CAREER: Bridging Databases and Computer Architecture: Optimizing DBMS for Deep Memory Hierarchies
NSF Project 0133686
PI: Anastasia Ailamaki

Project Description:

Recent evaluation studies on modern processors show that database workloads do not take advantage of the sophisticated processor and memory architectures as much as scientific workloads do. This is counter-intuitive, because many modern database applications (e.g., data mining) are compute and memory intensive. I have started investigating this problem by examining the behavior of four commercial database management systems running microbenchmarks and TPC on an Intel platform to find common performance bottlenecks. The striking common result is that, even when running microbenchmarks, DBMSs spend most of their execution time on memory stalls. In addition, the dominant memory bottlenecks are due to data misses on the second-level cache and misses on the first-level instruction cache. My research focuses on techniques to alleviate performance bottlenecks throughout the memory hierarchy (including caches, memory, and disks).


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