THE SQUARE KILOMETER ARRAY (SKA) ESD USE CASE

Transcription

1 THE SQUARE KILOMETER ARRAY (SKA) ESD USE CASE Ronald Nijboer Head ASTRON R&D Computing Group With material from Chris Broekema (ASTRON) John Romein (ASTRON) Nick Rees (SKA Office) Miles Deegan (SKA Office) John Taylor (U. of Cambridge) Michael Wise (ASTRON) 1

2 ASTRON, Offices & Locations Dwingeloo Groningen LOFAR / WSRT Operations LOFAR CEP R&D, Science JIVE, NOVA Borger-Odoorn, Exloo LOFAR core Westerbork WSRT 2

3 Radio Astronomy Doppler shift 21 cm line Galaxy M81 3

4 Square Kilometer Array (SKA) SKA: one Observatory (HQ in UK), two sites (South-Africa & Australia) 4

5 SKA: big scientific questions Testing gravitation Epoch of Reionisation Cosmic Magnetism Cradle of life Large scale structures Turbulent Universe 5

6 SKA Context Diagram The Science Data Processor transforms the Signals into Science Data Products SDP is off-site! (Perth & Cape Town) 6

7 Regional Science Centers Regional Centers are proposed for doing Science with the SKA Data Products 7

8 RSC Functionality Data Discovery Observation database Associated metadata Quick-look data products Flexible catalog queries Integration with VO tools Publish data to VO Data Processing Data Mining Multi-wavelength studies Catalog cross-matching Light-curve analysis Transient classification Feature detection Visualization Reprocessing and calibration High resolution imaging Mosaicing Source extraction Catalog re-creation DM searches 8

9 RSC Requirements Regional Science Centers are being discussed and planned for Requirements do not exist yet H2020 project Aeneas submitted Likely RSCs will be different in different locations SKA SDP type processing will be needed, as well as Data Discovery and Data Mining Design and specification of a distributed, European Science Data Centre (ESDC) to support the pan-european astronomical community in achieving the scientific goals of the SKA 9

10 SDP Key Performance Requirements SDP Local Monitoring & Control C S P Data Processor High Performance ~100 PetaFLOPS Data Intensive ~100 PetaBytes/observation (job) Partially real-time ~10s response time Data Preservation High Volume & High Growth Rate ~100 PetaByte/year Infrequent Access ~few times/year max Delivery System Data Distribution ~100 PetaByte/year from Cape Town & Perth to rest of World Data Discovery Visualisation of 100k by 100k by 100k voxel cubes Partially iterative ~1 Tbytes -1 ~200 ~10 ~10 iterations/job (~6hour) Gbytes -1 Gbytes -1 Observatory 10

11 SDP Functional Breakdown 11

12 Data Parallelism Time & baseline Frequency Visibility data Exploit frequency independence o o o Data parallelism: Dominated by frequency Provides dominant scaling Nothing more needed if each processing node can manage a frequency channel complete processing Grid and degrid Buffered UV data FFT Processing nodes A lot of the processing is embarrasingly (data) parallel, but there will be synchronisation points where data needs to be combined 12

13 SDP Compute Requirements ~50 PFLOPS total sustained, max FFT and Gridding dominant Mixed precision Achieve 10-15% of peak now Large fast working memory (~2 FLOP/byte) Can exchange memory for FLOPs using facetting Fast Storage ~3 Tb/s write, ~30 Tb/s read ~ FLOPS/byte read ~5MW per site 13

14 SDP Compute Characteristics Few, well known applications -> co-design Trivially parallel workloads, baseline architecture leverages this Low arithmatic intensity, thus I/O bound Pseudo real-time + fast storage + batch processing Tight budgets (energy, capital and ops) 14

15 Current Timeline SKA Pre-Construction SKA Construction 2020 Start Early Science 2023 Start Full Operations 15

16 Conclusions SKA is a huge computational challenge RSCs in the process of being defined SDP ~ 50 Pflop (sustained), 5 MW Power is also a major driver. Software complexity is also beyond what has been achieved in astronomy previously. Traditional HPC is not a good match because the problem is bandwidth dominated. SKA would be a perfect Use Case as Big Data application for the EsD projects 16

17 Questions? 17