282,737 research outputs found
Hybrid performance modeling and prediction of large-scale computing systems
Performance is a key feature of large-scale computing systems. However, the achieved performance when a certain program is executed is significantly lower than the maximal theoretical performance of the large-scale computing system. The model-based performance evaluation may be used to support the performance-oriented program development for large-scale computing systems. In this paper we present a hybrid approach for performance modeling and prediction of parallel and distributed computing systems, which combines mathematical modeling and discrete-event simulation. We use mathematical modeling to develop parameterized performance models for components of the system. Thereafter, we use discrete-event simulation to describe the structure of system and the interaction among its components. As a result, we obtain a high-level performance model, which combines the evaluation speed of mathematical models with the structure awareness and fidelity of the simulation model. We evaluate empirically our approach with a real-world material science program that comprises more than 15,000 lines of codePeer ReviewedPostprint (published version
Methodology for modeling high performance distributed and parallel systems
Performance modeling of distributed and parallel systems is of considerable importance to the high performance computing community. To achieve high performance, proper task or process assignment and data or file allocation among processing sites is essential. This dissertation describes an elegant approach to model distributed and parallel systems, which combines the optimal static solutions for data allocation with dynamic policies for task assignment. A performance-efficient system model is developed using analytical tools and techniques.
The system model is accomplished in three steps. First, the basic client-server model which allows only data transfer is evaluated. A prediction and evaluation method is developed to examine the system behavior and estimate performance measures. The method is based on known product form queueing networks. The next step extends the model so that each site of the system behaves as both client and server. A data-allocation strategy is designed at this stage which optimally assigns the data to the processing sites. The strategy is based on flow deviation technique in queueing models. The third stage considers process-migration policies. A novel on-line adaptive load-balancing algorithm is proposed which dynamically migrates processes and transfers data among different sites to minimize the job execution cost. The gradient-descent rule is used to optimize the cost function, which expresses the cost of process execution at different processing sites.
The accuracy of the prediction method and the effectiveness of the analytical techniques is established by the simulations. The modeling procedure described here is general and applicable to any message-passing distributed and parallel system. The proposed techniques and tools can be easily utilized in other related areas such as networking and operating systems. This work contributes significantly towards the design of distributed and parallel systems where performance is critical
LUNES: Agent-based Simulation of P2P Systems (Extended Version)
We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which
allows to simulate complex networks composed of a high number of nodes. LUNES
is modular, since it splits the three phases of network topology creation,
protocol simulation and performance evaluation. This permits to easily
integrate external software tools into the main software architecture. The
simulation of the interaction protocols among network nodes is performed via a
simulation middleware that supports both the sequential and the
parallel/distributed simulation approaches. In the latter case, a specific
mechanism for the communication overhead-reduction is used; this guarantees
high levels of performance and scalability. To demonstrate the efficiency of
LUNES, we test the simulator with gossip protocols executed on top of networks
(representing peer-to-peer overlays), generated with different topologies.
Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation
of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011
International Conference on High Performance Computing and Simulation (HPCS
2011
PETRI NET BASED MODELING OF PARALLEL PROGRAMS EXECUTING ON DISTRIBUTED MEMORY MULTIPROCESSOR SYSTEMS
The development of parallel programs following the paradigm of communicating sequen-
tial processes to be executed on distributed memory multiprocessor systems is addressed.
The key issue in programming parallel machines today is to provide computerized tools
supporting the development of efficient parallel software, i.e. software effectively har-
nessing the power of parallel processing systems. The critical situations where a parallel
programmer needs help is in expressing a parallel algorithm in a programming language,
in getting a parallel program to work and in tuning it to get optimum performance (for
example speedup). .
We show that the Petri net formalism is higly suitable as a performance modeling
technique for asynchronous parallel systems, by introducing a model taking care of the
parallel program, parallel architecture and mapping influences on overall system perfor-
mance. PRM -net (Program-Resource- Mapping) models comprise a Petri net model of the
multiple flows of control in a parallel program, a Petri net model of the parallel hardware
and the process-to-processor mapping information into a single integrated performance
model. Automated analysis of PRM-net models addresses correctness and performance
of parallel programs mapped to parallel hardware. Questions upon the correctness of
parallel programs can be answered by investigating behavioural properties of Petri net
programs like liveness, reachability, boundedness, mutualy exclusiveness etc. Peformance
of parallel programs is usefully considered only in concern with a dedicated target hard-
ware. For this reason it is essential to integrate multiprocessor hardware characteristics
into the specification of a parallel program. The integration is done by assigning the
concurrent processes to physical processing devices and communication patterns among
parallel processes to communication media connecting processing elements yielding an in-
tegrated, Petri net based performance model. Evaluation of the integrated model applies
simulation and markovian analysis to derive expressions characterising the peformance of
the program being developed.
Synthesis and decomposition rules for hierarchical models naturally give raise to
use PRM-net models for graphical, performance oriented parallel programming, support-
ing top-down (stepwise refinement) as well as bottom-up development approaches. The
graphical representation of Petri net programs visualizes phenomena like parallelism, syn-
chronisation, communication, sequential and alternative execution. Modularity of pro-
gram blocks aids reusability, prototyping is promoted by automated code generation on
the basis of high level program specifications
Scalability in the Presence of Variability
Supercomputers are used to solve some of the world’s most computationally demanding
problems. Exascale systems, to be comprised of over one million cores and capable of 10^18
floating point operations per second, will probably exist by the early 2020s, and will provide
unprecedented computational power for parallel computing workloads. Unfortunately,
while these machines hold tremendous promise and opportunity for applications in High
Performance Computing (HPC), graph processing, and machine learning, it will be a major
challenge to fully realize their potential, because to do so requires balanced execution across
the entire system and its millions of processing elements. When different processors take different
amounts of time to perform the same amount of work, performance imbalance arises,
large portions of the system sit idle, and time and energy are wasted. Larger systems incorporate
more processors and thus greater opportunity for imbalance to arise, as well as larger
performance/energy penalties when it does. This phenomenon is referred to as performance
variability and is the focus of this dissertation.
In this dissertation, we explain how to design system software to mitigate variability
on large scale parallel machines. Our approaches span (1) the design, implementation, and
evaluation of a new high performance operating system to reduce some classes of performance
variability, (2) a new performance evaluation framework to holistically characterize
key features of variability on new and emerging architectures, and (3) a distributed modeling
framework that derives predictions of how and where imbalance is manifesting in order to
drive reactive operations such as load balancing and speed scaling. Collectively, these efforts
provide a holistic set of tools to promote scalability through the mitigation of variability
Distributed Hybrid Simulation of the Internet of Things and Smart Territories
This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)
In this tutorial paper, we will firstly review some basic simulation concepts
and then introduce the parallel and distributed simulation techniques in view
of some new challenges of today and tomorrow. More in particular, in the last
years there has been a wide diffusion of many cores architectures and we can
expect this trend to continue. On the other hand, the success of cloud
computing is strongly promoting the everything as a service paradigm. Is
parallel and distributed simulation ready for these new challenges? The current
approaches present many limitations in terms of usability and adaptivity: there
is a strong need for new evaluation metrics and for revising the currently
implemented mechanisms. In the last part of the paper, we propose a new
approach based on multi-agent systems for the simulation of complex systems. It
is possible to implement advanced techniques such as the migration of simulated
entities in order to build mechanisms that are both adaptive and very easy to
use. Adaptive mechanisms are able to significantly reduce the communication
cost in the parallel/distributed architectures, to implement load-balance
techniques and to cope with execution environments that are both variable and
dynamic. Finally, such mechanisms will be used to build simulations on top of
unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International
Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul
(Turkey), IEEE, July 2011. ISBN 978-1-61284-382-
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