Competitive cost-effective memory access predictor through short-term online SVM and dynamic vocabularies
In recent years, there has been a significant increase in the processing of massive amounts of data, driven by the growing demands of mobile systems, parallel and distributed architectures, and real-time systems. This applies to various types of platforms, both specific and general-purpose. Despite...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/168823 |
| Acceso en línea: | https://hdl.handle.net/11441/168823 https://doi.org/10.1016/j.future.2024.107592 |
| Access Level: | acceso abierto |
| Palabra clave: | Address prediction Support vector machines Memory Access Superscalar processors Machine learning |
| Sumario: | In recent years, there has been a significant increase in the processing of massive amounts of data, driven by the growing demands of mobile systems, parallel and distributed architectures, and real-time systems. This applies to various types of platforms, both specific and general-purpose. Despite numerous advancements in Computer Systems, a critical challenge remains: the efficiency and speed of memory access. This bottleneck is being addressed through cache prefetching, that is, by predicting the next memory address to be accessed and then by having always prefetched in the cache system those data to be used shortly by the processor. This paper explores established intelligent techniques for address prediction, examining their limitations and analyzing the memory access patterns of popular software applications. Building on the successes of previous intelligent predictors based on Machine and Deep Learning models, we introduce a new predictor, SVM4AP (Support Vector Machine For Address Prediction), designed to overcome the identified drawbacks of its predecessors. The architecture of SVM4AP improves the trade-off between performance and cost, compared to those previous proposals in the literature, achieving high accuracy through short-term learning. Comparisons are made with two prominent predictors from the literature: the classical DFCM (Differential Finite Context Method) and the contemporary Deep Learning-based DCLSTM (Doubly Compressed Long-Short Term Memory). The results demonstrate that SVM4AP achieves superior cost-effectiveness across various configurations. Simulations reveal that SVM4AP configurations dominate both DFCM and DCLSTM counterparts, forming the majority of the first Paretto front. Particularly noteworthy is the significant advantage of our proposal for small-size predictors. Furthermore, we release an open-source tool enabling the scientific community to reproduce the results presented in this paper using a set of benchmark traces. |
|---|