161,258 research outputs found
Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks
Wireless content caching in small cell networks (SCNs) has recently been
considered as an efficient way to reduce the traffic and the energy consumption
of the backhaul in emerging heterogeneous cellular networks (HetNets). In this
paper, we consider a cluster-centric SCN with combined design of cooperative
caching and transmission policy. Small base stations (SBSs) are grouped into
disjoint clusters, in which in-cluster cache space is utilized as an entity. We
propose a combined caching scheme where part of the available cache space is
reserved for caching the most popular content in every SBS, while the remaining
is used for cooperatively caching different partitions of the less popular
content in different SBSs, as a means to increase local content diversity.
Depending on the availability and placement of the requested content,
coordinated multipoint (CoMP) technique with either joint transmission (JT) or
parallel transmission (PT) is used to deliver content to the served user. Using
Poisson point process (PPP) for the SBS location distribution and a hexagonal
grid model for the clusters, we provide analytical results on the successful
content delivery probability of both transmission schemes for a user located at
the cluster center. Our analysis shows an inherent tradeoff between
transmission diversity and content diversity in our combined
caching-transmission design. We also study optimal cache space assignment for
two objective functions: maximization of the cache service performance and the
energy efficiency. Simulation results show that the proposed scheme achieves
performance gain by leveraging cache-level and signal-level cooperation and
adapting to the network environment and user QoS requirements.Comment: 13 pages, 10 figures, submitted for possible journal publicatio
A fine-grain time-sharing Time Warp system
Although Parallel Discrete Event Simulation (PDES) platforms relying on the Time Warp (optimistic) synchronization
protocol already allow for exploiting parallelism, several techniques have been proposed to
further favor performance. Among them we can mention optimized approaches for state restore, as well as
techniques for load balancing or (dynamically) controlling the speculation degree, the latter being specifically
targeted at reducing the incidence of causality errors leading to waste of computation. However, in
state of the art Time Warp systems, events’ processing is not preemptable, which may prevent the possibility
to promptly react to the injection of higher priority (say lower timestamp) events. Delaying the processing
of these events may, in turn, give rise to higher incidence of incorrect speculation. In this article we present
the design and realization of a fine-grain time-sharing Time Warp system, to be run on multi-core Linux
machines, which makes systematic use of event preemption in order to dynamically reassign the CPU to
higher priority events/tasks. Our proposal is based on a truly dual mode execution, application vs platform,
which includes a timer-interrupt based support for bringing control back to platform mode for possible CPU
reassignment according to very fine grain periods. The latter facility is offered by an ad-hoc timer-interrupt
management module for Linux, which we release, together with the overall time-sharing support, within the
open source ROOT-Sim platform. An experimental assessment based on the classical PHOLD benchmark and
two real world models is presented, which shows how our proposal effectively leads to the reduction of the
incidence of causality errors, as compared to traditional Time Warp, especially when running with higher
degrees of parallelism
The DUNE-ALUGrid Module
In this paper we present the new DUNE-ALUGrid module. This module contains a
major overhaul of the sources from the ALUgrid library and the binding to the
DUNE software framework. The main changes include user defined load balancing,
parallel grid construction, and an redesign of the 2d grid which can now also
be used for parallel computations. In addition many improvements have been
introduced into the code to increase the parallel efficiency and to decrease
the memory footprint.
The original ALUGrid library is widely used within the DUNE community due to
its good parallel performance for problems requiring local adaptivity and
dynamic load balancing. Therefore, this new model will benefit a number of DUNE
users. In addition we have added features to increase the range of problems for
which the grid manager can be used, for example, introducing a 3d tetrahedral
grid using a parallel newest vertex bisection algorithm for conforming grid
refinement. In this paper we will discuss the new features, extensions to the
DUNE interface, and explain for various examples how the code is used in
parallel environments.Comment: 25 pages, 11 figure
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
MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data
In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
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