1,783 research outputs found
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
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
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
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