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 Engineering 2

CODAC System Control, Data Access and Communications System for: Control –Experiment configuration –Support systems configuration Data Acquisition and Retrieval Communications –Remote Participation 9 October 2008, European Doctorate on Fusion Science and Engineering 3

CODAC Diagram for ITER 9 October 2008, European Doctorate on Fusion Science and Engineering 4

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 ~ Elongation ~ 1.8 Pulse length < 1 s P LH, 1.3 GHz < 0.4 MW P NBI 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. 9 October 2008, European Doctorate on Fusion Science and Engineering 6

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 2 Msamples/s) Small number of pulses during a campaign Constant monitoring of the machine and its envolving 9 October 2008, European Doctorate on Fusion Science and Engineering 7

Typical Experiment Flow Chart Goal SettingConfigurationExperiment Data Collection Data Analysis 9 October 2008, European Doctorate on Fusion Science and Engineering 8

Desired Experimental Chart Experiment Data Collection Data AnalysisGoal Setting Configuration 9 October 2008, European Doctorate on Fusion Science and Engineering 9

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) 9 October 2008, European Doctorate on Fusion Science and Engineering 10

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 9 October 2008, European Doctorate on Fusion Science and Engineering 11

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 9 October 2008, European Doctorate on Fusion Science and Engineering 12

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 9 October 2008, European Doctorate on Fusion Science and Engineering 13

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. 9 October 2008, European Doctorate on Fusion Science and Engineering 14

Issues – Digital Instrumentation 9 October 2008, European Doctorate on Fusion Science and Engineering

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 9 October 2008, European Doctorate on Fusion Science and Engineering 16

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 9 October 2008, European Doctorate on Fusion Science and Engineering 17

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 9 October 2008, European Doctorate on Fusion Science and Engineering 18

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 9 October 2008, European Doctorate on Fusion Science and Engineering 19

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 9 October 2008, European Doctorate on Fusion Science and Engineering 20

Improvements on Firesignal IssuesImprovements Data transmission bottleneckData 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 acquisitionMore flexible support of mixed acquisitions and real-time control boards Hardware clients need to be restarted after “hot-swap” Intrinsic support to “hot-swap” 9 October 2008, European Doctorate on Fusion Science and Engineering 21

SUPPORT SLIDES 9 October 2008, European Doctorate on Fusion Science and Engineering 22

Data Compression 9 October 2008, European Doctorate on Fusion Science and Engineering 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)