1,934 research outputs found
FullSWOF_Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications
In this paper, we perform a comparison of two approaches for the
parallelization of an existing, free software, FullSWOF 2D (http://www.
univ-orleans.fr/mapmo/soft/FullSWOF/ that solves shallow water equations for
applications in hydrology) based on a domain decomposition strategy. The first
approach is based on the classical MPI library while the second approach uses
Parallel Algorithmic Skeletons and more precisely a library named SkelGIS
(Skeletons for Geographical Information Systems). The first results presented
in this article show that the two approaches are similar in terms of
performance and scalability. The two implementation strategies are however very
different and we discuss the advantages of each one.Comment: 27 page
Development of an oceanographic application in HPC
High Performance Computing (HPC) is used for running advanced application programs
efficiently, reliably, and quickly.
In earlier decades, performance analysis of HPC applications was evaluated based on
speed, scalability of threads, memory hierarchy. Now, it is essential to consider the
energy or the power consumed by the system while executing an application.
In fact, the High Power Consumption (HPC) is one of biggest problems for the High
Performance Computing (HPC) community and one of the major obstacles for exascale
systems design.
The new generations of HPC systems intend to achieve exaflop performances and will
demand even more energy to processing and cooling. Nowadays, the growth of HPC
systems is limited by energy issues
Recently, many research centers have focused the attention on doing an automatic tuning
of HPC applications which require a wide study of HPC applications in terms of power
efficiency.
In this context, this paper aims to propose the study of an oceanographic application,
named OceanVar, that implements Domain Decomposition based 4D Variational model
(DD-4DVar), one of the most commonly used HPC applications, going to evaluate not
only the classic aspects of performance but also aspects related to power efficiency in
different case of studies.
These work were realized at Bsc (Barcelona Supercomputing Center), Spain within the
Mont-Blanc project, performing the test first on HCA server with Intel technology and then on a mini-cluster Thunder with ARM technology.
In this work of thesis it was initially explained the concept of assimilation date, the
context in which it is developed, and a brief description of the mathematical model
4DVAR.
After this problem’s close examination, it was performed a porting from Matlab
description of the problem of data-assimilation to its sequential version in C language.
Secondly, after identifying the most onerous computational kernels in order of time, it
has been developed a parallel version of the application with a parallel multiprocessor
programming style, using the MPI (Message Passing Interface) protocol.
The experiments results, in terms of performance, have shown that, in the case of
running on HCA server, an Intel architecture, values of efficiency of the two most
onerous functions obtained, growing the number of process, are approximately equal to
80%.
In the case of running on ARM architecture, specifically on Thunder mini-cluster,
instead, the trend obtained is labeled as "SuperLinear Speedup" and, in our case, it can
be explained by a more efficient use of resources (cache memory access) compared with
the sequential case.
In the second part of this paper was presented an analysis of the some issues of this
application that has impact in the energy efficiency.
After a brief discussion about the energy consumption characteristics of the Thunder
chip in technological landscape, through the use of a power consumption detector, the
Yokogawa Power Meter, values of energy consumption of mini-cluster Thunder were
evaluated in order to determine an overview on the power-to-solution of this application
to use as the basic standard for successive analysis with other parallel styles.
Finally, a comprehensive performance evaluation, targeted to estimate the goodness of
MPI parallelization, is conducted using a suitable performance tool named Paraver,
developed by BSC.
Paraver is such a performance analysis and visualisation tool which can be used to
analyse MPI, threaded or mixed mode programmes and represents the key to perform a parallel profiling and to optimise the code for High Performance Computing.
A set of graphical representation of these statistics make it easy for a developer to
identify performance problems. Some of the problems that can be easily identified are
load imbalanced decompositions, excessive communication overheads and poor average
floating operations per second achieved.
Paraver can also report statistics based on hardware counters, which are provided by the
underlying hardware.
This project aimed to use Paraver configuration files to allow certain metrics to be
analysed for this application.
To explain in some way the performance trend obtained in the case of analysis on the
mini-cluster Thunder, the tracks were extracted from various case of studies and the
results achieved is what expected, that is a drastic drop of cache misses by the case ppn
(process per node) = 1 to case ppn = 16.
This in some way explains a more efficient use of cluster resources with an increase of
the number of processes
Parallel computing of numerical schemes and big data analytic for solving real life applications
This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance
Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale
The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian
method, used in numerical simulations of fluids in astrophysics and
computational fluid dynamics, among many other fields. SPH simulations with
detailed physics represent computationally-demanding calculations. The
parallelization of SPH codes is not trivial due to the absence of a structured
grid. Additionally, the performance of the SPH codes can be, in general,
adversely impacted by several factors, such as multiple time-stepping,
long-range interactions, and/or boundary conditions. This work presents
insights into the current performance and functionalities of three SPH codes:
SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an
interdisciplinary co-design project, SPH-EXA, for the development of an
Exascale-ready SPH mini-app. To gain such insights, a rotating square patch
test was implemented as a common test simulation for the three SPH codes and
analyzed on two modern HPC systems. Furthermore, to stress the differences with
the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an
additional test case, the Evrard collapse, has also been carried out. This work
extrapolates the common basic SPH features in the three codes for the purpose
of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app.
Moreover, the outcome of this serves as direct feedback to the parent codes, to
improve their performance and overall scalability.Comment: 18 pages, 4 figures, 5 tables, 2018 IEEE International Conference on
Cluster Computing proceedings for WRAp1
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)
Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application
SbPOM: A parallel implementation of Princenton Ocean Model
This paper presents the Stony Brook Parallel Ocean Model (sbPOM) for execution on workstations, Linux clusters and massively parallel supercomputers. The sbPOM is derived from the Princenton Ocean Model (POM), a widely used community ocean circulation model. Two-dimensional data decomposition of the horizontal domain is used with a halo of ghost cells to minimize communication between processors. Communication consists of the exchange of information between neighbor processors based on the Message Passing Interface (MPI) standard interface. The Parallel-NetCDF library is also implemented to achieve a high efficient input and output (I/O). Parallel performance is tested on an IBM Blue Gene/L massively parallel supercomputer, and efficiency using up to 2048 processors remains very good. © 2012 Elsevier Ltd.his research utilized resources at the New York Center for Computational Sciences at Stony Brook University/Brookhaven National Laboratory which was supported by the U.S. Department of Energy under Contract No. DE-AC02-98CH10886 and by the State of New York. A. Jordi's work was supported by a Ramón y Cajal grant from MICINNPeer Reviewe
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