618 research outputs found
An efficient parallel method for mining frequent closed sequential patterns
Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential patterns. In this paper, we propose an efficient parallel approach called parallel dynamic bit vector frequent closed sequential patterns (pDBV-FCSP) using multi-core processor architecture for mining FCSPs from large databases. The pDBV-FCSP divides the search space to reduce the required storage space and performs closure checking of prefix sequences early to reduce execution time for mining frequent closed sequential patterns. This approach overcomes the problems of parallel mining such as overhead of communication, synchronization, and data replication. It also solves the load balance issues of the workload between the processors with a dynamic mechanism that re-distributes the work, when some processes are out of work to minimize the idle CPU time.Web of Science5174021739
Parallel Sort-Based Matching for Data Distribution Management on Shared-Memory Multiprocessors
In this paper we consider the problem of identifying intersections between
two sets of d-dimensional axis-parallel rectangles. This is a common problem
that arises in many agent-based simulation studies, and is of central
importance in the context of High Level Architecture (HLA), where it is at the
core of the Data Distribution Management (DDM) service. Several realizations of
the DDM service have been proposed; however, many of them are either
inefficient or inherently sequential. These are serious limitations since
multicore processors are now ubiquitous, and DDM algorithms -- being
CPU-intensive -- could benefit from additional computing power. We propose a
parallel version of the Sort-Based Matching algorithm for shared-memory
multiprocessors. Sort-Based Matching is one of the most efficient serial
algorithms for the DDM problem, but is quite difficult to parallelize due to
data dependencies. We describe the algorithm and compute its asymptotic running
time; we complete the analysis by assessing its performance and scalability
through extensive experiments on two commodity multicore systems based on a
dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.Comment: Proceedings of the 21-th ACM/IEEE International Symposium on
Distributed Simulation and Real Time Applications (DS-RT 2017). Best Paper
Award @DS-RT 201
Scalable analysis of multitemporal images using an array database
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesMassive archives of earth observation data are now available and the size of this data is increasing at a tremendous rate. This data is a very important resource and has a variety of applications including monitoring change, forestry application, agricultural application and urban planning. At the same time, they also possess challenge of storage, management, and high computational needs. In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to investigate the performance of SciDB based scalable solution to run arithmetic operation, simple time series analysis and complex time series analysis on multitemporal satellite imagery. This study provides better insight of SciDB architecture and provides suggestions for better performance in SciDB for remote sensing jobs. The research also compared the performance of time series analysis on SciDB array with file-based analysis using multicore parallelization (Using „Parallel‟ Package of R). It is found that SciDB provides a faster solution for time series analysis. However, SciDB might not be the best solution if the data size is smaller. Also, relative immaturity of SciDB and limited inherent support of remote sensing operations increases effort for the scientist to develop SciDB based solution. Nevertheless, SciDB has the potential to meet the ever increasing storage, management and computational need of big remote sensing data
Fast Density-Peaks Clustering: Multicore-based Parallelization Approach
Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. This paper addresses the problem of Density-Peaks Clustering (DPC), a recently proposed density-based clustering framework. Although DPC already has many applications, its straightforward implementation incurs a quadratic time computation to the number of points in a given dataset, thereby does not scale to large datasets. To enable DPC on large datasets, we propose efficient algorithms for DPC. Specifically, we propose an exact algorithm, Ex-DPC, and two approximation algorithms, Approx-DPC and S-Approx-DPC. Under a reasonable assumption about a DPC parameter, our algorithms are sub-quadratic, i.e., break the quadratic barrier. Besides, Approx-DPC does not require any additional parameters and can return the same cluster centers as those of Ex-DPC, rendering an accurate clustering result. S-Approx-DPC requires an approximation parameter but can speed up its computational efficiency. We further present that their efficiencies can be accelerated by leveraging multicore processing. We conduct extensive experiments using synthetic and real datasets, and our experimental results demonstrate that our algorithms are efficient, scalable, and accurate
Coz: Finding Code that Counts with Causal Profiling
Improving performance is a central concern for software developers. To locate
optimization opportunities, developers rely on software profilers. However,
these profilers only report where programs spent their time: optimizing that
code may have no impact on performance. Past profilers thus both waste
developer time and make it difficult for them to uncover significant
optimization opportunities.
This paper introduces causal profiling. Unlike past profiling approaches,
causal profiling indicates exactly where programmers should focus their
optimization efforts, and quantifies their potential impact. Causal profiling
works by running performance experiments during program execution. Each
experiment calculates the impact of any potential optimization by virtually
speeding up code: inserting pauses that slow down all other code running
concurrently. The key insight is that this slowdown has the same relative
effect as running that line faster, thus "virtually" speeding it up.
We present Coz, a causal profiler, which we evaluate on a range of
highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite.
Coz identifies previously unknown optimization opportunities that are both
significant and targeted. Guided by Coz, we improve the performance of
Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as
much as 68%; in most cases, these optimizations involve modifying under 10
lines of code.Comment: Published at SOSP 2015 (Best Paper Award
- …