6,475 research outputs found
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
Programming MPSoC platforms: Road works ahead
This paper summarizes a special session on multicore/multi-processor system-on-chip (MPSoC) programming challenges. The current trend towards MPSoC platforms in most computing domains does not only mean a radical change in computer architecture. Even more important from a SW developer´s viewpoint, at the same time the classical sequential von Neumann programming model needs to be overcome. Efficient utilization of the MPSoC HW resources demands for radically new models and corresponding SW development tools, capable of exploiting the available parallelism and guaranteeing bug-free parallel SW. While several standards are established in the high-performance computing domain (e.g. OpenMP), it is clear that more innovations are required for successful\ud
deployment of heterogeneous embedded MPSoC. On the other hand, at least for coming years, the freedom for disruptive programming technologies is limited by the huge amount of certified sequential code that demands for a more pragmatic, gradual tool and code replacement strategy
ImageJ2: ImageJ for the next generation of scientific image data
ImageJ is an image analysis program extensively used in the biological
sciences and beyond. Due to its ease of use, recordable macro language, and
extensible plug-in architecture, ImageJ enjoys contributions from
non-programmers, amateur programmers, and professional developers alike.
Enabling such a diversity of contributors has resulted in a large community
that spans the biological and physical sciences. However, a rapidly growing
user base, diverging plugin suites, and technical limitations have revealed a
clear need for a concerted software engineering effort to support emerging
imaging paradigms, to ensure the software's ability to handle the requirements
of modern science. Due to these new and emerging challenges in scientific
imaging, ImageJ is at a critical development crossroads.
We present ImageJ2, a total redesign of ImageJ offering a host of new
functionality. It separates concerns, fully decoupling the data model from the
user interface. It emphasizes integration with external applications to
maximize interoperability. Its robust new plugin framework allows everything
from image formats, to scripting languages, to visualization to be extended by
the community. The redesigned data model supports arbitrarily large,
N-dimensional datasets, which are increasingly common in modern image
acquisition. Despite the scope of these changes, backwards compatibility is
maintained such that this new functionality can be seamlessly integrated with
the classic ImageJ interface, allowing users and developers to migrate to these
new methods at their own pace. ImageJ2 provides a framework engineered for
flexibility, intended to support these requirements as well as accommodate
future needs
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