ASKAP Central Processor: Design and Implementa8on
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1 ASKAP Central Processor: Design and Implementa8on Calibra8on and Imaging Workshop 2014 Ben Humphreys ASKAP So(ware and Compu3ng Project Engineer 3rd - 7th March 2014 ASTRONOMY AND SPACE SCIENCE
2 Australian SKA Pathfinder (ASKAP) Sited at the Murchison Radio Observatory, Western Australia Observes between 0.7 and 1.8 GHz 36 antennas, 12m diameter Started construc3on July 2006 Data rate from correlator ~2.5GB/s A DVD every two seconds! Science processing requirement 200TF/s for basic capabili3es 800+TF/s for high angular resolu3on spectral line imaging
3 28 Gbit/s Correlator Card 1 Card 2 Card 3 Card 4 Card 5 Card 6 Card 7 Card 8 Card 9 Card 10 Card 11 Card 12 Card 13 Card 14 Card 15 Card Ethernet Switch Correlator Control Computer 1 Correlator Control Computer 2 Correlator Control Computer 3 Correlator Control Computer 4 Ethernet Switch ~20 Gbit/s DWDM Terminal To Perth Card 96
4 The Pawsey High Performance Compu8ng Centre for SKA Science AUD$80M super- compu3ng centre Supports storage and processing of data from the Australian SKA Pathfinder and the Murchison Widefield Array Construc3on completed April 2013
5 ASKAP Central Processor 472 x Cray XC30 Compute Nodes 200 TFlop/s Peak Cray Aries (Dragonfly topology) Cray Sonexion Lustre Storage 1.4 PB usable 480 x 4TB Disk Drives, RAID 6 + Hot Spares Approximately 30 GByte/s I/O performance
6 Cray XC30 Compute Nodes 472 x Cray XC30 Compute Nodes 2 x 3.0 GHz Intel Xeon E v2 (Ivy Bridge) CPUs 10 Cores per CPU (20 per node) 64 GB DDR3-1866Mhz RAM Image Credit: Cray
7 ASKAP Central Processor 16 x Ingest Nodes 2 x 2.0 GHz Intel Xeon E (Sandy Bridge) CPUs + 64GB RAM 10 GbE connec3vity to MRO 4x FDR Infiniband connec3vity to compute nodes and Lustre filesystem 2 x Login Nodes 2 x Data Mover Nodes (dedicated to external data transfers) 1.4 PB (usable) Cray Sonexion Lustre storage
8 I/O & Network Hall
9 Tape Hall
10 From MRO DWDM Terminal 2 x 56 Gbit/s IB per node 2 x MMUs Cray Sonexion Storage 6 x SSUs 2 x 10 GbE per node Ingest Nodes Node 1 Cray XC30 Node 2 Ethernet Switch Node 3 Node 4 Node 5 Node 6 Node 7 Node 8 Node 9 Node 10 Node 11 Node 12 Node 13 Node 14 FDR Infiniband Switch Router Node Router Node Router Node Router Node Node 15 Node 16 (472 compute nodes)
11 Ingest Pipeline Ingest Pipeline Telescope Observation Manager Correlator Telescope metadata Visibilities Merge Metadata & Visibilities Flag (From RFI database) Flag (On the fly detection) Apply Calibration Channel Averaging (16416 to 304) Downstream Pipelines Downstream Pipelines Access database of known RFI sources Obtain latest calibration solution Channel Averaging (304 to ~30) Downstream Pipelines Services RFI Source Service Calibration Data Service
12 Calibra8on and Imaging Pipelines Calibration Pipeline Services Channels (18.5kHz) ccalibrator Calibration Solution Calibration Data Service Large-N (eg. Spectral Line) Imager Pipeline UV Data Channels (18.5kHz) Imager (cimager) Image Cube Source Finder/ Identifier Source Catalog Ingest Pipeline Small-N (e.g. Continuum) Imager Pipeline Sky Model Service UV Data 304 Channels (1MHz) Imager (cimager) Images Source Finder/ Identifier Source Catalog Transient Detector Pipeline ~30 Channels (10MHz) Transient Imager (cfimager) Images Transient Finder/Identifier Transient Detections Light Curve Service
13 Data Services Sky Model Service Provides access to the Global Sky Model (GSM), an all- sky database with flux measurements in an appropriate frequency range to ASKAP RFI Source Service Responsible for managing and providing access to a database of known RFI sources that may impact ASKAP observa3ons Calibra8on Data Service Provides an interface to a database containing calibra3on parameters
14 Challenges and Lessons Learned
15 Challenges and Lessons Learned Per process memory footprint Disk I/O Fault Tolerance & Error Handling
16 Thank you CSIRO Astronomy and Space Science Ben Humphreys ASKAP Compu3ng Project Engineer t e ben.humphreys@csiro.au w ASTRONOMY AND SPACE SCIENCE
17 Per process memory footprint Approximately 7GB per spectral line imaging process Approximately 16GB per con3nuum imaging process (40 GB with taylor terms 0, 1, 2) Our target was 4GB per core, actual system 3.2GB per core!! Currently run MPI everywhere no mul3- threading 1 core == 1 process Lots of shared- memory parallelism could be exploited Gridding/degridding, genera3on of convolu3on func3ons, FFT
18 Disk I/O Many inhibitors to stable performance Load- balancing (OSS selec3on) Raid- Check (Sunday night slow- down) Lustre filesystem capable of achieving 30GB/s write Yet we typically see 2GB/s wri3ng measurement sets
19 Fault Tolerance Its hard to retrofit fault tolerance! Possible to get Checkpoint/Restart easily E.g. BLCR Time to checkpoint 30TB RAM at 15 GB/s is ~30 mins Beuer models Checkpoint at end of minor cycle Only checkpoint meta- data (e.g. which spectral channels have been wriuen to disk and which are yet to be processed)
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