Evaluation of UPC programmability using classroom studies

Teijeiro, Carlos; Taboada, Guillermo L.; Touriño, Juan; Fraguela, Basilio B.; Doallo, Ramón; Mallón, Damián A.; Gómez, A.; Mouriño, J. Carlos; Wibecan, Brian

doi:10.1145/1809961.1809975

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…In [27], the number of lines of code needed by the MPI and the UPC implementations of the NAS Parallel Benchmarks and other kernels are compared. Similar statistical studies with university students are presented in [28] and [29]. These works have demonstrated that the effort needed to solve the same problem is lower in UPC than that in MPI.…”

Section: Related Worksupporting

confidence: 61%

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

González-Domínguez

Martín

Taboada

et al. 2012

Concurrency and Computation

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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.

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Section: Related Worksupporting

confidence: 61%

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

González-Domínguez

Martín

Taboada

et al. 2012

Concurrency and Computation

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“…While the ideal approach for programmability analyses involves asking programmers with similar expertise to develop codes and comparing the results [28], this is seldom possible. Therefore, our study relies on objective metrics extracted from the source codes, which is a widely used alternative.…”

Section: Programmability Comparisonmentioning

confidence: 99%

A highly optimized skeleton for unbalanced and deep divide-and-conquer algorithms on multi-core clusters

2022

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Efficiently implementing the divide-and-conquer pattern of parallelism in distributed memory systems is very relevant, given its ubiquity, and difficult, given its recursive nature and the need to exchange tasks and data among the processors. This task is noticeably further complicated in the presence of multi-core systems, where hybrid parallelism must be exploited to attain the best performance, and when unbalanced and deep workloads are considered, as additional measures must be taken to load balance and avoid deep recursion problems. In this manuscript a parallel skeleton that fulfills all these requirements while providing high levels of usability is presented. In fact, the evaluation shows that our proposal is on average 415.32% faster than MPI codes and 229.18% faster than MPI + OpenMP benchmarks, while offering an average improvement in the programmability metrics of 131.04% over MPI alternatives and 155.18% over MPI + OpenMP solutions.

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“…The best approach to measure the programmability of different options is probably to rely on the observations and results from a group of programmers with a similar degree of expertise when trying to apply them [29]. This is seldom possible, thus, our study relies on three approximate metrics of this kind.…”

Section: Programmability Comparisonmentioning

confidence: 99%

A Parallel Skeleton for Divide-and-conquer Unbalanced and Deep Problems

Martínez

Fraguela

Dominguez

2021

Int J Parallel Prog

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The Divide-and-conquer (D&C) pattern appears in a large number of problems and is highly suitable to exploit parallelism. This has led to much research on its easy and efficient application both in shared and distributed memory parallel systems. One of the most successful approaches explored in this area consists of expressing this pattern by means of parallel skeletons which automate and hide the complexity of the parallelization from the user while trying to provide good performance. In this paper, we tackle the development of a skeleton oriented to the efficient parallel resolution of D&C problems with a high degree of imbalance among the subproblems generated and/or a deep level of recurrence. The skeleton achieves in our experiments average speedups between 11% and 18% higher than those of other solutions, reaching a maximum speedup of 78% in some tests. Nevertheless, the new proposal requires an average of between 13% and 29% less programming effort than the usual alternatives.

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Evaluation of UPC programmability using classroom studies

Cited by 10 publications

References 14 publications

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

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

A highly optimized skeleton for unbalanced and deep divide-and-conquer algorithms on multi-core clusters

A Parallel Skeleton for Divide-and-conquer Unbalanced and Deep Problems

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