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DaMoN 2011 Paper Preview
Organized by Stavros Harizopoulos and Qiong Luo
Athens, GreeceJun 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
• 17:00-17:30 Coffee Break • 17:30-18:30 Panel: WHITHER HARDWARE-
SOFTWARE CO-DESIGN?
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
IBM T.J. Watson Research Center
© 2010 IBM Corporation
Enhancing Recovery Using an SSD Bufferpool ExtensionB. 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
Server
CPUs DRAM Flash SSD SSD
SSD
HDD
HDDHDD
Storage
Bufferpool
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IBM T.J. Watson Research Center
© 2010 IBM Corporation
Contribution of this work
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Prior work does not retain SSD Bufferpool contents after crash/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 overheads
DaMoN 2011Athens, Greece, June 13, 2011 1/2
DaMoN 2011Athens, Greece, June 13, 2011 2/2
A Case for Micro-CellstoresEnergy-Efficient Data Managementon Recycled Smartphones
Stavros Harizopoulos
Spiros Papadimitriou
The views contained herein are the authors' only and do not necessarily reflect the views of Hewlett-Packard or Google
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S. Papadimitriou
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?
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S. Papadimitriou
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
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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
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Scalable Aggregation on
Multicore Processors
Yang Ye, Kenneth Ross, Norases VesdapuntColumbia University
DaMoN 2011
2/4 DaMoN 2011
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
3/4 DaMoN 2011
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
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Architecture Dependence
• How architecture-independent can a high-performance implementation be?
DaMoN 2011
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Vectorization vs. Compilationin Query Execution
Juliusz SompolskiPeter BonczMarcin Zukowski
June 13th, 2011DaMoN 2011, Athens, Greece
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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.
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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.
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