Suffix Array Performance Analysis for Multi-Core Platforms

• Autores: Gil Costa, Graciela Verónica; Ochoa, Cesar; Printista, Alicia Marcela
• Año de publicación: 2013
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Multicore.; Suffix array.
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Performance analysis helps to understand how a particular invocation of an algorithm executes. Using the information provided by specific tools like the profiler tool Perf or the Performance Application Programming Interface (PAPI), the performance analysis process provides a bridging relationship between the algorithm execution and processor events according to the metrics defined by the developer. It is also useful to find performance limitations which depend exclusively on the code. Furthermore, to change an algorithm in order to optimize the code requires more than understanding of the obtained performance. It requires understanding the problem being solved. In this work we evaluate the performance achieved by a suffix array over a 32-core platform. Suffix arrays are efficient data structures for solving complex queries in a number of applications related to text databases, for instance, biological databases. We perform experiments to evaluate hardware features directly aimed to parallelize computation. Moreover, according to the results obtained by the performance evaluation tools, we propose an optimization technique to improve the use of the cache memory. In particular, we aim to reduce the number of cache memory replacement performed each time a new query is processed.
Fil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Laboratorio de Inv.en Inteligencia Artificial; Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina;
Fil: Ochoa, Cesar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Laboratorio de Inv.en Inteligencia Artificial; Argentina;
Fil: Printista, Alicia Marcela. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Laboratorio de Inv.en Inteligencia Artificial; Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina;