1,250 research outputs found
Power efficient job scheduling by predicting the impact of processor manufacturing variability
Modern CPUs suffer from performance and power consumption variability due to the manufacturing process. As a result, systems that do not consider such variability caused by manufacturing issues lead to performance degradations and wasted power. In order to avoid such negative impact, users and system administrators must actively counteract any manufacturing variability.
In this work we show that parallel systems benefit from taking into account the consequences of manufacturing variability when making scheduling decisions at the job scheduler level. We also show that it is possible to predict the impact of this variability on specific applications by using variability-aware power prediction models. Based on these power models, we propose two job scheduling policies that consider the effects of manufacturing variability for each application and that ensure that power consumption stays under a system-wide power budget. We evaluate our policies under different power budgets and traffic scenarios, consisting of both single- and multi-node parallel applications, utilizing up to 4096 cores in total. We demonstrate that they decrease job turnaround time, compared to contemporary scheduling policies used on production clusters, up to 31% while saving up to 5.5% energy.Postprint (author's final draft
Power Bounded Computing on Current & Emerging HPC Systems
Power has become a critical constraint for the evolution of large scale High Performance Computing (HPC) systems and commercial data centers. This constraint spans almost every level of computing technologies, from IC chips all the way up to data centers due to physical, technical, and economic reasons. To cope with this reality, it is necessary to understand how available or permissible power impacts the design and performance of emergent computer systems. For this reason, we propose power bounded computing and corresponding technologies to optimize performance on HPC systems with limited power budgets.
We have multiple research objectives in this dissertation. They center on the understanding of the interaction between performance, power bounds, and a hierarchical power management strategy. First, we develop heuristics and application aware power allocation methods to improve application performance on a single node. Second, we develop algorithms to coordinate power across nodes and components based on application characteristic and power budget on a cluster. Third, we investigate performance interference induced by hardware and power contentions, and propose a contention aware job scheduling to maximize system throughput under given power budgets for node sharing system. Fourth, we extend to GPU-accelerated systems and workloads and develop an online dynamic performance & power approach to meet both performance requirement and power efficiency.
Power bounded computing improves performance scalability and power efficiency and decreases operation costs of HPC systems and data centers. This dissertation opens up several new ways for research in power bounded computing to address the power challenges in HPC systems. The proposed power and resource management techniques provide new directions and guidelines to green exscale computing and other computing systems
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
Libra: An Economy driven Job Scheduling System for Clusters
Clusters of computers have emerged as mainstream parallel and distributed
platforms for high-performance, high-throughput and high-availability
computing. To enable effective resource management on clusters, numerous
cluster managements systems and schedulers have been designed. However, their
focus has essentially been on maximizing CPU performance, but not on improving
the value of utility delivered to the user and quality of services. This paper
presents a new computational economy driven scheduling system called Libra,
which has been designed to support allocation of resources based on the users?
quality of service (QoS) requirements. It is intended to work as an add-on to
the existing queuing and resource management system. The first version has been
implemented as a plugin scheduler to the PBS (Portable Batch System) system.
The scheduler offers market-based economy driven service for managing batch
jobs on clusters by scheduling CPU time according to user utility as determined
by their budget and deadline rather than system performance considerations. The
Libra scheduler ensures that both these constraints are met within an O(n)
run-time. The Libra scheduler has been simulated using the GridSim toolkit to
carry out a detailed performance analysis. Results show that the deadline and
budget based proportional resource allocation strategy improves the utility of
the system and user satisfaction as compared to system-centric scheduling
strategies.Comment: 13 page
Sustainability in HPC: Vision and Opportunities
Tackling climate change by reducing and eventually eliminating carbon
emissions is a significant milestone on the path toward establishing an
environmentally sustainable society. As we transition into the exascale era,
marked by an increasing demand and scale of HPC resources, the HPC community
must embrace the challenge of reducing carbon emissions from designing and
operating modern HPC systems. In this position paper, we describe challenges
and highlight different opportunities that can aid HPC sites in reducing the
carbon footprint of modern HPC systems.Comment: Accepted at the ACM Sustainable Supercomputing Workshop in
conjunction with SC'2
From Quantity to Quality: Massive Molecular Dynamics Simulation of Nanostructures under Plastic Deformation in Desktop and Service Grid Distributed Computing Infrastructure
The distributed computing infrastructure (DCI) on the basis of BOINC and
EDGeS-bridge technologies for high-performance distributed computing is used
for porting the sequential molecular dynamics (MD) application to its parallel
version for DCI with Desktop Grids (DGs) and Service Grids (SGs). The actual
metrics of the working DG-SG DCI were measured, and the normal distribution of
host performances, and signs of log-normal distributions of other
characteristics (CPUs, RAM, and HDD per host) were found. The practical
feasibility and high efficiency of the MD simulations on the basis of DG-SG DCI
were demonstrated during the experiment with the massive MD simulations for the
large quantity of aluminum nanocrystals (-). Statistical
analysis (Kolmogorov-Smirnov test, moment analysis, and bootstrapping analysis)
of the defect density distribution over the ensemble of nanocrystals had shown
that change of plastic deformation mode is followed by the qualitative change
of defect density distribution type over ensemble of nanocrystals. Some
limitations (fluctuating performance, unpredictable availability of resources,
etc.) of the typical DG-SG DCI were outlined, and some advantages (high
efficiency, high speedup, and low cost) were demonstrated. Deploying on DG DCI
allows to get new scientific from the simulated
of numerous configurations by harnessing sufficient computational power to
undertake MD simulations in a wider range of physical parameters
(configurations) in a much shorter timeframe.Comment: 13 pages, 11 pages (http://journals.agh.edu.pl/csci/article/view/106
A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds
Cloud platforms have emerged as a prominent environment to execute high
performance computing (HPC) applications providing on-demand resources as well
as scalability. They usually offer different classes of Virtual Machines (VMs)
which ensure different guarantees in terms of availability and volatility,
provisioning the same resource through multiple pricing models. For instance,
in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs
are unused instances available for lower price. Despite the monetary
advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any
moment.
Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we
propose in this paper a static scheduling for HPC applications which are
composed by independent tasks (bag-of-task) with deadline constraints. However,
if a spot VM hibernates and it does not resume within a time which guarantees
the application's deadline, a temporal failure takes place. Our scheduling,
thus, aims at minimizing monetary costs of bag-of-tasks applications in EC2
cloud, respecting its deadline and avoiding temporal failures. To this end, our
algorithm statically creates two scheduling maps: (i) the first one contains,
for each task, its starting time and on which VM (i.e., an available spot or
on-demand VM with the current lowest price) the task should execute; (ii) the
second one contains, for each task allocated on a VM spot in the first map, its
starting time and on which on-demand VM it should be executed to meet the
application deadline in order to avoid temporal failures. The latter will be
used whenever the hibernation period of a spot VM exceeds a time limit.
Performance results from simulation with task execution traces, configuration
of Amazon EC2 VM classes, and VMs market history confirms the effectiveness of
our scheduling and that it tolerates temporal failures
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