Vectorization vs. compilation in query execution

@inproceedings{Sompolski2011VectorizationVC,
  title={Vectorization vs. compilation in query execution},
  author={Juliusz Sompolski and Marcin Zukowski and Peter A. Boncz},
  booktitle={International Workshop on Data Management on New Hardware},
  year={2011}
}
Compiling database queries into executable (sub-) programs provides substantial benefits comparing to traditional interpreted execution. Many of these benefits, such as reduced interpretation overhead, better instruction code locality, and providing opportunities to use SIMD instructions, have previously been provided by redesigning query processors to use a vectorized execution model. In this paper, we try to shed light on the question of how state-of-the-art compilation strategies relate to… 

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References

SHOWING 1-10 OF 15 REFERENCES

Compiled Query Execution Engine using JVM

Both an interpreted and a compiled query execution engine are developed in a relational, Java-based, in-memory database prototype, and experimental results show that, despite both engines benefiting from JIT, the compiled engine runs on average about twice as fast as the interpreted one, and significantly faster than an in- memory database prototype.

MonetDB/X100: Hyper-Pipelining Query Execution

An in-depth investigation to the reason why database systems tend to achieve only low IPC on modern CPUs in compute-intensive application areas, and a new set of guidelines for designing a query processor for the MonetDB system that follows these guidelines.

Balancing vectorized query execution with bandwidth-optimized storage

A new database system architecture is presented, realized in the MonetDB/X100 prototype, that combines a coherent set of new architecture-conscious techniques that are designed to work well together and achieves in-memory performance often one or two orders of magnitude higher than the existing approaches.

Conjunctive selection conditions in main memory

It is demonstrated that branch misprediction has a substantial impact on the performance of an algorithm for applying selection conditions, and a cost model that takes branch prediction into account is proposed and a query optimization algorithm that chooses a plan with optimal estimated cost is developed.

Block oriented processing of relational database operations in modern computer architectures

It is argued that a block-oriented processing strategy for database operations can lead to better utilization of the processors and caches, generating significantly higher performance.

Generating code for holistic query evaluation

The results show that HIQUE satisfies its design objectives, while its efficiency surpasses that of both well-established and currently-emerging query processing techniques.

DSM vs. NSM: CPU performance tradeoffs in block-oriented query processing

This paper focuses on the CPU efficiency tradeoffs of tuple representations inside the query execution engine, while tuples flow through a processing pipeline, and analyzes the performance in the context of query engines using so-called "block-oriented" processing.

Monet; a next-Generation DBMS Kernel For Query-Intensive Applications

This thesis is a reference to the Monet system in all its detail, and outlines an SQL front-end that uses Monet as a back-end, for constructing a full-fledged SQL compliant RDBMS including ACID properties.

Volcano - An Extensible and Parallel Query Evaluation System

  • G. Graefe
  • Computer Science
    IEEE Trans. Knowl. Data Eng.
  • 1994
Volcano is the first implemented query execution engine that effectively combines extensibility and parallelism, and is extensible with new operators, algorithms, data types, and type-specific methods.

Improving hash join performance through prefetching

This work shows that the standard hash join algorithm/or disk-oriented databases (i.e. GRACE) spends over 73% of its user time stalled on CPU cache misses, and explores the use of prefetching to improve its cache performance.