DISTRIBUTED EVALUATION OF FUNCTIONAL BSP PROGRAMS

The BS λp-calculus is a calculus of functional bulk synchronous parallel (BSP) programs. It is the basis for the design of a bulk synchronous parallel ML language. For data-parallel languages, there are two points of view: the programming model where a program is seen as a sequence of operations on parallel vectors, and the execution model where the program is a parallel composition of programs run on each processor of the parallel machine. BSP algorithms are defined by data-parallel algorithms with explicit (physical) processes in order to allow their parallel execution time to be estimated. We present here a distributed evaluation minimally synchronous for BSP execution (which corresponds to the execution model). This distributed evaluation is correct w.r.t. the call-by-value strategy of the BS λp-calculus (which corresponds to the programming model).

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A HYBRID SHARED MEMORY EXECUTION MODEL FOR A DATA PARALLEL LANGUAGE WITH I/O

Parallel Processing Letters ◽

10.1142/s012962640800320x ◽

2008 ◽

Vol 18 (01) ◽

pp. 23-37 ◽

Cited By ~ 1

Author(s):

CLEMENS GRELCK ◽

STEFFEN KUTHE ◽

SVEN-BODO SCHOLZ

Keyword(s):

Program Analysis ◽

Flow Characteristics ◽

Parallel Execution ◽

Parallel Language ◽

Data Parallel ◽

Execution Model ◽

Execution Mode ◽

Parallel Code ◽

Execution Models ◽

Compilation Techniques

We propose a novel execution model for the implicitly parallel execution of data parallel programs in the presence of general I/O operations. This model is called hybrid because it combines the advantages of the standard execution models fork/join and SPMD. Based on program analysis the hybrid model adapts itself to one or the other on the granularity of individual instructions. We outline compilation techniques that systematically derive the organization of parallel code from data flow characteristics aiming at the reduction of execution mode switches in general and synchronization/communication requirements in particular. Experiments based on a prototype implementation show the effectiveness of the hybrid execution model for reducing parallel overhead.

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Data-Parallel Execution Model

Programming Massively Parallel Processors ◽

10.1016/b978-0-12-415992-1.00004-3 ◽

2013 ◽

pp. 63-94

Author(s):

David B. Kirk ◽

Wen-mei W. Hwu

Keyword(s):

Parallel Execution ◽

Data Parallel ◽

Execution Model

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A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation

Theory and Practice of Logic Programming ◽

10.1017/s1471068419000358 ◽

2019 ◽

Vol 19 (5-6) ◽

pp. 1056-1072 ◽

Cited By ~ 2

Author(s):

ARIYAM DAS ◽

CARLO ZANIOLO

Keyword(s):

Parallel Execution ◽

Distributed Model ◽

Correctness Proof ◽

Bulk Synchronous Parallel ◽

Data Parallel ◽

Data Mining Algorithms ◽

Recursive Query ◽

Recursive Computation ◽

Image Position ◽

Mining Algorithms

AbstractA large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved byPre-Mappability(${\cal P}$reM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs,$\[{\cal P}\]$reMcan also facilitate and improve their parallel execution. We prove that$\[{\cal P}\]$reM-optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore,$\[{\cal P}\]$reMcan be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with$\[{\cal P}\]$reMunder this relaxed synchronization model, we present experimental evidence of its benefits.

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New general mixed-integer linear programming model for mobile workforce management

Optimization and Engineering ◽

10.1007/s11081-021-09597-0 ◽

2021 ◽

Author(s):

András Éles ◽

István Heckl ◽

Heriberto Cabezas

Keyword(s):

Programming Model ◽

Time Windows ◽

Mutual Exclusion ◽

Parallel Execution ◽

Mixed Integer ◽

Management Problem ◽

Routing Problem ◽

Workforce Management ◽

Computational Performance ◽

Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

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