Performance Evaluation of Sparse Matrix Products in UPC

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-012-0796-4[Abstract] Unified Parallel C (UPC) is a Partitioned Global Address Space (PGAS) language whose popularity has increased during the last years owing to its high programmability and reasonable performance through an efficient exploitation of data locality, especially on hierarchical architectures like multicore clusters. However, the performance issues that arise in this language due to the irregular structure of sparse matrix operations have not yet been studied. Among them, the selection of an adequate storage format for the sparse matrices can significantly improve the efficiency of the parallel codes. This paper presents an evaluation, using UPC, of the most common sparse storage formats with different implementations of the matrix-vector and matrix-matrix products, which are key kernels in many scientific applications.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación; AP2008-01578Ministerio de Ciencia e Innovación; CAPAP-H3; TIN2010-12011-

A 2D algorithm with asymmetric workload for the UPC conjugate gradient method

This is a post-peer-review, pre-copyedit version of an article published in Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-014-1300-0[Abstract] This paper examines four different strategies, each one with its own data distribution, for implementing the parallel conjugate gradient (CG) method and how they impact communication and overall performance. Firstly, typical 1D and 2D distributions of the matrix involved in CG computations are considered. Then, a new 2D version of the CG method with asymmetric workload, based on leaving some threads idle during part of the computation to reduce communication, is proposed. The four strategies are independent of sparse storage schemes and are implemented using Unified Parallel C (UPC), a Partitioned Global Address Space (PGAS) language. The strategies are evaluated on two different platforms through a set of matrices that exhibit distinct sparse patterns, demonstrating that our asymmetric proposal outperforms the others except for one matrix on one platform.Ministerio de Economía y Competitividad; TIN2013-42148-PXunta de Galicia; GRC2013/055United States. Department of Energy; DEAC03-76SF0009

UPCBLAS : a numerical library for unified parallel C with architecture-aware optimizations

[Abstract] The popularity of Partitioned Global Address Space (PGAS) languages has increased during the last years thanks to their high programmability and performance through an efficient exploitation of data locality, especially on hierarchical architectures like multicore clusters. This PhD Thesis describes UPCBLAS, a parallel library for numerical computation using the PGAS Unified Parallel C (UPC) language. The routines are built on top of sequential BLAS and SparseBLAS functions and exploit the particularities of the PGAS paradigm, taking into account data locality in order to achieve a good performance. However, the growing complexity in computer system hierarchies due to the increase in the number of cores per processor, levels of cache (some of them shared) and the number of processors per node, as well as the high-speed interconnects, demands the use of new optimization techniques and libraries that take advantage of their features. For this reason, this Thesis also presents Servet, a suite of benchmarks focused on detecting a set of parameters with high in uence on the overall performance of multicore systems. UPCBLAS routines use the hardware parameters provided by Servet to implement optimization techniques that improve their performance. The performance of the library has been experimentally evaluated on several multicore supercomputers and compared to message-passing-based parallel numerical libraries, demonstrating good scalability and efficiency. UPCBLAS has also been used to develop more complex numerical codes in order to demonstrate that it is a good alternative to MPI-based libraries for increasing the productivity of numerical application developers

Supporting general data structures and execution models in runtime environments

Para aprovechar las plataformas paralelas, se necesitan herramientas de programación para poder representar apropiadamente los algoritmos paralelos. Además, los entornos paralelos requieren sistemas en tiempo de ejecución que ofrezcan diferentes paradigmas de computación. Existen diferentes áreas a estudiar con el fin de construir un sistema en tiempo de ejecución completo para un entorno paralelo. Esta Tesis aborda dos problemas comunes: el soporte unificado de datos densos y dispersos, y la integración de paralelismo orientado a mapeo de datos y paralelismo orientado a flujo de datos. Esta Tesis propone una solución que desacopla la representación, partición y reparto de datos, del algoritmo y de la estrategia de diseño paralelo para integrar manejo para datos densos y dispersos. Además, se presenta un nuevo modelo de programación basado en el paradigma de flujo de datos, donde diferentes actividades pueden ser arbitrariamente enlazadas para formar redes genéricas pero estructuradas que representan el cómputo globalDepartamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos