Processing-In-Memory of Data Filter On Compressed Data
Resumo
The data filter is essential in data-centric applications, but it requires moving large data sets to the processing units. One approach to tackle such a hassle is data compression by using lightweight compression methods such as dictionary encoding. In this paper, we exploit the idea of Processing-In-Memory (PIM) data filters directly over compressed data. The initial experiments show noticeable speed-ups of over 2x against the AVX512 architecture.
Referências
Abadi, D. J., Madden, S., and Ferreira, M. (2006). Integrating compression and execution in column-oriented database systems. SIGMOD, pages 671–682.
Cordeiro, A. S. and et al. (2021). Machine learning migration for efficient near-data processing. In Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pages 212–219.
Feng, Z., Lo, E., Kao, B., and Xu, W. (2015). Byteslice: Pushing the envelop of main memory data processing with a new storage layout. SIGMOD, pages 31–46.
Li, Y. and Patel, J. M. (2013). Bitweaving: fast scans for main memory data processing. SIGMOD, pages 289–300.
Cordeiro, A. S. and et al. (2021). Machine learning migration for efficient near-data processing. In Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pages 212–219.
Feng, Z., Lo, E., Kao, B., and Xu, W. (2015). Byteslice: Pushing the envelop of main memory data processing with a new storage layout. SIGMOD, pages 31–46.
Li, Y. and Patel, J. M. (2013). Bitweaving: fast scans for main memory data processing. SIGMOD, pages 289–300.
Publicado
18/04/2022
Como Citar
KEPE, Tiago R.; MOREIRA, Francis B.; ALVES, Marco A. Z..
Processing-In-Memory of Data Filter On Compressed Data. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO SUL (ERAD-RS), 22. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 101-102.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2022.19182.
