based fast data transfer on the
  data paths (e.g. PCIe)
• Use of local fast memory with sizes of several GB
  and bandwidth of GB/s
• Use of data compression when bandwidth
  bottlenecks still remain

12
Issues- Collected Data (3/3)

• Data reduction techniques
• Data Compression
• Lossless algorithms
• Keep all the data
• Fast compression and decompression available
• Typical data can be highly compressed
• Loss algorithms can provide extra compression
• Can provide extra compression for specific data
• Variable acquisition rates
• Good for events localized in time
• Data loss for unexpected events

13
Issues RT Data Processing (1/2)

• Higher RTC processing power required for local
  data compression or reduction, monitoring of
  diagnostic output and generation of plasma
  control variables
• Use of processors with parallel processing
  capabilities, high-throughput and low latency
  (multi-core CPUs, FPGAs, DSP )
• hardware processors included on the digitizers
  can process and manage RTC high throughput data
  flow and perform preliminary basic algorithms or
  data compression/reduction
• Use of data processing units where various boards
  are interconnected through a full-mesh topology
  network having low-latency and high bandwidth

14
Issues RT Data Processing (2/2)

• New diagnostics and plasma controllers may
  require an updated real-time control and
  monitoring infrastructure.
• Higher algorithm complexity and higher number of
  input signals
• Lower loop delays for time-critical real-time
  control and distribution of plasma variables and
  events (sometimes under 10 µs)
• Better timing, synchronization and RT messages
  networks.

15
Issues Digital Instrumentation
16
Innovation on Instrumentation

• The referred requirements reveal the importance
  of a platform capable of providing
• High-throughput real-time hardware signal
  processors at the acquisition level
• Low-latency serial gigabit full-mesh
  interconnection between cards
• Integrated RTC event-based acquisition, operation
  and storage
• Integrated synchronism of all digitizer
• Presently the ATCA based instrumentation is a
  good candidate
• ATCA systems are expected to become the backbone
  of the CODAC in Compass

17
Existing CODACs for Long Pulses (1/2)

• LHD (Japan)
• Based on PC cluster
• Communication through TCP/IP
• VXI based systems
• Data Streaming (10 s slices)
• Lossless data compression (ZLIB and JPEG-LS)
• Two stage backup
• Web interface for data analysis

18
Existing CODACs for Long Pulses (2/2)

• EAST (China)
• Distributed data system
• Communications via TCP/IP network
• CAMAC and PCI based systems
• Data streaming (5 s slices)
• Data compression with LZO
• Windows software for data analysis

19
The Firesignal System

• Modular client/server approach with XML plant
  description/ systems integration.
• Standalone operation or interfaced with other
  CODACs.
• Event-driven/Steady State Operation on absolute
  time.
• User friendly interface with remote management
  and participation control room spread over
  campus/web.
• Easy and universal integration (Matlab, IDL,
  SciLab, C, Java, Python...)?.
• Modules connected through CORBA run in various
  OS.
• PlugPlay and HotSwap of hardware

20
Conclusions

• Modern fusion experiments share common needs and
  issues regarding control and data acquisition
• Technological developments in hardware and
  software allow us to address them efficiently
• Existing CODACs have implemented with success
  many of these technologies
• Compass provides an excellent platform for
  deploying and testing the ideas here presented.
• It is desirable for the new CODAC to be flexible,
  in order to accommodate new developments

21
Improvements on Firesignal
Issues Improvements
Data transmission bottleneck Data transmitted through TCP/IP Support to data streaming Distributed server Variable acquisition rates Data compression
Assumes all data with same size and equally spaced in time Improved support for other types of data
Event support added later ? somewhat poor event management Event support from start
Designed mainly for data acquisition More flexible support of mixed acquisitions and real-time control boards
Hardware clients need to be restarted after hot-swap Intrinsic support to hot-swap
22
Support Slides
23
Data Compression
JETs Fast Camera. Results provided by Jesús Vega
(CIEMAT/ES)?
L.Ying, L. Jiarong, L. Guiming, Z. Yingfei, L.
Shia, The EAST Distributed Data System, Fusion
Eng. Des. 82 (2007) 339 - 343