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DaMoN 2011 Paper Preview

DaMoN 2011 Paper Preview. Organized by Stavros Harizopoulos and Qiong Luo Athens, Greece Jun 13, 2011. Preview of Afternoon Program. 13:00-15:00 Paper Session I: FLASH DISKS, FPGAS, AND SMARTPHONES 15:00-15:30 Coffee Break 15:30-17:00 Paper Session II: MODERN CPUS AND MEMORY SYSTEMS

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DaMoN 2011 Paper Preview

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  1. DaMoN 2011 Paper Preview Organized by Stavros Harizopoulos and Qiong Luo Athens, Greece Jun 13, 2011

  2. Preview of Afternoon Program • 13:00-15:00 Paper Session I: FLASH DISKS, FPGAS, AND SMARTPHONES • 15:00-15:30 Coffee Break • 15:30-17:00 Paper Session II: MODERN CPUS AND MEMORY SYSTEMS • 17:00-17:30 Coffee Break • 17:30-18:30 Panel: WHITHER HARDWARE-SOFTWARE CO-DESIGN?

  3. Paper Session I: FLASH DISKS, FPGAS, AND SMARTPHONES • Enhancing Recovery Using an SSD Buffer Pool Extension • Towards Highly Parallel Event Processing through Reconfigurable Hardware • QMD: Exploiting Flash for Energy Efficient Disk Arrays • A Case for Micro-Cellstores: Energy-Efficient Data Management on Recycled Smartphones

  4. Enhancing Recovery Using an SSD Bufferpool Extension B. Bhattacharjee, C.A. Lang, G.A.Mihaila,K. A. Ross, M. Banikazemi All prior work including “SSD Bufferpool Extensions for Database Systems” By M. Canim, G.A.Mihaila, B. Bhattacharjee, K. A. Ross, C.A. Lang, PVLDB 2010 Focused on exploiting Random access capability of SSDs Latency of SSDs Persistence of SSDs 1/2

  5. Contribution of this work Prior work does not retain SSD Bufferpool contents after crash/shutdownLeverage persistence to exploit SSD Bufferpool contents for Crash recovery of a database system Normal shutdown and start Demonstrate Shorter recovery times Improved transaction performance after recovery With minimal overheads 2/2

  6. QMD: Exploiting Flash for Energy-Efficient Disk Arrays Sean Snyder1, Shimin Chen2, Panos K. Chrysanthis1, Alexandros Labrinidis1 1University of Pittsburgh 2Intel Athens, Greece, June 13, 2011 DaMoN 2011 1/2

  7. QMD: 99 second overview Athens, Greece, June 13, 2011 DaMoN 2011 2/2 • Goal: energy proportionality of data storage • Facts: • HDDs are bad: idle power = 60 - 80% of active power • SDDs are good, but expensive • Our proposal: • Utilize SSDs attached to HDD configurations (RAID) • Currently used for redundancy • Use for energy reduction • Make it so that it is ok to switch off HDDs

  8. A Case for Micro-CellstoresEnergy-Efficient Data Managementon Recycled Smartphones Stavros Harizopoulos Spiros Papadimitriou 1/3 The views contained herein are the authors' only and do not necessarily reflect the views of Hewlett-Packard or Google

  9. A Case for Micro-CellstoresEnergy-Efficient Data Management on Recycled Smartphones • >1 billion smartphones expected to become obsolete in the next 5 years • What happens to old computers, servers, cell phones? • Can we do better? 2/3

  10. A Case for Micro-CellstoresEnergy-Efficient Data Management on Recycled Smartphones • Repurpose old smartphones • Power-profile characterization of current-generation smartphone • Initial evaluation: up to 6x more efficient (vs. other “wimpy” nodes) on scan workloads • Motivate energy-efficient, sustainable solutions 3/3

  11. Paper Session II: MODERN CPUS AND MEMORY SYSTEMS • Scalable Aggregation on Multicore Processors • How to Efficiently Snapshot Transactional Data: Hardware or Software Controlled? • Vectorization vs. Compilation in Query Execution

  12. Scalable Aggregation on Multicore Processors Yang Ye, Kenneth Ross, Norases Vesdapunt Columbia University DaMoN 2011 DaMoN 2011

  13. Utilization Challenge • What is the best way to use the shared/partitioned resources for computations like aggregation? • Issues: • Coordination overhead of shared data structures • Latches and/or atomic operations • Contention • Space overhead of replicated data structures • With n threads, each thread gets 1/nth of the shared cache and RAM • Robustness under many input data distributions DaMoN 2011 DaMoN 2011

  14. Niagara vs Nehalem • Prior work on Sun Niagara T1 and T2 machines • Some TPC benchmark winners use the T2 (!) • Many threads: high parallelism • Do these results generalize to other architectures such as the Nehalem processor? • Differences in: • Clock speed • Relative cost of a miss • Degree of parallelism • Memory hierarchy & consistency model • Core sophistication (pipelines, branch prediction, etc.) DaMoN 2011 DaMoN 2011

  15. Architecture Dependence How architecture-independent can a high-performance implementation be? DaMoN 2011 DaMoN 2011

  16. Juliusz Sompolski Peter Boncz Marcin Zukowski Vectorization vs. Compilationin Query Execution June 13th, 2011 DaMoN 2011, Athens, Greece 1/3

  17. 2/3 Vectorization vs. Compilation:get rid of interpretation overhead Vectorization processes data in blocks to amortize interpretation overhead over multiple tuples. JIT query compilation generates and compiles specialized program for each query remove interpretation at all. Both get rid of interpretation overhead.

  18. 3/3 Vectorization vs. Compilation Once we’re rid with interpretation overhead... are they worth combining? Vectorized systems could use compilation to move to tuple-at-a-time processing without interpretation overhead in some operations. Existing systems using JIT compilation still choose to work tuple-at-a-time. Should they sometimes switch to vector-at-a-time model? Case studies and examples.

  19. Summary • An exciting afternoon program ahead • Seven interesting papers in two sessions • Flash disks, FPGAs, and (recycled) smartphones • Modern (multicore) CPUs and memory systems • Panel with experts on hardware-software co-design issues

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