Publications - Rodrigo Canales

Peer Reviewed Conference Publication

LAMMPS' PPPM Long-Range Solver for the Second Generation Xeon Phi
William McDoniel, Markus Höhnerbach, Rodrigo Canales, Ahmed E. Ismail and Paolo Bientinesi
High Performance Computing. ISC 2017., Volume 10266, pp. 61-78, Springer, June 2017.
Lecture Notes in Computer Science.
```
@inproceedings{McDoniel2017:890,
    author    = "William McDoniel and Markus Höhnerbach and Rodrigo Canales and {Ahmed E.} Ismail and Paolo Bientinesi",
    title     = "LAMMPS' PPPM Long-Range Solver for the Second Generation Xeon Phi",
    booktitle = "High Performance Computing. ISC 2017.",
    year      = 2017,
    volume    = 10266,
    pages     = "61--78",
    address   = "Cham",
    month     = jun,
    publisher = "Springer",
    doi       = "https://doi.org/10.1007/978-3-319-58667-0_4",
    note      = "Lecture Notes in Computer Science",
    url       = "https://arxiv.org/pdf/1702.04250.pdf"
}
```
Molecular Dynamics is an important tool for computational biologists, chemists, and materials scientists, consuming a sizable amount of supercomputing resources. Many of the investigated systems contain charged particles, which can only be simulated accurately using a long-range solver, such as PPPM. We extend the popular LAMMPS molecular dynamics code with an implementation of PPPM particularly suitable for the second generation Intel Xeon Phi. Our main target is the optimization of computational kernels by means of vectorization, and we observe speedups in these kernels of up to 12x. These improvements carry over to LAMMPS users, with overall speedups ranging between 2-3x, without requiring users to retune input parameters. Furthermore, our optimizations make it easier for users to determine optimal input parameters for attaining top performance.
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Technical Report

Large Scale Parallel Computations in R through Elemental
Rodrigo Canales, Elmar Peise and Paolo Bientinesi
October 2016.
```
@techreport{Canales2016:718,
    author = "Rodrigo Canales and Elmar Peise and Paolo Bientinesi",
    title  = "Large Scale Parallel Computations in R through Elemental",
    year   = 2016,
    month  = oct,
    url    = "https://arxiv.org/pdf/1610.07310v1"
}
```
Even though in recent years the scale of statistical analysis problems has increased tremendously, many statistical software tools are still limited to single-node computations. However, statistical analyses are largely based on dense linear algebra operations, which have been deeply studied, optimized and parallelized in the high-performance-computing community. To make high-performance distributed computations available for statistical analysis, and thus enable large scale statistical computations, we introduce RElem, an open source package that integrates the distributed dense linear algebra library Elemental into R. While on the one hand, RElem provides direct wrappers of Elemental's routines, on the other hand, it overloads various operators and functions to provide an entirely native R experience for distributed computations. We showcase how simple it is to port existing R programs to Relem and demonstrate that Relem indeed allows to scale beyond the single-node limitation of R with the full performance of Elemental without any overhead.
abstractwebPDFbibtexhide