10,927 research outputs found
Actors vs Shared Memory: two models at work on Big Data application frameworks
This work aims at analyzing how two different concurrency models, namely the
shared memory model and the actor model, can influence the development of
applications that manage huge masses of data, distinctive of Big Data
applications. The paper compares the two models by analyzing a couple of
concrete projects based on the MapReduce and Bulk Synchronous Parallel
algorithmic schemes. Both projects are doubly implemented on two concrete
platforms: Akka Cluster and Managed X10. The result is both a conceptual
comparison of models in the Big Data Analytics scenario, and an experimental
analysis based on concrete executions on a cluster platform
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
Parallel Processing of Large Graphs
More and more large data collections are gathered worldwide in various IT
systems. Many of them possess the networked nature and need to be processed and
analysed as graph structures. Due to their size they require very often usage
of parallel paradigm for efficient computation. Three parallel techniques have
been compared in the paper: MapReduce, its map-side join extension and Bulk
Synchronous Parallel (BSP). They are implemented for two different graph
problems: calculation of single source shortest paths (SSSP) and collective
classification of graph nodes by means of relational influence propagation
(RIP). The methods and algorithms are applied to several network datasets
differing in size and structural profile, originating from three domains:
telecommunication, multimedia and microblog. The results revealed that
iterative graph processing with the BSP implementation always and
significantly, even up to 10 times outperforms MapReduce, especially for
algorithms with many iterations and sparse communication. Also MapReduce
extension based on map-side join usually noticeably presents better efficiency,
although not as much as BSP. Nevertheless, MapReduce still remains the good
alternative for enormous networks, whose data structures do not fit in local
memories.Comment: Preprint submitted to Future Generation Computer System
The role of concurrency in an evolutionary view of programming abstractions
In this paper we examine how concurrency has been embodied in mainstream
programming languages. In particular, we rely on the evolutionary talking
borrowed from biology to discuss major historical landmarks and crucial
concepts that shaped the development of programming languages. We examine the
general development process, occasionally deepening into some language, trying
to uncover evolutionary lineages related to specific programming traits. We
mainly focus on concurrency, discussing the different abstraction levels
involved in present-day concurrent programming and emphasizing the fact that
they correspond to different levels of explanation. We then comment on the role
of theoretical research on the quest for suitable programming abstractions,
recalling the importance of changing the working framework and the way of
looking every so often. This paper is not meant to be a survey of modern
mainstream programming languages: it would be very incomplete in that sense. It
aims instead at pointing out a number of remarks and connect them under an
evolutionary perspective, in order to grasp a unifying, but not simplistic,
view of the programming languages development process
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