Designing a CODAC for Compass

Presented by:André Sancho Duarte

Outline

• Introduction to the CODAC concept• Compass Tokamak• CODAC in modern fusion experiments

– Issues– Needs– Solutions

• CODAC implementations– Firesignal– Other examples

• Application to Compass

9 October 2008, European Doctorate on Fusion Science and Engineering2

CODAC System

Control, Data Access and CommunicationsSystem for:• Control

– Experiment configuration– Support systems configuration

• Data Acquisition and Retrieval• Communications

– Remote Participation

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CODAC Diagram for ITER

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Compass Tokamak

• Major radius 0.56 m

• Minor radius 0.18 – 0.23 m

• Plasma current < 350 kA

• Magnetic field 1.2 or 2.1 T

• Triangularity ~ 0.5 - 0.7

• Elongation ~ 1.8

• Pulse length < 1 s

• PLH, 1.3 GHz < 0.4 MW

• PNBI 2 0.3 MW

9 October 2008, European Doctorate on Fusion Science and Engineering

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.

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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 @ 2 Msamples/s)

• Small number of pulses during a campaign• Constant monitoring of the machine and its

envolving

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Typical Experiment Flow Chart

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Desired Experimental Chart

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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)

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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

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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

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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

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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.

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Issues – Digital Instrumentation

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Issues Actions

Multitude of different hardware platforms to maintain on the sub-systems. A comprehensive platform easier to deploy and maintain is required.

Design of a ‘generic’ sub-system platform capable of operate as a local controller, a feedback controller and/or a data acquisition unit.

Improve systems availability and reliability

operation for long periods or in real-time

radiation environment

Standards based instrumentation with inherent redundancy and mechanical/thermal characteristics.

Implement electronics redundancy

Malfunction detection/correction.

Local and global management of hardware operation (on enclosures, boards and components) is required to improve maintainability. Required operations are:

- Measurement of temperatures

- Cooling control

- Energy management

- Bus management

Specification of a standards based improved hardware management interface (e.g. Inteligent Platform Management Interface (IPMI), Shelf (crate) management (ShM).

Implement a hardware management infrastructure testbench.

Easier installation/replacement of hardware modules Hardware interface designed for ‘Plug-and-Play’, and ‘Hot-swap’.

Implementation of a prototype of the hardware description using XML

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

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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

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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

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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.

• Plug&Play and HotSwap of hardware

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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

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Improvements on Firesignal

Issues Improvements

Data transmission bottleneck Data transmitted through TCP/IP Support to data streamingDistributed serverVariable acquisition ratesData 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”

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SUPPORT SLIDES

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Data Compression

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Diagnostic/ShotFile Size(MBytes)

Deltacompression

over time (%)

Deltacompression

overframes(rows)

Deltacompression

overframes(columns)

WinZipCompress

(Unix)

KL7/65140 66.7 92.78 92.05 92.04 82.21 78.30

KL7/70231 232.5 94.36 94 .65 85.39 92.09 91.50

KL8/69787 192.8 88.80 90.48 90.11 77.95 75.17

KL8/70231 69.3 94.90 95.31 95.28 96.99 97.40

KL8/70398 138.8 88.26 92.77 92.45 87.27 85.25

JET’s 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