4,755 research outputs found
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
OGSA first impressions: a case study re-engineering a scientific applicationwith the open grid services architecture
We present a case study of our experience re-engineeringa scientific application using the Open Grid Services Architecture(OGSA), a new specification for developing Gridapplications using web service technologies such as WSDLand SOAP. During the last decade, UCL?s Chemistry departmenthas developed a computational approach for predictingthe crystal structures of small molecules. However,each search involves running large iterations of computationallyexpensive calculations and currently takes a fewmonths to perform. Making use of early implementationsof the OGSA specification we have wrapped the Fortranbinaries into OGSI-compliant service interfaces to exposethe existing scientific application as a set of loosely coupledweb services. We show how the OGSA implementationfacilitates the distribution of such applications across alarge network, radically improving performance of the systemthrough parallel CPU capacity, coordinated resourcemanagement and automation of the computational process.We discuss the difficulties that we encountered turning Fortranexecutables into OGSA services and delivering a robust,scalable system. One unusual aspect of our approachis the way we transfer input and output data for the Fortrancodes. Instead of employing a file transfer service wetransform the XML encoded data in the SOAP message tonative file format, where possible using XSLT stylesheets.We also discuss a computational workflow service that enablesusers to distribute and manage parts of the computationalprocess across different clusters and administrativedomains. We examine how our experience re-engineeringthe polymorph prediction application led to this approachand to what extent our efforts have succeeded
Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications
Many scientific problems require multiple distinct computational tasks to be
executed in order to achieve a desired solution. We introduce the Ensemble
Toolkit (EnTK) to address the challenges of scale, diversity and reliability
they pose. We describe the design and implementation of EnTK, characterize its
performance and integrate it with two distinct exemplar use cases: seismic
inversion and adaptive analog ensembles. We perform nine experiments,
characterizing EnTK overheads, strong and weak scalability, and the performance
of two use case implementations, at scale and on production infrastructures. We
show how EnTK meets the following general requirements: (i) implementing
dedicated abstractions to support the description and execution of ensemble
applications; (ii) support for execution on heterogeneous computing
infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv)
fault tolerance. We discuss novel computational capabilities that EnTK enables
and the scientific advantages arising thereof. We propose EnTK as an important
addition to the suite of tools in support of production scientific computing
An Extensible Timing Infrastructure for Adaptive Large-scale Applications
Real-time access to accurate and reliable timing information is necessary to
profile scientific applications, and crucial as simulations become increasingly
complex, adaptive, and large-scale. The Cactus Framework provides flexible and
extensible capabilities for timing information through a well designed
infrastructure and timing API. Applications built with Cactus automatically
gain access to built-in timers, such as gettimeofday and getrusage,
system-specific hardware clocks, and high-level interfaces such as PAPI. We
describe the Cactus timer interface, its motivation, and its implementation. We
then demonstrate how this timing information can be used by an example
scientific application to profile itself, and to dynamically adapt itself to a
changing environment at run time
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI), 201
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
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