Interfaces for parallel numerical linear algebra libraries in high level languages

Drummond, L. A.; Galiano, Vicente; Migallón, Violeta; Penadés, José

doi:10.1016/j.advengsoft.2008.11.014

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, users must be aware of the appropriate local indexes to use in each process, which increase the complexity of developing parallel codes . In the literature, there exist some proposals that try to ease the use of message‐passing numerical libraries, such as PyScaLAPACK and Elemental . Following this trend, one of the goals of UPCBLAS is to increase programmability.…”

Section: Related Workmentioning

confidence: 99%

UPCBLAS: a library for parallel matrix computations in Unified Parallel C

González-Domínguez

Martín

Taboada

et al. 2012

Concurrency and Computation

View full text Add to dashboard Cite

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 such as multicore clusters. This paper describes UP-CBLAS, a parallel numerical library for dense matrix computations using the PGAS Unified Parallel C (UPC) language. The routines developed in UPCBLAS are built on top of sequential BLAS functions and exploit the particularities of the PGAS paradigm, taking into account data locality in order to achieve a good performance. Furthermore, the routines implement other optimization techniques, several of them by automatically taking into account the hardware characteristics of the underlying systems on which they are executed. The library has been experimentally evaluated on a multicore supercomputer and compared to a message-passing based parallel numerical library, demonstrating good scalability and efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

UPCBLAS: a library for parallel matrix computations in Unified Parallel C

González-Domínguez

Martín

Taboada

et al. 2012

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…An example is PyScaLAPACK [12], a Python interface to ScaLAPACK [40]. ScaLAPACK is a subset of the LAPACK linear algebra routines but adapted for cluster computing by using the MPI [44] or the PVM [44] parallel libraries.…”

Section: Related Workmentioning

confidence: 99%