3,440 research outputs found
HyperLoom possibilities for executing scientific workflows on the cloud
We have developed HyperLoom - a platform for defining and executing scientific workflows in large-scale HPC systems. The computational tasks in such workflows often have non-trivial dependency patterns, unknown execution time and unknown sizes of generated outputs. HyperLoom enables to efficiently execute the workflows respecting task requirements and cluster resources agnostically to the shape or size of the workflow. Although HPC infrastructures provide an unbeatable performance, they may be unavailable or too expensive especially for small to medium workloads. Moreover, for some workloads, due to HPCs not very flexible resource allocation policy, the system energy efficiency may not be optimal at some stages of the execution. In contrast, current public cloud providers such as Amazon, Google or Exoscale allow users a comfortable and elastic way of deploying, scaling and disposing a virtualized cluster of almost any size. In this paper, we describe HyperLoom virtualization and evaluate its performance in a virtualized environment using workflows of various shapes and sizes. Finally, we discuss the Hyperloom potential for its expansion to cloud environments.61140639
Virtualizing the Stampede2 Supercomputer with Applications to HPC in the Cloud
Methods developed at the Texas Advanced Computing Center (TACC) are described
and demonstrated for automating the construction of an elastic, virtual cluster
emulating the Stampede2 high performance computing (HPC) system. The cluster
can be built and/or scaled in a matter of minutes on the Jetstream self-service
cloud system and shares many properties of the original Stampede2, including:
i) common identity management, ii) access to the same file systems, iii)
equivalent software application stack and module system, iv) similar job
scheduling interface via Slurm.
We measure time-to-solution for a number of common scientific applications on
our virtual cluster against equivalent runs on Stampede2 and develop an
application profile where performance is similar or otherwise acceptable. For
such applications, the virtual cluster provides an effective form of "cloud
bursting" with the potential to significantly improve overall turnaround time,
particularly when Stampede2 is experiencing long queue wait times. In addition,
the virtual cluster can be used for test and debug without directly impacting
Stampede2. We conclude with a discussion of how science gateways can leverage
the TACC Jobs API web service to incorporate this cloud bursting technique
transparently to the end user.Comment: 6 pages, 0 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
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
Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster
The NEMO High Performance Computing Cluster at the University of Freiburg has
been made available to researchers of the ATLAS and CMS experiments. Users
access the cluster from external machines connected to the World-wide LHC
Computing Grid (WLCG). This paper describes how the full software environment
of the WLCG is provided in a virtual machine image. The interplay between the
schedulers for NEMO and for the external clusters is coordinated through the
ROCED service. A cloud computing infrastructure is deployed at NEMO to
orchestrate the simultaneous usage by bare metal and virtualized jobs. Through
the setup, resources are provided to users in a transparent, automatized, and
on-demand way. The performance of the virtualized environment has been
evaluated for particle physics applications
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