9,378 research outputs found
Distributed data mining in grid computing environments
The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper
Parallel and Distributed Performance of a Depth Estimation Algorithm
Expansion of dataset sizes and increasing complexity of processing algorithms have led to consideration of parallel and distributed implementations. The rationale for distributing the computational load may be to thin-provision computational resources, to accelerate data processing rate, or to efficiently reuse already available but otherwise idle computational resources. Whatever the rationale, an efficient solution of this type brings with it questions of data distribution, job partitioning, reliability, and robustness. This paper addresses the first two of these questions in the context of a local cluster-computing environment. Using the CHRT depth estimator, it considers active and passive data distribution and their effect on data throughput, focusing mainly on the compromises required to maintain minimal communications requirements between nodes. As metric, the algorithm considers the overall computation time for a given dataset (i.e., the time lag that a user would experience), and shows that although there are significant speedups to be had by relatively simple modifications to the algorithm, there are limitations to the parallelism that can be achieved efficiently, and a balance between inter-node parallelism (i.e., multiple nodes running in parallel) and intranode parallelism (i.e., multiple threads within one node) for most efficient utilization of available resources
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
Towards Loosely-Coupled Programming on Petascale Systems
We have extended the Falkon lightweight task execution framework to make
loosely coupled programming on petascale systems a practical and useful
programming model. This work studies and measures the performance factors
involved in applying this approach to enable the use of petascale systems by a
broader user community, and with greater ease. Our work enables the execution
of highly parallel computations composed of loosely coupled serial jobs with no
modifications to the respective applications. This approach allows a new-and
potentially far larger-class of applications to leverage petascale systems,
such as the IBM Blue Gene/P supercomputer. We present the challenges of I/O
performance encountered in making this model practical, and show results using
both microbenchmarks and real applications from two domains: economic energy
modeling and molecular dynamics. Our benchmarks show that we can scale up to
160K processor-cores with high efficiency, and can achieve sustained execution
rates of thousands of tasks per second.Comment: IEEE/ACM International Conference for High Performance Computing,
Networking, Storage and Analysis (SuperComputing/SC) 200
Scalable Task-Based Algorithm for Multiplication of Block-Rank-Sparse Matrices
A task-based formulation of Scalable Universal Matrix Multiplication
Algorithm (SUMMA), a popular algorithm for matrix multiplication (MM), is
applied to the multiplication of hierarchy-free, rank-structured matrices that
appear in the domain of quantum chemistry (QC). The novel features of our
formulation are: (1) concurrent scheduling of multiple SUMMA iterations, and
(2) fine-grained task-based composition. These features make it tolerant of the
load imbalance due to the irregular matrix structure and eliminate all
artifactual sources of global synchronization.Scalability of iterative
computation of square-root inverse of block-rank-sparse QC matrices is
demonstrated; for full-rank (dense) matrices the performance of our SUMMA
formulation usually exceeds that of the state-of-the-art dense MM
implementations (ScaLAPACK and Cyclops Tensor Framework).Comment: 8 pages, 6 figures, accepted to IA3 2015. arXiv admin note: text
overlap with arXiv:1504.0504
Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation
Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on
wired infrastructure especially with the advent of the multi-radio
multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer
viz., augmented network capacity, uninterrupted connectivity and reduced
latency, are depreciated by the detrimental effect of prevalent interference.
Interference mitigation is thus a prime objective in WMN deployments. It is
often accomplished through prudent channel allocation (CA) schemes which
minimize the adverse impact of interference and enhance the network
performance. However, a multitude of CA schemes have been proposed in research
literature and absence of a CA performance prediction metric, which could aid
in the selection of an efficient CA scheme for a given WMN, is often felt. In
this work, we offer a fresh characterization of the interference endemic in
wireless networks. We then propose a reliable CA performance prediction metric,
which employs a statistical interference estimation approach. We carry out a
rigorous quantitative assessment of the proposed metric by validating its CA
performance predictions with experimental results, recorded from extensive
simulations run on an ns-3 802.11g environment
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