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Show all documents ScaLAPACK is slower because of the special distribution of the data used within it, distributed parallel processing thesis. In this application, we need to change the definition of the distribution of the data in order to reduce the import time. We can expect the same outcome on the K Computer if we run the ScaLAPACK application on it; the computation time would be excellent but the IO to import the data will slow down the distributed parallel processing thesis. As we are using ScaLAPACK, a YML task which make a call to the ScaLAPACK functions will not be efficient since YML load the data from disk and the distribution of the data in ScaLAPACK makes the IO difficult.
Finally, we compared the Gaussian elimination to the Gauss-Jordan elimination and we find out that the Gauss-Jordan elimination has execution time close to the Gaussian elimination although the Gauss-Jordan elimination has more operations. So the parallel task programming paradigm seems well suited to execute methods with high parallelism. Thus a programming paradigm using a graph of parallel tasks where each task can distributed parallel processing thesis be parallel may be interesting to create efficient parallel and distributed applications on distributed parallel processing thesis super-computers.
Contribution to the Emergence of New Intelligent Parallel and Distributed Methods Using a Multi-level Programming Paradigm for Extreme Computing The subject of my dissertation is related to this research context, and it focuses on the proposi- tion and analysis of a distributed and parallel programming paradigm for smart hybrid Krylov methods targeting at the exascale computing.
Since more than thirty years ago, different hy- brid Krylov methods were introduced to accelerate the convergence of iterative solvers by using the selected eigenvalues of systems, e. The research of this thesis relies on the Unite and Conquer approach proposed by Emad et al, distributed parallel processing thesis.
These different components can be deployed on different platforms such as P2P, cloud and the supercomputer systems, or different processors on the same platform. The ingredients of this dissertation on building a Unite and Conquer linear solver come from previous research, e. Unite and Con- quer approach is suitable to design a new programming paradigm for iterative methods with distributed and parallel computing on modern distributed parallel processing thesis. Static Analysis of Java for Distributed and Parallel Programming - Villers lès Nancy Cedex France Unité de recherche INRIA Rennes : IRISA, Campus universitaire de Beaulieu - Rennes Cedex France Unité de recherche INRIA Rhône-Alpes : 65[r].
Distributed futures for efficient data transfer between parallel processes Here, we choose actors as the task- parallel model, and BSP Bulk Synchronous Parallel [13] as the data- parallel programming frame- work.
The Actor [1] model is a task- parallel paradigm. Actors pre- vent data-races by enforcing that asynchronous message passing is the only interaction between processes: actors communicate with each other by putting messages in their mailboxes.
In this article we use distributed parallel processing thesis objects [4], which are objects that are at the same time actors. In active objects, a method call to an active object creates a message that reaches its mailbox and a future is used to represent the result returned by such an asynchronous method invocation.
Data- parallel programming abstractions like BSP are better suited to parallel computations on large amounts of data, distributed parallel processing thesis. BSP algorithms are defined as a sequence of supersteps, each made of three phases: computation, communication, distributed parallel processing thesis, and synchronization.
BSP is limited in terms of application elasticity or loose coupling of computing components as it relies on the strong synchronization of all computing entities. Contribution 1. parallel communications between components [ 1314 ], distributed parallel processing thesis, orchestration of services exposed by components for SOA Service Oriented Architecture [ 15 ].
Other works in the component community aim to dig into efficient ways to implement the component model in specific contexts leveraging well chosen supporting environment or middleware for deploying and running the components. These later approaches are bottom-up: first, authors select the middleware they want to rest upon, then they design a software component model in which they introduce features and related implementation in a way that efficiently uses the desirable features from the underlying middleware.
Some of those component models have been proposed for distributed application programming and execution, like for example: CCA and Assist component models on top of web service based grid distributed parallel processing thesis [ 1617 ], PaCO and GridCCM on top of Corba object request brokers [ 18 ], distributed parallel processing thesis, CCA on top of H2O [ 19 ]. The drawback of this approach is that distributed parallel processing thesis features of the underlying middleware impact too much the resulting component model, its implementation, and its runtime behaviour, leading to entangled, hard to maintain code.
We advocate another, more self-contained, approach. Given the features we believe are needed at the component model level, we carefully devise a component model implementation independently of the underlying middleware selected to effectively deploy and run the application. Instead, we underline the relation between the component model and its programming and execution model, i.
In other words, we make explicit the resulting runtime behaviour of the component model implementation without having to consider the effective supporting environment. To the best of our knowledge, the only examples of such kinds of discussion relating a component model with the programming model chosen to implement it are around: aspects [ 20 ], mixins [ 21 ], and direct enhancements of the core programming language as Java [ 22 ] to implement component constructions.
Consequently, we are among the first ones to. Pseudo-Random Streams for Distributed and Parallel Stochastic Simulations on GP-GPU compute their own data sets, including their own stochastic stream. Thus, our first requirement concerning random streams parallelization can be expressed as follows: each thread should dispose of its own random sequence.
As we explained previously, GP-GPU programming frameworks offer a thread scope rather than a processor one. The threads in use for GP-GPU propose an abstraction of the underlying architecture.
They are concurrently running on the same device and handle their own local memory area. Thread scheduling is at the basis of GPU performances. Memory accesses are the well-known bottleneck of this kind of device. Indeed, running a large amount of threads in turn allows GPUs to bypass memory latency. There should always be runnable threads while others are waiting for their input data. Disregarding the effective number of processors, we theoretically say that the more threads you have, distributed parallel processing thesis, the better your application will leverage the device.
Applications need to be written to use the maximum number of threads, but also to scale up transparently when the next GPU generation will be able to run twice as many threads as today. So, in accordance with Coddington who advocates that the generator should. Vascular system modeling in parallel environment - distributed and shared memory approaches.
The experiments were carried out on two available multi-core computers. The hardware specifications and results are summarized in Table I. It can be noticed that very good speedups are obtained.
However, we see that the second machine provides a slightly lower acceleration despite the fact that both computers consist of very similar hardware and work on the same operating systems. We speculate that this difference can be caused by using two different compilers. It is often emphasized that Intel compilers are especially tuned for its own hardware e. Xeon processorsi, distributed parallel processing thesis. that they include advanced optimization features and provide highly optimized performance libraries for creating multithreaded applications Types, compilers, and cryptography for secure distributed programming 1.
The system consists of any number of participants that do not trust one distributed parallel processing thesis, and that communicate over an untrusted network. In this first approach, the system is specified distributed parallel processing thesis the global pattern of communication between the participants, but not the local computation: the local program of each participant is left unspecified.
For example, in a protocol with three participants: a client, distributed parallel processing thesis, a bank, and a merchant, it may be specified that the merchant can send a specific message to the bank e. Corin et al. For example, it ensures that, if the client has not sent his agreement, a compromised merchant cannot convincingly forge a message that the implementation of the bank will accept. However, their compiler only accepts specifications that are sequential, that is, in which only one of participants is authorized to send a message at any given time.
We improve on their work by providing support for more elaborate parallel specifications and by generalizing the condition for compilation using a new type system, distributed parallel processing thesis. We detail this work in Chapter 2described below. A parallel tabu search for the unconstrained binary quadratic programming problem It is important to notice that parallel TS algorithms exist for other problem classes.
In fact, De Falco et al. Al-Yamani et al. Bort- feldt et al. Attanasio et al. Banos et al, distributed parallel processing thesis. Blazewicz et al. Le Bouthillier and Crainic [33] proposed a cooperative parallel metaheuristic for the vehicle routing problem with time windows, in which TS processes and EA processes are executed in parallel.
Talbi and Bachelet [34] proposed a parallel metaheuristic for the quadratic assignment problem, which uses TS as the main search agent. Maischberger [35] proposed distributed parallel processing thesis synchronous distributed parallel metaheuristic for the vehicle routing prob- lems, in which each process executes ILS extended with TS.
It is worth noticing that the parallel algorithm proposed in this paper is different from the aforementioned parallel algo- rithms, essentially because it uses a novel cooperation strategy andmore importantly, a finely-tuned UBQP-dedicated bit-flip perturbation operator. On the Optimization of Iterative Programming with Distributed Data Collections 5 RELATED WORKS Bigdata frameworks such as Spark and Flink offer an API with operations such as map and reduce that can be seen as a highly embedded domain specific language EDSL.
As explained in [3], this API approach as well as that of other EDSLs offers a big advantage over approaches like relational query languages and Datalog as they allow to express 1 more general purpose computations on 2 more complex data in their native format which can be arbitrarily nested. Also, functions such as map and reduce can have as argument any function f of the host language 13 called second order functions map f and reduce f thus exposing parallelism while allowing a seamless integration with the host language.
However, as pointed out by [3], distributed parallel processing thesis, this approach suffers from the difficulty of automatically optimizing programs. To enable automatic optimizations, they propose an algebra based on monads and monad comprehensions and propose an EDSL called Emma. Emma targets JVM-based parallel dataflow engines such as Spark and Flink.
However, in order to support recursive programs a large class of programsone needs to distributed parallel processing thesis loops to mimick fixpoints but optimizations are not available for such constructs.
Parallel itemset mining in massively distributed environments 40 4. Data Partitioning for Fast Mining of Frequent Itemset correlations of features.
Their discovery is known as Frequent itemset mining FIM for shortand presents an essential and fundamental role in many domains. In business and e-commerce, for instance, FIM techniques can be applied to recommend new items, such as books and different other products. In science and engineering, FIM can be used to analyze such different scientific parameters e.
Finally, FIM methods can help to perform other data mining tasks such as text mining [1], for instance, andas it will be better illustrated by our experiments in Section 4. However, the manipulation and processing of large-scale databases have opened up new challenges in data mining [68]. First, the data is no longer located in one computer, instead, it is distributed over several machines.
Thus, a parallel and efficient design of FIM algorithms must be taken into account. Second, parallel frequent itemset mining PFIM for short algorithms should scale with very large data and therefore very low MinSup threshold. Fortunately, with the availability of powerful programming models, such as MapReduce [2] or Spark [3], distributed parallel processing thesis parallelism of most FIM algorithms can be elegantly achieved. They have gained increasing popularity, as shown by the tremendous success of Hadoop [45], an open-source implementation.
Despite the robust parallelism setting that these solutions offer, PFIM algorithms remain holding major crucial challenges. With very low MinSup, and very large data, as will be illustrated by our experiments, most of standard PFIM algorithms do not scale. Hence, the problem of mining large-scale databases does not only depend on the parallelism design of FIM algorithms. In fact, PFIM algorithms have brought the same regular issues and challenges of their sequential implementations.
Sequential and Parallel Computing
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