1. 1 Cache-Oblivious Query Processing Bingsheng He, Qiong Luo {saven, luo}@cse.ust.hk Department of Computer Science & Engineering Hong Kong University of Science & Technology

2. 2 Cache-Oblivious Algorithms [Frigo et al., FOCS 1999] Assuming no knowledge about cache parameter values, e.g., cache size Optimal cache complexity For an ideal cache model Two-level hierarchy: cache on top of memory Automatic, optimal cache replacement Fully associative For more realistic cache models as well

3. 3 Motivation Relational database systems have too many knobs to tune for performance. Tuning may be difficult, ineffective, and sometimes infeasible. The memory hierarchy becomes increasingly complex.

4. 4 Memory Hierarchy CPU Capacity256 B8 KB512 KB2 GB80 GB Block size8 B64 B128 B4 KB Access time <1 cyc2 cyc10 cyc376 cyc~10,000cyc Registers L1 L2 Disk Main memory Our focus: CPU caches

5. 5 Cache-Conscious (CC) Techniques Aware of cache parameters of a target level in a specific memory hierarchy Cache block size, e.g., B+-trees Cache capacity, e.g., blocked NLJ Achieve a high performance with correct parameter values

6. 6 Tuning the parameter is difficult The best parameter value varies with the platform. It may be none of the cache parameters of the platform. It may vary with different data and algorithmic characteristics.

7. 7 Our Goal To automatically and consistently achieve a good performance on various memory hierarchies at all times

8. 8 Challenges How to optimize query processing cache- obliviously? Divide-and-conquer methodology Amortization methodology How to achieve a comparable overall performance with fine-tuned cache-conscious algorithms? Work complexity Recursion overhead

9. 9 Divide-and-conquer Fit into the cache Reuse

10. 10 Amortization Reduce the average cost for a set of operations A buffer hierarchy Buffer sizes are recursively defined. Buffer R Partitioner Partition

11. 11 EaseDB: System Architecture

12. 12 Limitations Employ sophisticated data structures and mechanisms. Require some automatic and machine- independent optimization to improve their efficiency.

13. 13 Opportunities Storage models Transactions New architectural features CMP/SMT GPUs Transactional memory

14. 14 Conclusion First cache-oblivious query processor Complexity results on our CO alg. Empirical results of our CO alg. on three hardware platforms in comparison with their CC counterparts http://www.cse.ust.hk/cactus/