Memory Access

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Database Architecture Optimized for
the New Bottleneck: Memory Access
Peter Boncz
Stefan Manegold
Martin Kersten
Data Distilleries B.V.
Amsterdam
The Netherlands
CWI
Amsterdam
The Netherlands
[email protected]
{S.Manegold,M.Kersten}@cwi.nl
Contents
• How Memory Access works
• Simple Scan Experiment
• Consequences for DBMS
– Data Structures: vertical decomposition
– Algorithms: tune random memory access
• Partitioned Join Algorithms
– Monet Experiments
– Accurate Cost Models
• Conclusion
2
CPU Speed vs. Memory Speed
Moore’s Law:
CPU speed doubles
every 3 years
3
Memory Access in Hierarchical Systems
4
Simple Scan Experiment
5
Consequences for DBMS
• Memory access is a bottleneck
• Prevent cache & TLB misses
• Cache lines must be used fully
• DBMS must optimize
– Data structures
– Algorithms (focus: join)
6
Vertical Decomposition in Monet
7
Partitioned Joins
• Cluster both input relations
• Create clusters that fit in
memory cache
• Join matching clusters
• Two algorithms:
– Partitioned hash-join
– Radix-Join
(partitioned nested-loop)
8
Partitioned Joins: Straightforward Clustering
• Problem:
Number of clusters exceeds number of
– TLB entries ==> TLB trashing
– Cache lines ==> cache trashing
• Solution:
Multi-pass radix-cluster
9
Partitioned Joins: Multi-Pass Radix-Cluster
• Multiple clustering
passes
• Limit number of
clusters per pass
• Avoid cache/TLB
trashing
• Trade memory cost for
CPU cost
• Any data type
(hashing)
10
Monet Experiments: Setup
• Platform:
– SGI Origin2000 (MIPS R10000, 250 MHz)
• System:
– Monet DBMS
• Data sets:
– Integer join columns
– Join hit-rate of 1
– Cardinalities: 15,625 - 64,000,000
• Hardware event counters
– to analyze cache & TLB misses
11
Monet Experiments: Radix-Cluster
(64,000,000 tuples)
12
Accurate Cost Modeling: Radix-Cluster
13
Monet Experiments: Partitioned Hash-Join
14
Monet Experiments: Radix-Join
15
Monet Experiments: Overall Performance
(64,000,000 tuples)
16
Conclusion
• Problem:
– Memory access is increasingly the most important bottleneck
for database performance
• Solutions:
– Vertical decomposition improves column-wise data access
– Radix-algorithms optimize join performance
• General:
– Algorithms can be tuned to achieve optimal memory access
– Detailed and accurate estimation of memory cost is possible
Monet homepage: www.cwi.nl/~monet
17
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