Thread partitioning and value prediction for exploiting speculative thread-level parallelism
Speculative thread-level parallelism has been recently proposed as a source of parallelism to improve the performance in applications where parallel threads are hard to find. However, the efficiency of this execution model strongly depends on the performance of the control and data speculation techn...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2004 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/101437 |
| Acceso en línea: | https://hdl.handle.net/2117/101437 https://dx.doi.org/10.1109/TC.2004.1261823 |
| Access Level: | acceso abierto |
| Palabra clave: | Parallel processing (Electronic computers) Compilers (Computer programs) Speculative thread-level parallelism Value prediction Branch prediction Thread spawning policies Clustered architectures Processament en paral·lel (Ordinadors) Compiladors (Programes d'ordinador) Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Sumario: | Speculative thread-level parallelism has been recently proposed as a source of parallelism to improve the performance in applications where parallel threads are hard to find. However, the efficiency of this execution model strongly depends on the performance of the control and data speculation techniques. Several hardware-based schemes for partitioning the program into speculative threads are analyzed and evaluated. In general, we find that spawning threads associated to loop iterations is the most effective technique. We also show that value prediction is critical for the performance of all of the spawning policies. Thus, a new value predictor, the increment predictor, is proposed. This predictor is specially oriented for this kind of architecture and clearly outperforms the adapted versions of conventional value predictors such as the last value, the stride, and the context-based, especially for small-sized history tables. |
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