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Implementation of a streaming database management system on a Blue Gene architecture for measurement data processing. Erik Zeitler Uppsala data base lab www.it.uu.se/research/group/udbl
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Looking out into space: Use large radio telescopes!
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Problem: Size matters We have hit the limit
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Use many large radio telescopes? Augment the measurements using signal processing They act together as a HUGE telescope Look in one direction only Expensive…
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Solution Use a huge amount of small antennas This enables new scientific applications (and challenges) } Broad band Multi direction receivers
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Scientific applications Re-ionization epoch the 1 st 10 5 years – hydrogen forming Deep Extragalactic Surveys To boldly go… Transient Sources All-sky surveys of –gamma bursts –flare stars –supernovae Ultra High Energy Cosmic Rays Pulsars
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Antennas, antennas, antennas… Broad band radio receiver 80…300 MHz, 3 dimensions Produces 0.9 Gbps raw data Central site + 20 outstations located within a circular area, diameter 350 km 13 10 3 antennas
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System overview Antennas Basic beam forming FPGAs Network GbE, 10GbE Central Processing facility Linux clusters, IBM Blue Gene/L Off line analysis PCs, workstations, Blue Gene
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System overview
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Central processing tasks FFT Signal correlation Calibration RFI mitigation (noise from human activities) Stratosphere plasma Subtracting known objects Transient analysis Peak detection
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Computing challenges Multiple incoming data streams 20 Tbps Multiple experiments Complex computations Demand for rapid reconfiguration of computing systems Use case: On-line transient analysis
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Central processing facilities On line processing Linux cluster (buffering) Light weight BG/L (beam) 6 racks 6144 compute nodes + 96 I/O nodes Off-line processing Linux clusters, SAN, GRID, …
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Blue Gene Dataflow supercomputer LLNL installation: 64 racks (65536 CPUs) 70 TFLOPS on the size of a tennis court
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BG/L architecture I/O node: 2x PPC440@700MHz Linux Each I/O node coordinates 64 compute nodes 512 MB RAM Compute node: 2x PPC440@700MHz Single threaded light weight OS Typically: –1 CPU for computation –1 CPU for communication 512 MB RAM
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User agent
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UDBL project Implement a very high performance stream database manager based on AmosII DB kernel (http://user.it.uu.se/~udbl/amos/)http://user.it.uu.se/~udbl/amos/ Utilize the BG/L computing environment for scalable data stream queries involving user-defined computations Implement specialized query optimization: Planning BG/L node configuration for given stream queries Re-configuration when interesting phenomena occur
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This far (after 4 months) Implementing primitives for data ~ Computation Aggregation Communication Fusion Proof of concept cases Signal processing Peak detection Stream join Benchmark Based on real LOFAR/LOIS data Performance analysis for stream databases
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A simple example gnuplot(peakdetect(vector_elements(wina gg(vector_elements(readlofarvectorfile( "temp.DAT")),256,256))));
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Other application areas Other space physics research areas projects at IRFU Network traffic analysis Financial (stock market) information Content analysis of streaming media
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Questions?
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