Damon2011 preview


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Damon2011 preview

  1. 1. DaMoN 2011 Paper Preview Organized by Stavros Harizopoulos and Qiong Luo Athens, Greece Jun 13, 2011
  2. 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. 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. 4. IBM T.J. Watson Research Center Enhancing Recovery Using an SSD Bufferpool Extension B. Bhattacharjee, C.A. Lang, G.A.Mihaila, K. A. Ross, M. Banikazemi Bufferpool CPUs DRAM Flash SSD SSD HDD SSD HDD HDD Storage Server 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 SSDs1/2 © 2010 IBM Corporation
  5. 5. IBM T.J. Watson Research Center Contribution of this work Prior work does not retain SSD Bufferpool contents aftercrash/shutdown Leverage 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 overheads2/2 © 2010 IBM Corporation
  6. 6. Athens, Greece, June 13, 2011 1/2 DaMoN 2011
  7. 7. Athens, Greece, June 13, 2011 2/2 DaMoN 2011
  8. 8. A Case for Micro-CellstoresEnergy-Efficient Data Managementon Recycled SmartphonesStavros HarizopoulosSpiros Papadimitriou 1/3 The views contained herein are the authors only and do not necessarily reflect the views of Hewlett-Packard or Google
  9. 9. A Case for Micro-Cellstores Energy-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? 2/3S. Papadimitriou
  10. 10. A Case for Micro-Cellstores Energy-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 3/3S. Papadimitriou
  11. 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. 12. Scalable Aggregation on Multicore Processors Yang Ye, Kenneth Ross, Norases Vesdapunt Columbia University1/4 DaMoN 2011
  13. 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 distributions2/4 DaMoN 2011
  14. 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.)3/4 DaMoN 2011
  15. 15. Architecture Dependence • How architecture-independent can a high- performance implementation be?4/4 DaMoN 2011
  16. 16. Vectorization vs. Compilation in Query Execution Juliusz Sompolski Peter BonczJune 13th, 2011DaMoN 2011, Athens, Greece Marcin Zukowski 1/3
  17. 17. Vectorization vs. Compilation: get rid of interpretation overheadVectorization 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. 2/3
  18. 18. Vectorization vs. CompilationOnce 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. 3/3
  19. 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