Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

Nowadays, fingerprint is the most used biometric trait for individuals identification. In this area, the state-of-the-art algorithms are very accurate, but when the database contains millions of identities, an acceleration of the algorithm is required. From these algorithms, Minutia Cylinder-Code (MCC) stands out for its good results in terms of accuracy, however its efficiency in computational time is not high. In this work, we propose to use two different parallel platforms to accelerate fingerprint matching process by using MCC: (1) a multi-core server, and (2) a Xeon Phi coprocessor. Our proposal is based on heaps as auxiliary structure to process the global similarity of MCC. As heap-based algorithms are exhaustive (all the elements are accessed), we also explored the use an indexing algorithm to avoid comparing the query against all the fingerprints of the database. Experimental results show an improvement up to 97.15x of speed-up, which is competitive compared to other state-of-the-art algorithms in GPU and FPGA. To the best of our knowledge, this is the first work for fingerprint identification using a Xeon Phi coprocessor.Instituto de Investigación en Informátic

Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

Nowadays, fingerprint is the most used biometric trait for individuals identification. In this area, the state-of-the-art algorithms are very accurate, but when the database contains millions of identities, an acceleration of the algorithm is required. From these algorithms, Minutia Cylinder-Code (MCC) stands out for its good results in terms of accuracy, however its efficiency in computational time is not high. In this work, we propose to use two different parallel platforms to accelerate fingerprint matching process by using MCC: (1) a multi-core server, and (2) a Xeon Phi coprocessor. Our proposal is based on heaps as auxiliary structure to process the global similarity of MCC. As heap-based algorithms are exhaustive (all the elements are accessed), we also explored the use an indexing algorithm to avoid comparing the query against all the fingerprints of the database. Experimental results show an improvement up to 97.15x of speed-up, which is competitive compared to other state-of-the-art algorithms in GPU and FPGA. To the best of our knowledge, this is the first work for fingerprint identification using a Xeon Phi coprocessor.Instituto de Investigación en Informátic

Desarrollo de Aplicaciones sobre Cluster Multi-core

Con la aparición de las CPU multi-cores (o Chip-level-Multi-Processor -CMP-), es importante el desarrollo de las técnicas que exploten las ventajas de las CMP para acelerar las aplicaciones paralelas que poseen una gran demanda de cómputo paralelo. En particular, para aplicaciones que requieren de un gran poder computacional de los recursos disponibles, es esencial poder desarrollar estrategias y algoritmos que aprovechen el uso adecuado del hardware. En este trabajo se presentan los objetivos y los desafíos de una línea de investigación que abarca los problemas de mapping, uso adecuado de las nuevas arquitecturas de procesadores, y cómo estas nuevas arquitecturas pueden ser utilizadas para mejorar el desarrollo de algoritmos de computación de grafos y cálculo de matrices, utilizando como base formal el modelo de programación paralela BSP; conjuntamente con algoritmos de búsqueda e indexación sobre grandes colecciones de datos como la Web.Eje: Procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Optimizing multi-core algorithms for pattern search

The suffix array index is a data structure formed by sorting the suffixes of a string into lexicographic order. It is used for string matching, which is perhaps one of those tasks on which computers and servers spend quite a bit of time. Research in this area spans from genetics (finding DNA sequences), to keyword search in billions of web documents, to cyberespionage. The attractiveness is that they are completely “array based” and have some benefits in terms of improving the locality of memory references. In this work we propose and evaluate the performance achieved by a static scheduling algorithm for suffix array query search. We focus on multi-core systems 32- core system. Results show that our proposed algorithm can dramatically reduce the cost of computing string matching.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Consultas sobre espacios métricos en paralelo

En este trabajo se proponen estrategias eficientes y escalables de procesamiento paralelo de consultas, sobre índices distribuidos para bases de datos compuestas de un gran número de objetos en espacios métricos. Las estrategias están diseñadas para satisfacer los requerimientos de las máquinas de búsqueda para la Web, que operan a una gran tasa de consultas por unidad de tiempo, lo cual en este trabajo se logra mediante la combinación de las siguientes estrategias: (a) Particionado del índice de tal manera de reducir el número de procesadores involucrados en la solución de cada consulta, (b) reducción del número de objetos de la base de datos que son directamente comparados con cada consulta, (c) planificación de consultas para balancear la carga de los procesadores, (d) asignación equitativa de recursos de hardware y software a las consultas siendo resueltas, y (e) reducción de latencias mediante una combinación de los modelos síncrono y asíncrono de computación paralela. La eficiencia y escalabilidad de las estrategias propuestas se evalúan utilizando diferentes bases de datos y clusters de computadores, y los resultados muestran que éstas logran mejor desempeño que estrategias alternativas presentadas en la literatura.Eje: Concurso de tesisRed de Universidades con Carreras en Informática (RedUNCI

Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

Nowadays, fingerprint is the most used biometric trait for individuals identification. In this area, the state-of-the-art algorithms are very accurate, but when the database contains millions of identities, an acceleration of the algorithm is required. From these algorithms, Minutia Cylinder-Code (MCC) stands out for its good results in terms of accuracy, however its efficiency in computational time is not high. In this work, we propose to use two different parallel platforms to accelerate fingerprint matching process by using MCC: (1) a multi-core server, and (2) a Xeon Phi coprocessor. Our proposal is based on heaps as auxiliary structure to process the global similarity of MCC. As heap-based algorithms are exhaustive (all the elements are accessed), we also explored the use an indexing algorithm to avoid comparing the query against all the fingerprints of the database. Experimental results show an improvement up to 97.15x of speed-up, which is competitive compared to other state-of-the-art algorithms in GPU and FPGA. To the best of our knowledge, this is the first work for fingerprint identification using a Xeon Phi coprocessor.Instituto de Investigación en Informátic

Similarity search implementations for multi-core and many-core processors

Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications, when processing large volumes of data, query response time can be high enough. In this case, it is necessary to apply mechanisms in order to significantly reduce the average query response time. In this sense, the parallelization of the metric structures processing is an interesting field of research. Modern multi-core and many-core systems offer a very impressive cost/performance ratio. In this paper two new parallel implementations for range queries on Spaghettis data structures have been carried out: one of them on a many-core processor and the other one on a multi-core processor. Both implementations have been compared in terms of execution time and speedup

Hybrid architecture for metric space searches

Every day, new technologies are developed to combine the facilities arranged for shared memory systems with the facilities that provide distributed memory systems. This paper proposes a hybrid system that enables communication between threads running in a shared memory environment and a cluster of computers. To do this we use specific directives provided by MPI to solve a problem of similarity search on metric spaces .This work is part of a larger project that deals with improving query searches over high dimensional spaces, managing large volumes of data, reducing the number of distance evaluations and query response times. While the proposal of this work may be generalized and used for other problems, the results show that the proposed hybrid algorithm allows a significant improvement. This work is part of a larger project that deals with improving the execution of parallel algorithms using a hybrid architecture. The goal is to take advantage of the features and facilities provided by the new parallel architectures that combine distributed and shared memory systems. The former allows to solve large scale problems while the second allows better use of resources.Presentado en el XI Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

A gpu-based implementation for range queries on spaghettis data structure

Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications processing large volumes of generated data, query response times can be high enough. In these cases, it is necessary to apply mechanisms in order to significantly reduce the average query time. In this sense, the parallelization of metric structures is an interesting field of research. The recent appearance of GPUs for general purpose computing platforms offers powerful parallel processing capabilities. In this paper we propose a GPU-based implementation for Spaghettis metric structure. Firstly, we have adapted Spaghettis structure to GPU-based platform. Afterwards, we have compared both sequential and GPU-based implementation to analyse the performance, showing significant improvements in terms of time reduction, obtaining values of speed-up close to 10. Keywords: Databases ? similarity search ? metric spaces ? algorithms ? data structures ? parallel processing ? GPU ? CUD

Scheduling Metric-Space Queries Processing on Multi-Core Processors

Abstract—This paper proposes a strategy to organize metricspace query processing in multi-core search nodes as understood in the context of search engines running on clusters of computers. The strategy is applied in each search node to process all active queries visiting the node as part of their solution which, in general, for each query is computed from the contribution of each search node. When query traffic is high enough, the proposed strategy assigns one thread to each query and lets them work in a fully asynchronous manner. When query traffic is moderate or low, some threads start to idle so they are put to work on queries being processed by other threads. The strategy solves the associated synchronization problem among threads by switching query processing into a bulk-synchronous mode of operation. This simplifies the dynamic re-organization of threads and overheads are very small with the advantage that the overall work-load is evenly distributed across all threads. I