572 research outputs found
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
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
Spark deployment and performance evaluation on the MareNostrum supercomputer
In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications. As far as we know, this is the first attempt to investigate optimized deployment configurations of Spark on a petascale HPC setup. We detail the design of the framework and present some benchmark data to provide insights into the scalability of the system. We examine the impact of different configurations including parallelism, storage and networking alternatives, and we discuss several aspects in executing Big Data workloads on a computing system that is based on the compute-centric paradigm. Further, we derive conclusions aiming to pave the way towards systematic and optimized methodologies for fine-tuning data-intensive application on large clusters emphasizing on parallelism configurations.Peer ReviewedPostprint (author's final draft
Load-Varying LINPACK: A Benchmark for Evaluating Energy Efficiency in High-End Computing
For decades, performance has driven the high-end computing (HEC) community. However, as highlighted in recent exascale studies that chart a path from petascale to exascale computing, power consumption is fast becoming the major design constraint in HEC. Consequently, the HEC community needs to address this issue in future petascale and exascale computing systems.
Current scientific benchmarks, such as LINPACK and SPEChpc, only evaluate HEC systems when running at full throttle, i.e., 100% workload, resulting in a focus on performance and ignoring the issues of power and energy consumption. In contrast, efforts like SPECpower evaluate the energy efficiency of a compute server at varying workloads. This is analogous to evaluating the energy efficiency (i.e., fuel efficiency) of an automobile at varying speeds (e.g., miles per gallon highway versus city). SPECpower, however, only evaluates the energy efficiency of a single compute server rather than a HEC system; furthermore, it is based on SPEC's Java Business Benchmarks (SPECjbb) rather than a scientific benchmark. Given the absence of a load-varying scientific benchmark to evaluate the energy efficiency of HEC systems at different workloads, we propose the load-varying LINPACK (LV-LINPACK) benchmark. In this paper, we identify application parameters that affect performance and provide a methodology to vary the workload of LINPACK, thus enabling a more rigorous study of energy efficiency in supercomputers, or more generally, HEC
High performance computations using dynamical nucleation theory
Chemists continue to explore the use of very large computations to perform simulations that describe the molecular level physics of critical challenges in science. In this paper, we describe the Dynamical Nucleation Theory Monte Carlo (DNTMC) model - a model for determining molecular scale nucleation rate constants - and its parallel capabilities. The potential for bottlenecks and the challenges to running on future petascale or larger resources are delineated. A \u27master-slave\u27 solution is proposed to scale to the petascale and will be developed in the NWChem software. In addition, mathematical and data analysis challenges are described
Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme
This paper addresses the problem of efficiently storing and accessing massive
data blocks in a large-scale distributed environment, while providing efficient
fine-grain access to data subsets. This issue is crucial in the context of
applications in the field of databases, data mining and multimedia. We propose
a data sharing service based on distributed, RAM-based storage of data, while
leveraging a DHT-based, natively parallel metadata management scheme. As
opposed to the most commonly used grid storage infrastructures that provide
mechanisms for explicit data localization and transfer, we provide a
transparent access model, where data are accessed through global identifiers.
Our proposal has been validated through a prototype implementation whose
preliminary evaluation provides promising results
The Bases of Association Rules of High Confidence
We develop a new approach for distributed computing of the association rules
of high confidence in a binary table. It is derived from the D-basis algorithm
in K. Adaricheva and J.B. Nation (TCS 2017), which is performed on multiple
sub-tables of a table given by removing several rows at a time. The set of
rules is then aggregated using the same approach as the D-basis is retrieved
from a larger set of implications. This allows to obtain a basis of association
rules of high confidence, which can be used for ranking all attributes of the
table with respect to a given fixed attribute using the relevance parameter
introduced in K. Adaricheva et al. (Proceedings of ICFCA-2015). This paper
focuses on the technical implementation of the new algorithm. Some testing
results are performed on transaction data and medical data.Comment: Presented at DTMN, Sydney, Australia, July 28, 201
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