Presentation of: DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing -- M Zukowski, N Nes, P Boncz - 2008

1. Tuple Representation
Row vs Column
DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing
M Zukowski, N Nes, P Boncz - 2008

2. Personal Take
Directly applicable? Yes
Tuples in Cypher Runtime (e.g., “ExecutionContext”)
More interesting when we have better type information

3. Scope
Tuple representation within execution engine
Not about storage format
(Block-oriented processing)

4. Optimize
CPU cache hit/miss ratio
Row/column access cost
SIMD opportunities
Instructions unbound by data-/code-dependencies

5. Questions
(Dis)advantages of NSM & DSM?
Opportunities/challenges (SIMD, prefetching, block size)?
Dynamic transformation possible?

6. Tuple Representations
Arrays (stored within blocks)
Simple (no compression, NULL stored) → fast access
Variable-length uses pointers - also not evaluated…

7. Tuple Representations - Column/DSM

8. Tuple Representations - Row/NSM

9. Evaluation
(1) Sequential: SUM & ADD
(2) Random: Group + Aggregate
(3) Data Conversion: NSM <--> DSM

10. Sequential Access - SUM
MATCH (n) RETURN sum(n.c1)
4-byte int array
Array size varied (simulates diff block sizes)
Unused column count varied

11. Sequential Access - ADD
MATCH (n) RETURN n.c1 + n.c2 AS c3

12. Sequential Access

13. Random Access - Group & Aggregate
MATCH (n) RETURN n.key, sum(n.c1) , …, sum(n.cn)
4M rows
1/4/16 columns
1-4M unique keys

14. Random Access

15. Data Conversion - NSM <--> DSM

16. Data Conversion - NSM <--> DSM
~1ns per row:column

17. TPC-H Query 1
Extra curricular activity, for the inspired

18. Summary
DSM wins sequential (simple access, smaller)
NSM wins when out-of-cache & many accessed columns
DSM can SIMD in sequential (project, select, etc.)
Efficient NSM <--> DSM is feasible