Probabilistic Graphical Models on Multi-Core CPUs using Java 8

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on Computational Intelligence Software at IEEE Computational Intelligence Magazine journa

On the Design and Analysis of Parallel and Distributed Algorithms

Arrival of multicore systems has enforced a new scenario in computing, the parallel and distributed algorithms are fast replacing the older sequential algorithms, with many challenges of these techniques. The distributed algorithms provide distributed processing using distributed file systems and processing units, while network is modeled as minimum cost spanning tree. On the other hand, the parallel processing chooses different language platforms, data parallel vs. parallel programming, and GPUs. Processing units, memory elements and storage are connected through dynamic distributed networks in the form of spanning trees. The article presents foundational algorithms, analysis, and efficiency considerations.Comment: 9 page

An experimental validation of the PRO model for parallel and distributed computation

National audienceThe Parallel Resource-Optimal (PRO) computation model was introduced by Gebremedhin et al. [2002] as a framework for the design and analysis of efficient parallel algorithms. The key features of the PRO model that distinguish it from previous parallel computation models are the full integration of resource-optimality into the design process and the use of a {granularity function as a parameter for measuring quality. In this paper we present experimental results on parallel algorithms, designed using the PRO model, for two representative problems: list ranking and sorting. The algorithms are implemented using SSCRAP, our environment for developing coarse-grained algorithms. The experimental performance results observed agree well with analytical predictions using the PRO model. Moreover, by using different platforms to run our experiments, we have been able to provide an integrated view of the modeling of an underlying architecture and the design and implementation of scalable parallel algorithms

Concurrent extensions to the FORTRAN language for parallel programming of computational fluid dynamics algorithms

Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures

Solving the More Difficult Aspects of Electric Motor Thermal Analysis in Small and Medium Size Industrial Induction Motors

With the ever-increasing pressure on electric motor manufacturers to develop smaller and more efficient electric motors, there is a need for more thermal analysis in parallel with the traditional electromagnetic design. Attention to the thermal design can be rewarded by major improvements in the overall performance. Technical papers published to date highlight a number of thermal design issues that are difficult to analyze. This paper reviews some of these issues and gives advice on how to deal with them when developing algorithms for inclusion in design software

Index structures for distributed text databases

The Web has became an obiquitous resource for distributed computing making it relevant to investigate new ways of providing efficient access to services available at dedicated sites. Efficiency is an ever-increasing demand which can be only satisfied with the development of parallel algorithms which are efficient in practice. This tutorial paper focuses on the design, analysis and implementation of parallel algorithms and data structures for widely-used text database applications on the Web. In particular we describe parallel algorithms for inverted files and suffix arrays structures that are suitable for implementing search engines. Algorithmic design is effected on top of the BSP model of parallel computing. This model ensures portability across diverse parallel architectures ranging from clusters to super-computers.Facultad de Informátic

Index structures for distributed text databases

The Web has became an obiquitous resource for distributed computing making it relevant to investigate new ways of providing efficient access to services available at dedicated sites. Efficiency is an ever-increasing demand which can be only satisfied with the development of parallel algorithms which are efficient in practice. This tutorial paper focuses on the design, analysis and implementation of parallel algorithms and data structures for widely-used text database applications on the Web. In particular we describe parallel algorithms for inverted files and suffix arrays structures that are suitable for implementing search engines. Algorithmic design is effected on top of the BSP model of parallel computing. This model ensures portability across diverse parallel architectures ranging from clusters to super-computers.Facultad de Informátic

Fast Parallel Algorithms for Basic Problems

Parallel processing is one of the most active research areas these days. We are interested in one aspect of parallel processing, i.e. the design and analysis of parallel algorithms. Here, we focus on non-numerical parallel algorithms for basic combinatorial problems, such as data structures, selection, searching, merging and sorting. The purposes of studying these types of problems are to obtain basic building blocks which will be useful in solving complex problems, and to develop fundamental algorithmic techniques. In this thesis, we study the following problems: priority queues, multiple search and multiple selection, and reconstruction of a binary tree from its traversals. The research on priority queue was motivated by its various applications. The purpose of studying multiple search and multiple selection is to explore the relationships between four of the most fundamental problems in algorithm design, that is, selection, searching, merging and sorting; while our parallel solutions can be used as subroutines in algorithms for other problems. The research on the last problem, reconstruction of a binary tree from its traversals, was stimulated by a challenge proposed in a recent paper by Berkman et al. ( Highly Parallelizable Problems, STOC 89) to design doubly logarithmic time optimal parallel algorithms because a remarkably small number of such parallel algorithms exist

WMTrace : a lightweight memory allocation tracker and analysis framework

The diverging gap between processor and memory performance has been a well discussed aspect of computer architecture literature for some years. The use of multi-core processor designs has, however, brought new problems to the design of memory architectures - increased core density without matched improvement in memory capacity is reduc- ing the available memory per parallel process. Multiple cores accessing memory simultaneously degrades performance as a result of resource con- tention for memory channels and physical DIMMs. These issues combine to ensure that memory remains an on-going challenge in the design of parallel algorithms which scale. In this paper we present WMTrace, a lightweight tool to trace and analyse memory allocation events in parallel applications. This tool is able to dynamically link to pre-existing application binaries requiring no source code modification or recompilation. A post-execution analysis stage enables in-depth analysis of traces to be performed allowing memory allocations to be analysed by time, size or function. The second half of this paper features a case study in which we apply WMTrace to five parallel scientific applications and benchmarks, demonstrating its effectiveness at recording high-water mark memory consumption as well as memory use per-function over time. An in-depth analysis is provided for an unstructured mesh benchmark which reveals significant memory allocation imbalance across its participating processes