Abstract state machines capture parallel algorithms

The process of merging two arbitrary partitions of a given finite set 𝒰 of n elements is known as coarsest refinement. In the COARSEST REFINEMENT PROBLEM we are given two arbitrary partitions 𝒳, 𝒴 of the set 𝒰 such that 𝒳 = {𝒳1, 𝒳2, ..., 𝒳 x} and 𝒴 = {𝒴1, 𝒴2, ..., 𝒴 y}, and determine a new partition 𝒵 = {𝒵1, 𝒵2, ..., 𝒵 z} such that each is a common non-empty subset of some 𝒳 a ∈ 𝒳 and some 𝒴 b ∈ 𝒴 and |𝒵| is as small as possible. This article describes a resource-efficient parallel algorithm to solve this problem. More specifically, we show that a coarsest refinement can be computed in O( t( n) + log n) parallel time using max{nlogn,p(n)} processors, where t( n) denotes the running time of a parallel stable sorting algorithm that uses p( n) processors on an EREW PRAM. This result depends on t( n) and p( n). We give a table that shows the best known time and processor complexities for a parallel stable sorting algorithm. If the parallel stable sorting algorithms by Ajtai et al., Cole, and Leighton are used, the coarsest refinement can be computed in O(log n) parallel time using n processors on an EREW PRAM. On the other hand, if the parallel stable sorting algorithm by Bahig et al. is used, the coarsest refinement can be computed in O(lognlog(nlogn)) parallel time using nlogn processors on an EREW PRAM. In addition, we show that on, a RAM machine, our parallel algorithm runs as asymptotically efficient as the fastest known sequential algorithm.

Sorting strings and constructing digital search trees in parallel

The authors propose a novel framework for designing efficient parallel algorithms on series parallel graphs. Recently, a novel approach for recognizing series parallel graphs was presented by D. Eppstein. Eppstein explored characterizations of the ear decomposition of series parallel graphs, which can be identified efficiently, in parallel. The authors extend Eppstein's results and show in a unified manner how to solve problems on series parallel graphs efficiently, in parallel, by finding a special ear decomposition of the graph. They demonstrate the utility of their novel framework by presenting O(log n) concurrent read exclusive write (CREW) parallel random access machine (PRAM) algorithms for the construction of a depth-first spanning tree, st-numbering, and a breadth-first spanning tree on series parallel graphs. >

Parallel recognition algorithms for chordal_planar graphs and planar [formula omitted]-trees

Algorithms for finding and updating minimum-depth spanning trees in parallel

Efficient parallel factorization and solution of structured and unstructured linear systems

In this paper, the problem of A-order of binary tree is studied with the PRAM(Parallel Random Access Machine) model of parallel computation and a parallel algorithm for A-order of binary tree is proposed. The process of the parallel algorithm is proceed with detailed description and verified analysis with an application instance. The parallel algorithm of A-order of binary tree provides using and reference for applying it to binary tree traverse sequence and solving the parallelism problem of application program.

We present a new programming language designed to allow the convenient expression of algorithms for a parallel random access machine (PRAM). The language attempts to satisfy two potentially conflicting goals: On the one hand, it should be simple and clear enough to serve as a vehicle for human-to-human communication of algorithmic ideas. On the other hand, it should be automatically translatable to efficient machine (i.e., PRAM) code, and it should allow precise statements to be made about the amount of resources (primarily time) consumed by a given program. In the sequential setting, both objectives are reasonably well met by the Algol-like languages, e.g., with the RAM as the underlying machine model, but we are not aware of any language that allows a satisfactory expression of typical PRAM algorithms. Our contribution should be seen as a modest attempt to fill this gap.

The authors provide optimal parallel solutions to several fundamental link distance problems set in trapezoided rectilinear polygons. All parallel algorithms are deterministic, run in logarithmic time, have an optimal time-processor product and are designed to run on EREW PRAM. The authors develop techniques (e.g. rectilinear window partition) for solving link distance problems in parallel which are expected to find applications in the design of other parallel computational geometry algorithms. They employ these parallel techniques for example to compute the link diameter, link center, and central diagonal of a rectilinear polygon. Their results also imply an optimal linear-time sequential algorithm for constructing a data structure to support rectilinear link distance queries between points.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A parallel algorithm to find k, 2/spl les/k/spl les/n/sup n-2/, spanning trees from a connected, weighted and undirected graph C(V, E, W) in the order of increasing weight is presented. It runs in O(T(n)+klogn) time with O(n/sup 2//log n) processors on a CREW PRAM, where n=|V|, m=|E| and T(n), O(log n)/spl les/T(n)/spl les/O(log/sup 2/ n), is the time of the fastest parallel algorithms to find a minimum spanning tree of G on a CREW PRAM with no more than O(n/sup 2//log n) processors. Since T(n)=O(log/sup 2/ n) for the time being, this result shows that to find k minimum spanning trees can be done in the same time bound as to find just one when k/spl les/O(log n) on a CREW PRAM.

While sliding window (LZ1) compression can be parallelized efficiently, the LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such negative result, there are parallel decoders which run in O(log/sup 2/ n) time with O(n/log n) processors on the PRAM EREW where n is the length of the output string, as for LZ1 decompression. We show a faster parallel decoding algorithm which runs on the PRAM EREW in O(log n) time with O(n) processors for text compressed by a standard implementation of the LZ2 algorithm (next character heuristic). We observe that LZ1 parallel decoders also can have such speed up. Moreover, we address a different implementation of LZ2 compression called identity heuristic. In this case, decoding on the PRAM EREW takes O(log n log log n) time with O(n/log n) processors with the realistic assumption that the length of the dictionary elements is logarithmic.

Two adaptive, level-order tree traversal algorithms are proposed for an exclusive-read and exclusive-write (EREW), parallel random access machine (PRAM) model of computation. Our breadth-first traversal algorithm for a general tree with n nodes achieves O((n/p)*log n /log(n/p)) time complexity using p processors on the EREW model, and hence it attains optimal speedup for p ≤ n1 − e, where 0 < e ≤ 1. This algorithm performs better (in terms of processor-time product) than an existing algorithm [12] which has O(k log n) time complexity using O(n1 + 1/k) processors on a concurrent-read and exclusive-write (CREW), PRAM model. The proposed breadth-depth algorithm for traversing a general tree requires O(n/p + log n) time on the EREW model, and thus it achieves optimal speedup for p ≤ n/log n. This algorithm provides a significant improvement over an existing parallel breadth-depth algorithm [4] which requires O(log n) time with O(n2) processors on CREW model. Our breadth-first traversal algorithm uses an Euler tour technique [20], and a list construction technique which is similar to the one used in [18] for solving the adjacency list construction problem for graphs. The breadth-depth traversal algorithm, on the other hand, is based on a special characterization which enables the reduction of this problem into a variety of list ranking problems.

We prove that every unambiguous context-free language can be recognized in O(log n) time on a parallel random access machine (P-RAM). This strengthens the result of Reif [2], who has proved that every deterministic context-free language can be recognized in O(log n) time on a P-RAM.

We consider the following force field computation problem: given a cluster of n particles in 3-dimensional space, compute the force exerted on each particle by the other particles. Depending on different applications, the pairwise interaction could be either gravitational or Lennard-Jones. In both cases, the force between two particles vanishes as the distance between them approaches to infinity. Since there are n(n−1)/2 pairs, direct method requires Θ(n 2) time for force-evaluation, which is very expensive for astronomical simulations. In 1985 and 1986, two famous Θ(n log n) time hierarchical tree algorithms were published by Appel [3] and by Barnes and Hut [4] respectively. In a recent paper, we presented a linear time algorithm which builds the oct tree bottom-up and showed that Appel’s algorithm can be implemented in Θ(n) sequential time. In this paper, we present an algorithm which computes the force field in Θ(log n) time using an lognn processor CREWPR AM. A key to this optimal parallel algorithm is replacing a recursive top-down force calculation procedure of Appel by an equivalent non-recursive bottom-up procedure. Our parallel algorithm also yields a new Θ(n) time sequential algorithm for force field computation.KeywordsParalle algorithmsspatial tree data structuresforce field evaluationN-body simulationsPRAMcost optimal algorithms

Dear SCPE Reader, We present a selection of papers which are extensions of papers presented at the 7-th International Symposium on Parallel and Distributed Computing, 1–5 July 2008, in Krakow, Poland. The motivation for publishing the selection in the SCPE Journal was, on the one hand, to present the flavour of the research reported at the conference and on the other hand to present some of the most relevant topics currently focused on the research on parallel and distributed computing in general. The selection contains only 6 papers out of about 60 presented at the conference, and thus, is far from covering all relevant topics represented at the ISPDC 2008. This is because not all of the invited authors were patient enough to accept a fairly long paper publishing process. Nevertheless, we hope that the presented papers will bring you closer to the research covered by the ISPDC conferences and will encourage you to participate in future ISPDC editions. The first paper ``The Impact of Workload Variability on Load Balancing Algorithms'' is by Marta Beltran and Antonio Guzman from King Juan Carlos University in Spain. It concerns an important topic of load balancing in cluster systems, namely adaptativity of the load balancing algorithms to changes of the workload in the system. Adequate accounting for additional load in the hosting system is of great relevance for correct optimization effects. The paper presents a thorough formal analysis of the workload variability metrics and their influence on the quality of load balancing algorithms. Four basic activities appearing in load balancing algorithms are identified, and based on them some algorithmic solutions are proposed to correctly deal with workload variability in system load balancing. The problem of dynamic load balancing algorithms robustness has been discussed. Two different robustness metrics sensitive to the applied type of opimization: local task-oriented or a global one enable selecting task remote execution or migration as load balancing operations. The proposed approach is illustrated with experiments. The second paper ``Model-Driven Engineering and Formal Validation of High-Performance Embedded Systems'' is by Abdoulaye Gamatie, Eric Rutten, Huafeng Yu, Pierre Boulet, Jean-Luc Dekeyser, from University of Lille and INRIA in France. The paper is concerned with a very advanced methodology of designing correct parallel embedded systems for intensive data-parallel computing. In their previous research, the authors of the paper designed the GASPARD embedded system design framework. It is based on the hardware/software co-design approach through model-driven engineering. The framework is based on an UML-like model specification language in which hardware and software elements are modelled using a component approach with special mechanisms for repetitive structures. This paper tries to combine the modelling framework of GASPARD with the mechanisms of synchronous languages to achieve design verifiability provided for such languages. The paper shows how GASPARD models can be translated into synchronous models based on data flow equations in order to formally check their correctness. The proposed approach is illustrated with an example of a video processing system. The third paper ``Relations Between Several Parallel Computational Models'' is by Stephan Bruda and Yuanqiao Zhang from Bishop’s University in Canada. The paper is concerned with theoretical aspects of shared memory systems described by the parallel random access machine PRAM model and aims in studying performance properties of different types of PRAM systems. The attention is focussed on analysing the computational power of two more sophisticated PRAM models (Combining CRCW and Broadcast Selective Reduction), which include data reduction in case of concurrent writes. The paper shows that these two models have equivalent computational power, which is a new result comparing the existing literature. The performance of both models applied to reconfigurable multiple bus machines was studied as a possible architectural solution for current VLSI processor implementations. It was shown that in such systems under reasonable assumptions concurrent-write does not enhance performance comparing the exclusive-write model. Another result important for the VLSI technology is that the Combining CRCW PRAM model (in which data of concurrent writes are arthmetically or logically combined before write) and the exclusive-write on directed reconfigurable busses perform in equivalent way under strong real-time requirements. The fourth paper ``Experiences with Mesh-Like Computations Using Prediction Binary Trees'' is by Gennaro Cordasco, Biagio Cosenza, Rosario de Chiara, Ugo Erra and Vittorio Scarano from the University ``degli Studi'' of Salerno and the University ``degli Studi della Biasilicata'' of Potenza in Italy. The paper concerns optimization methods for mesh-like computations in clusters of processors. The computations are perfomed assuming a phase-like program execution control using a tiling approach which reduces inter-processor communication. A temporal coherence is also assumed, which means that task sizes provide similar execution times in consecutive phases. Temporary coherent computations are structured in a Prediction Binary Tree, in which leaves represent computing tiles to be mapped to processors. A phase-by-phase semi-static load balancing is introduced to the scheduling algorithm. The scheduling algorithm is equipped with a predictor, which estimates the computation time of next phase tiles based on previous execution times and modifies the tiles to achieve balanced execution in phases. For this, two heuristics are used to leverage on data locality in processors. The proposed approach is illustrated by the example of interactive rendering with Parallel Ray Tracing algorithm. The fifth paper ``The Influence of the IBM pSeries Servers Virtualization Mechanism on Dynamic Resource Allocation in AIX 5L'' is by Maciej Mlynski from ASpartner Limited in Poland. The paper concerns a very up-to-date problem of system virtualization and presents the results of research carried on IBM pSeries servers. IBM is strongly developing the virtualization technique especially on IBM pSeries servers enabling an improved and flexible sharing of system resources between applications. The paper investigates novel facilities for dynamic resource management such as micro-partitioning and partition load manager. They enable dynamic creation of workload logical partitions of system resources and their dynamic mangement. It includes run-time resource re-alocation between logical partitions including setting of sharing specifications as well as run-time adding/removing/setting parameters of resources in the system. It remains an open question how to properly tune parameters of the operating system using the provided virtualization facilities to obtain the best efficiency for a given application program. The paper presents the results of experiments which study the effects of tuning the disk subsystem parameters under the IBM AIX 5L operating system with the use of the provided virtualization facilities on the resulting application execution performance. The results show that even small deterioration in the resource pool status requires an immediate adaptation of the operating system parameters to maintain the required performance. The sixth paper ``HeteroPBLAS: A Set of Parallel Basic Linear Algebra Subprograms Optimized for Heterogeneous Computational Clusters'' is by Ravi Reddy, Alexey Lastovetsky and Pedro Alonso from University College Dublin in Ireland and Polytechnic University of Valencia in Spain. The paper concerns the methodology for parallelization of linear algebra computations for execution in heterogeneous cluster environments. The design of the HeteroPBLAS library (Parallel Basic Linear Algebra Subprograms) for heterogeneous computational clusters is presented. The main contribution of the paper is the automation of the parallelization and optimization of the PBLAS, which is done by means of a special user interface and the underlying set of functions. An important element is here a performance model that is based on program code instrumentation, which determines parameters of the application and the executive heterogeneous platform relevant for execution performance of parallel code. The parameter values specified for or returned by execution of the performance model functions are next used for generation and optimal mapping of the parallel code of the library subroutines. The proposed approach is illustrated by experimental results of execution of optimized HeteroPBLAS programs on homogeneous and heterogeneous computing clusters. Marek Tudruj