34,945 research outputs found
About Parallel Programming: Paradigms, Parallel Execution and Collaborative Systems
In the last years, there were made efforts for delineation of a stabile and unitary frame, where the problems of logical parallel processing must find solutions at least at the level of imperative languages. The results obtained by now are not at the level of the made efforts. This paper wants to be a little contribution at these efforts. We propose an overview in parallel programming, parallel execution and collaborative systems.Parallel Programming, Parallel Execution, Collaborative systems, Collaborative parallel execution
On Parallel Join Processing in Object-Relational Database Systems
So far only few performance studies on parallel object-relational database
systems are available. In particular, the relative performance of relational
vs. reference-based join processing in a parallel environment has not been investigated sufficiently. We present a performance study based on the BUCKY benchmark to compare parallel join processing using reference attributes with
relational hash- and merge-join algorithms. In addition, we propose a data allocation
scheme especially suited for object hierarchies and set-valued attributes
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
From social networks to language modeling, the growing scale and importance
of graph data has driven the development of numerous new graph-parallel systems
(e.g., Pregel, GraphLab). By restricting the computation that can be expressed
and introducing new techniques to partition and distribute the graph, these
systems can efficiently execute iterative graph algorithms orders of magnitude
faster than more general data-parallel systems. However, the same restrictions
that enable the performance gains also make it difficult to express many of the
important stages in a typical graph-analytics pipeline: constructing the graph,
modifying its structure, or expressing computation that spans multiple graphs.
As a consequence, existing graph analytics pipelines compose graph-parallel and
data-parallel systems using external storage systems, leading to extensive data
movement and complicated programming model.
To address these challenges we introduce GraphX, a distributed graph
computation framework that unifies graph-parallel and data-parallel
computation. GraphX provides a small, core set of graph-parallel operators
expressive enough to implement the Pregel and PowerGraph abstractions, yet
simple enough to be cast in relational algebra. GraphX uses a collection of
query optimization techniques such as automatic join rewrites to efficiently
implement these graph-parallel operators. We evaluate GraphX on real-world
graphs and workloads and demonstrate that GraphX achieves comparable
performance as specialized graph computation systems, while outperforming them
in end-to-end graph pipelines. Moreover, GraphX achieves a balance between
expressiveness, performance, and ease of use
Fast Parallel Operations on Search Trees
Using (a,b)-trees as an example, we show how to perform a parallel split with
logarithmic latency and parallel join, bulk updates, intersection, union (or
merge), and (symmetric) set difference with logarithmic latency and with
information theoretically optimal work. We present both asymptotically optimal
solutions and simplified versions that perform well in practice - they are
several times faster than previous implementations
ACE: And/or-parallel copying-based execution of logic programs
In this paper we present a novel execution model for parallel implementation of logic programs which is capable
of exploiting both independent and-parallelism and or-parallelism in an efficient way. This model extends
the stack copying approach, which has been successfully
applied in the Muse system to implement or-parallelism, by integrating it with proven techniques used to support independent and-parallelism. We show how all solutions to non-deterministic andparallel goals are found without repetitions. This is done through recomputation as in Prolog (and in various and-parallel systems, like &-Prolog and DDAS), i.e., solutions of and-parallel goals are not shared. We propose a scheme for the efficient management of the address space in a way that is compatible with the apparently incompatible requirements of both and- and
or-parallelism. We also show how the full Prolog language,
with all its extra-logical features, can be supported in our and-or parallel system so that its sequential semantics is preserved. The resulting system retains the advantages of both purely or-parallel systems as well as purely and-parallel systems. The stack copying scheme together with our proposed memory management scheme can also be used to implement models that combine dependent and-parallelism and or-parallelism, such as Andorra and Prometheus
About Parallel Programming: Paradigms, Parallel Execution and Collaborative Systems
In the last years, there were made efforts for delineation of a stabile and unitary frame, where the problems of logical parallel processing must find solutions at least at the level of imperative languages. The results obtained by now are not at the level of the made efforts. This paper wants to be a little contribution at these efforts. We propose an overview in parallel programming, parallel execution and collaborative systems
Massively-Parallel Feature Selection for Big Data
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for
feature selection (FS) in Big Data settings (high dimensionality and/or sample
size). To tackle the challenges of Big Data FS PFBP partitions the data matrix
both in terms of rows (samples, training examples) as well as columns
(features). By employing the concepts of -values of conditional independence
tests and meta-analysis techniques PFBP manages to rely only on computations
local to a partition while minimizing communication costs. Then, it employs
powerful and safe (asymptotically sound) heuristics to make early, approximate
decisions, such as Early Dropping of features from consideration in subsequent
iterations, Early Stopping of consideration of features within the same
iteration, or Early Return of the winner in each iteration. PFBP provides
asymptotic guarantees of optimality for data distributions faithfully
representable by a causal network (Bayesian network or maximal ancestral
graph). Our empirical analysis confirms a super-linear speedup of the algorithm
with increasing sample size, linear scalability with respect to the number of
features and processing cores, while dominating other competitive algorithms in
its class
Parallel trajectory similarity joins in spatial networks
2018 Springer-Verlag GmbH Germany, part of Springer Nature The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider two cases of trajectory similarity joins (TS-Joins), including a threshold-based join (Tb-TS-Join) and a top-k TS-Join (k-TS-Join), where the objects are trajectories of vehicles moving in road networks. Given two sets of trajectories and a threshold (Formula presented.), the Tb-TS-Join returns all pairs of trajectories from the two sets with similarity above (Formula presented.). In contrast, the k-TS-Join does not take a threshold as a parameter, and it returns the top-k most similar trajectory pairs from the two sets. The TS-Joins target diverse applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide purposeful definitions of similarity. To enable efficient processing of the TS-Joins on large sets of trajectories, we develop search space pruning techniques and enable use of the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer search framework that lays the foundation for the algorithms for the Tb-TS-Join and the k-TS-Join that rely on different pruning techniques to achieve efficiency. For each trajectory, the algorithms first find similar trajectories. Then they merge the results to obtain the final result. The algorithms for the two joins exploit different upper and lower bounds on the spatiotemporal trajectory similarity and different heuristic scheduling strategies for search space pruning. Their per-trajectory searches are independent of each other and can be performed in parallel, and the mergings have constant cost. An empirical study with real data offers insight in the performance of the algorithms and demonstrates that they are capable of outperforming well-designed baseline algorithms by an order of magnitude
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