Abstract
We consider the Incremental Strong constraint 4D VARiational (IS4DVAR) algorithm for data assimilation implemented in ROMS with the aim to study its performance in terms of strong scaling scalability on computing architectures such as a cluster of CPUs. We consider realistic test cases with data collected in enclosed and semi enclosed seas, namely, Caspian sea, West Africa/Angola, as well as data collected into the California bay. The computing architecture we use is currently available at Imperial College London. The analysis allows us to highlight that the ROMS-IS4DVAR performance on emerging architectures depends on a deep relation among the problems size, the domain decomposition approach and the computing architecture characteristics.
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Notes
- 1.
Relation between \(t_{com}\) and \(t_{calc}\) (namely, the value of the parameter q) heavily depends on how the software under consideration is able to efficiently exploit the parallelism of such advanced architectures (the so called sustained performance).
- 2.
Helen is an SGI ICE 8200EX system. The first part of the system is comprised of 122 nodes. Each node has two 4-core 2.93 GHz Intel X5570 (Nehalem) processors and 24 GB of RAM. The processors are hyperthreaded - each physical core appears as two logical processors. The second part of the system consists of two extra ICE 8400EX racks with 179 extra nodes. These nodes have two 6-core 2.93 GHz X5670 (Westmere) processors and 24 GB of RAM. Like the Nehalem processors these are hyperthreaded. Then, the system has a total of 602 processors.
References
Antonelli, L., Carracciuolo, L., Ceccarelli, M., D’Amore, L., Murli, A.: Total variation regularization for edge preserving 3D SPECT imaging in high performance computing environments. In: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., Dongarra, J.J. (eds.) ICCS 2002. LNCS, vol. 2330, pp. 171–180. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46080-2_18
Arcucci, R., D’Amore, L., Celestino, S., Laccetti, G., Murli, A.: A scalable numerical algorithm for solving Tikhonov regularization problems. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds.) PPAM 2015. LNCS, vol. 9574, pp. 45–54. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32152-3_5
Arcucci, R., D’Amore, L., Carracciuolo, L., Murli, A.: A scalable variational data assimilation. J. Sci. Comput. 61, 239–257 (2014)
Arcucci, R., D’Amore, L., Marcellino, L., Murli, A.: Hpc computation issues of the incremental 3D variational data assimilation scheme in oceanvar software. J. Numer. Anal. Ind. Appl. Math. 7, 91–105 (2012)
Boccia, V., Carracciuolo, L., Laccetti, G., Lapegna, M., Mele, V.: HADAB: enabling fault tolerance in parallel applications running in distributed environments. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011. LNCS, vol. 7203, pp. 700–709. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31464-3_71
Carracciuolo, L., D’Amore, L., Murli, A.: Towards a parallel component for imaging in PETSc programming environment: a case study in 3-D echocardiography. Parallel Comput. 32, 67–83 (2006)
Caruso, P., Laccetti, G., Lapegna, M.: A performance contract system in a grid enabling, component based programming environment. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds.) EGC 2005. LNCS, vol. 3470, pp. 982–992. Springer, Heidelberg (2005). https://doi.org/10.1007/11508380_100
D’Amore, L., Campagna, R., Galletti, A., Marcellino, L., Murli, A.: A smoothing spline that approximates laplace transform functions only known on measurements on the real axis. Inverse Probl. 28, 025007 (2012)
D’Amore, L., Laccetti, G., Romano, D., Scotti, G., Murli, A.: Towards a parallel component in a GPU-CUDA environment: a case study with the L-BFGS harwell routine. Int. J. Comput. Math. 92, 59–76 (2015)
D’Amore, L., Marcellino, L., Mele, V., Romano, D.: Deconvolution of 3D fluorescence microscopy images using graphics processing units. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011. LNCS, vol. 7203, pp. 690–699. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31464-3_70
Gregoretti, F., Laccetti, G., Murli, A., Oliva, G., Scafuri, U.: MGF: a grid-enabled MPI library. Future Gener. Comput. Syst. (FGCS) 24, 158–165 (2008)
Guarracino, M.R., Laccetti, G., Murli, A.: Application oriented brokering in medical imaging: algorithms and software architecture. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds.) EGC 2005. LNCS, vol. 3470, pp. 972–981. Springer, Heidelberg (2005). https://doi.org/10.1007/11508380_99
Gurol, S., Weaver, A.T., Moore, A.M., Piacentini, M., Arango, H.G., Gratton, S.: B-preconditioned minimization algorithms for variational data assimilation with the dual formulation. Q. J. Roy. Metereol. Soc. 140, 539–556 (2014)
Laccetti, G., Lapegna, M.: PAMIHR. A parallel FORTRAN program for multidimensional quadrature on distributed memory architectures. In: Amestoy, P., Berger, P., Daydé, M., Ruiz, D., Duff, I., Frayssé, V., Giraud, L. (eds.) Euro-Par 1999. LNCS, vol. 1685, pp. 1144–1148. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48311-X_160
Maschhoff, A.J., Sorensen, D.: A portable implementation of ARPACK for distributed memory parallel architectures, vol. 91 (1996)
Montella, R., Giunta, G., Laccetti, G.: Virtualizing high-end GPGPUS on ARM clusters for the next generation of high performance cloud computing. Clust. Comput. 17, 139–152 (2014)
Montella, R., Giunta, G., Laccetti, G., Lapegna, M., Palmieri, C., Ferraro, C., Pelliccia, V.: Virtualizing CUDA enabled GPGPUs on ARM clusters. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds.) PPAM 2015. LNCS, vol. 9574, pp. 3–14. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32152-3_1
Moore, A.M., Arango, H.G., Broquet, G., Edwards, C., Veneziani, M., Powell, B., Foley, D., Doyle, J.D., Costa, D., Robinson, P.: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. Part II - performance and application to the California Current System. Prog. Oceanogr. 91(1), 50–73 (2011)
Moore, A.M., Arango, H.G., Broquet, G., Edwards, C., Veneziani, M., Powell, B., Foley, D., Doyle, J.D., Costa, D., Robinson, P.: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. Part III - observation impact and observation sensitivity in the California Current System. Prog. Oceanogr. 91(1), 74–94 (2011)
Moore, A.M., Arango, H.G., Broquet, G., Powell, B.S., Weaver, A.T., Zavala-Garay, J.: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. Part I - system overview and formulation. Prog. Oceanogr. 91(1), 34–49 (2011)
Murli, A., Boccia, V., Carracciuolo, L., D’Amore, L., Laccetti, G., Lapegna, M.: Monitoring and migration of a PETSc-based parallel application for medical imaging in a grid computing PSE. In: Gaffney, P.W., Pool, J.C.T. (eds.) Grid-Based Problem Solving Environments. ITIFIP, vol. 239, pp. 421–432. Springer, Boston, MA (2007). https://doi.org/10.1007/978-0-387-73659-4_25
Murli, A., Cuomo, S., D’Amore, L., Galletti, A.: Numerical regularization of a real inversion formula based on the Laplace transform’s eigenfunction expansion of the inverse function. Inverse Prob. 23(2), 713–731 (2007)
Murli, A., D’Amore, L., Laccetti, G., Gregoretti, F., Oliva, G.: A multi-grained distributed implementation of the parallel block conjugate gradient algorithm. Concurr. Comput.: Pract. Exp. 22, 2053–2072 (2010)
Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (1999). https://doi.org/10.1007/978-0-387-40065-5
Acknowledgment
The research has received funding from European Commission under H2020-MSCA-RISE NASDAC project (grant agreement n. 691184).
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D’Amore, L. et al. (2018). Performance Assessment of the Incremental Strong Constraints 4DVAR Algorithm in ROMS. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10778. Springer, Cham. https://doi.org/10.1007/978-3-319-78054-2_5
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