CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High-Energy Physics.
Ontology highlight
ABSTRACT:
SUBMITTER: Rovere M
PROVIDER: S-EPMC8080903 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The task-based approach is a parallelization paradigm in which an algorithm is transformed into a direct acyclic graph of tasks: the vertices are computational elements extracted from the original algorithm and the edges are dependencies between those. During the execution, the management of the dependencies adds an overhead that can become significant when the computational cost of the tasks is low. A possibility to reduce the makespan is to aggregate the tasks to make them heavier, while having fewer of them, with the objective of mitigating the importance of the overhead. In this paper, we study an existing clustering/partitioning strategy to speed up the parallel execution of a task-based application. We provide two additional heuristics to this algorithm and perform an in-depth study on a large graph set. In addition, we propose a new model to estimate the execution duration and use it to choose the proper granularity. We show that this strategy allows speeding up a real numerical application by a factor of 7 on a multi-core system.
| S-EPMC7924455 | biostudies-literature
Project description:BACKGROUNDS: Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. METHODS: Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. RESULT: A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.
| S-EPMC3976248 | biostudies-literature
Project description:Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein-protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL's scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ?70 million nodes with ?68 billion edges in ?2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.
| S-EPMC5888241 | biostudies-literature
Project description:It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification.
| S-EPMC4474961 | biostudies-literature
Project description:MotivationClustering algorithms play an important role in the analysis of biological networks, and can be used to uncover functional modules and obtain hints about cellular organization. While most available clustering algorithms work well on biological networks of moderate size, such as the yeast protein physical interaction network, they either fail or are too slow in practice for larger networks, such as functional networks for higher eukaryotes. Since an increasing number of larger biological networks are being determined, the limitations of current clustering approaches curtail the types of biological network analyses that can be performed.ResultsWe present a fast local network clustering algorithm SPICi. SPICi runs in time O(V log V+E) and space O(E), where V and E are the number of vertices and edges in the network, respectively. We evaluate SPICi's performance on several existing protein interaction networks of varying size, and compare SPICi to nine previous approaches for clustering biological networks. We show that SPICi is typically several orders of magnitude faster than previous approaches and is the only one that can successfully cluster all test networks within very short time. We demonstrate that SPICi has state-of-the-art performance with respect to the quality of the clusters it uncovers, as judged by its ability to recapitulate protein complexes and functional modules. Finally, we demonstrate the power of our fast network clustering algorithm by applying SPICi across hundreds of large context-specific human networks, and identifying modules specific for single conditions.AvailabilitySource code is available under the GNU Public License at http://compbio.cs.princeton.edu/spici.
| S-EPMC2853685 | biostudies-literature
Project description:MotivationBarcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.ResultsIn contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods.Availability and implementationBartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.Contactsasha.levy@stonybrook.edu or song.wu@stonybrook.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6049041 | biostudies-literature
Project description:Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.
| S-EPMC4090461 | biostudies-other
Project description:Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.
| S-EPMC5539863 | biostudies-other
Project description:The determination of the injection composition of cosmic ray nuclei within astrophysical sources requires sufficiently accurate descriptions of the source physics and the propagation - apart from controlling astrophysical uncertainties. We therefore study the implications of nuclear data and models for cosmic ray astrophysics, which involves the photo-disintegration of nuclei up to iron in astrophysical environments. We demonstrate that the impact of nuclear model uncertainties is potentially larger in environments with non-thermal radiation fields than in the cosmic microwave background. We also study the impact of nuclear models on the nuclear cascade in a gamma-ray burst radiation field, simulated at a level of complexity comparable to the most precise cosmic ray propagation code. We conclude with an isotope chart describing which information is in principle necessary to describe nuclear interactions in cosmic ray sources and propagation.
| S-EPMC5501817 | biostudies-literature
Project description:As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms.
| S-EPMC6657889 | biostudies-literature