Towards Type-Based Optimizations in Distributed Applications Using ABS and JAVA 8

Serbanescu, Vlad; Nagarajagowda, C.; Azadbakht, Keyvan; Boer, Frank S. de; Nobakht, Behrooz

doi:10.1007/978-3-319-13464-2_8

Cited by 8 publications

(6 citation statements)

References 15 publications

(16 reference statements)

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“…• An editor with syntax highlighting and integrated build system, including compiler error location • An ABS simulator/interpreter with interactive debugger • Visualization of ABS model execution as a sequence diagram • Code generator backends for Erlang, Haskell, ProActive [54] (see also Section 3.4), Java 8 [83].…”

Section: :12mentioning

confidence: 99%

A Survey of Active Object Languages

et al. 2017

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International audienceTo program parallel systems efficiently and easily, a wide range of programming models have been proposed, each with different choices concerning synchronization and communication between parallel entities. Among them, the actor model is based on loosely coupled parallel entities that communicate by means of asynchronous messages and mailboxes. Some actor languages provide a strong integration with object-oriented concepts; these are often called active object languages. This paper reviews four major actor and active object languages and compares them according to carefully chosen dimensions that cover central aspects of the programming paradigms and their implementation

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Section: :12mentioning

confidence: 99%

A Survey of Active Object Languages

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“…The ABS-API library [9][10][11] was introduced as a solution to translate ABS code into production code initially for parallel applications. Java 8 new features allow wrapping of method calls into lightweight lambda expressions such that they can be put into a scheduling queue of an Executor Service to which the running objects are mapped, significantly reducing the number of idle Threads at runtime.…”

Section: Background and Related Workmentioning

confidence: 99%

A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs

Serbanescu

Azadbakht

Boer

2016

Computer Communications and Networks

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Distributed systems and applications require large amounts of resources in terms of memory and computing power and are becoming a standard for large businesses and enterprises [12] within and outside the domain of Computer Science. A very important topic for distributed applications is Big Data management and more specifically the generation of large-scale social networks graphs where the number of nodes reaches very large numbers. Analysis of such networks is of importance in many areas, e.g., data mining, network sciences, physics, and social sciences [3]. The need for efficient and scalable methods of network generation is frequently mentioned in the literature [8], particularly for the preferential attachment process [1,13,14]. Barabasi-Albert model, which is based on preferential attachment (PA) [4], is one of the most commonly used models to produce artificial networks, because of its explanatory power, conceptual simplicity, and interesting mathematical properties [13]. Nevertheless the large number of nodes in such graphs may not fit in the memory on one machine. The need for efficient solutions which provide scalability also requires more computational resources as well as implementation considerations. As such, distribution and synchronization are two main challenges. In this chapter, we investigate as a case study a distributed solution for PA-based graph generation which avoids low level synchronization management, thanks to the notion of cooperative scheduling and futures.

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“…not prime) the multiples of each prime, starting with the multiples of 2 [6]. To model the algorithm in two different versions using actors [5] and PDPU, we use the well-known parallel algorithm which partitions the sequence of candidate numbers [2,1]. In this algorithm, the numbers are partitioned into smaller sequences of numbers with almost the same size.…”

Section: Example 2: the Concurrent Version Of Sieve Of Eratosthenesmentioning

confidence: 99%

High Performance Computing Applications Using Parallel Data Processing Units

Azadbakht

Serbanescu

Boer

2015

Fundamentals of Software Engineering

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Abstract. Multicore processors are growing with respect to the number of cores on a chip. In a parallel computation context, multicore platforms have several important features such as exploiting multiple parallel processes, having access to a shared memory with noticeably lower cost than the distributed alternative and optimizing different levels of parallelism. In this paper, we introduce the Parallel Data Processing Unit (PDPU) which is a group of objects that benefits from the shared memory of the multicore configuration and that consists of two parts: a shared memory for maintaining data consistent, and a set of objects that are processing the data, then producing and aggregating the results concurrently. We then implement two examples in Java that illustrate PDPU behavior, and compare them with their actor based counterparts and show significant performance improvements. We also put forward the idea of integrating PDPU with the actor model which will result in an optimization for a specific spectrum of problems in actor based development.

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Towards Type-Based Optimizations in Distributed Applications Using ABS and JAVA 8

Cited by 8 publications

References 15 publications

A Survey of Active Object Languages

A Survey of Active Object Languages

A Java-Based Distributed Approach for Generating Large-Scale Social Network Graphs

High Performance Computing Applications Using Parallel Data Processing Units

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