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Advancements and Challenges in Computing Hardware for Next-Gen Astronomy

This report outlines key discussions from the CALIM 2009 workshop, focusing on the latest trends and challenges in computing hardware relevant to radio astronomy. Presentations covered a range of topics including LOFAR's computing demands, software correlators, FPGA implementations, and scalability issues for future SKA projects. Emphasizing collaboration across research groups and the industry, the report highlights the need for effective data handling, efficient algorithms, and long-term development strategies in the face of increasing data rates and computing power requirements.

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Advancements and Challenges in Computing Hardware for Next-Gen Astronomy

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  1. CALIM2009 report on Computing Hardware Editor: Tim Cornwell Presenter: Chris Broekema

  2. Attendees • Chris Broekema • Tim Cornwell • Danielle Fenech • Panos Lampropoulos • Simon Ratcliffe • Note that we had a small number of people for a Red topic

  3. Relevant talks • Chris: LOFAR CP • Panos: LOFAR EOR • Pandey: MSSS pipeline • Miguel: Software holography (MOFF) • Aaron: PAPER • Tim: ASKAP • Bill: Multi-core • Simon: Stream processing

  4. Inventory: Software correlators • Small scale off line • Standard now, many groups • Large scale real time • Blue Gene • High efficiency • Required lots of work to get data flow right • Lots of research into: • Other hardware e.g. Cell, GPU, i7 • Single Digital Backend • Data rates are main challenge • Important long term implications • New concepts • MOFF • Limited applicability?

  5. Inventory: Hardware correlators • WIDAR • EVLA and eMERLIN • Packet switched + FPGA • E.g. CASPER implementation • Flexibility • Route to hybridization • FPGA/Custom ASICS/Modular ASICS • Convergence with Software Correlators

  6. Inventory: Calibration and Imaging • Hardware solutions • Simple clusters • Accelerators: FPGA, Cell, GPUs • Limitations • Large diversity in algorithms • Getting data to the processor • Multiple, fluid, programming models • Matching algorithms and hardware • Coarse e.g. Efficient cross-node parallelization • Fine e.g. efficient use of multi- and many-cores • Many (n>>8) cores • Scaling to e.g. 10000 cores • Accommodating very large data flows

  7. Unresolved issues: Scaling • Factors • Data handling • Computing • Memory • Bandwidth • Green power • Reliability • Algorithms • All scale differently with time • Algorithms scale differently

  8. Unresolved issues: Other • Resources required for long term development? • Hardware engineering • Software engineering to map algorithms • Involvement with industry? • Computing and networking • Models for collaboration? • Between research groups • With industry

  9. Relevance to SKA • Hardware requirements and priorities very uncertain • Needs continuous re-evaluation • All hardware issues are on the critical path for SKA • Risk management vital • Community maturity to get close to top of Top 500 • Very limited experience of HPC compared to LHC • Lack of urgency and resources

  10. Relevance to SKA • LOFAR demonstrated Teraflop-range real-time correlation on computer • SDB tests large scale computing beyond LOFAR • LOFAR EOR and ASKAP major drivers at 100 Tflops level • Many diverse approaches • LOFAR, MeerKAT, ASKAP, MWA

  11. Recommendations • Foster and facilitate collaborations • Track activities, hold topic meetings • SPDO add capability to track HPC • Increase HPC involvement in CALIM • Coordinate attendance at meetings

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