61,843 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
BSLD threshold driven power management policy for HPC centers
In this paper, we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. A CPU frequency assignment algorithm is integrated into the well established EASY backfilling job scheduling policy. Running a job at lower frequency results in a reduction in power dissipation and accordingly in energy consumption. However, lower frequencies introduce a penalty in performance. Our frequency assignment algorithm has two adjustable parameters in order to enable fine grain energy-performance trade-off control. Furthermore, we have done an analysis of HPC system dimension. This paper investigates whether having more DVFS enabled processors for same load can lead to better energy efficiency and performance. Five workload traces from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%– 18% on average depending on allowed job performance penalty. Using the power-aware job scheduling for 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Peer ReviewedPostprint (published version
BSLD threshold driven parallel job scheduling for energy efficient HPC centers
Recently, power awareness in high performance computing (HPC) community has increased significantly. While CPU power reduction of HPC applications using Dynamic Voltage Frequency Scaling (DVFS) has been explored thoroughly, CPU power management for large scale parallel systems at system level has left unexplored. In this paper we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. Traditional parallel job schedulers determine when a job will be run, power aware ones should assign CPU frequency which it will be run at. We have introduced two adjustable thresholds in order to enable fine grain energy performance trade-off control. Since our power reduction approach is policy independent it can be added to any parallel job scheduling policy. Furthermore, we have done an analysis of HPC system dimension. Running an application at lower frequency on more processors can be more energy efficient than running it at the highest CPU frequency on less processors. This paper investigates whether having more DVFS enabled processors and same load can lead to better energy efficiency and performance. Five workload logs from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%- 18% on average depending on allowed job performance penalty. Applying the same frequency scaling algorithm on 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Postprint (published version
Energy Efficient Scheduling and Routing via Randomized Rounding
We propose a unifying framework based on configuration linear programs and
randomized rounding, for different energy optimization problems in the dynamic
speed-scaling setting. We apply our framework to various scheduling and routing
problems in heterogeneous computing and networking environments. We first
consider the energy minimization problem of scheduling a set of jobs on a set
of parallel speed scalable processors in a fully heterogeneous setting. For
both the preemptive-non-migratory and the preemptive-migratory variants, our
approach allows us to obtain solutions of almost the same quality as for the
homogeneous environment. By exploiting the result for the
preemptive-non-migratory variant, we are able to improve the best known
approximation ratio for the single processor non-preemptive problem.
Furthermore, we show that our approach allows to obtain a constant-factor
approximation algorithm for the power-aware preemptive job shop scheduling
problem. Finally, we consider the min-power routing problem where we are given
a network modeled by an undirected graph and a set of uniform demands that have
to be routed on integral routes from their sources to their destinations so
that the energy consumption is minimized. We improve the best known
approximation ratio for this problem.Comment: 27 page
Metascheduling of HPC Jobs in Day-Ahead Electricity Markets
High performance grid computing is a key enabler of large scale collaborative
computational science. With the promise of exascale computing, high performance
grid systems are expected to incur electricity bills that grow super-linearly
over time. In order to achieve cost effectiveness in these systems, it is
essential for the scheduling algorithms to exploit electricity price
variations, both in space and time, that are prevalent in the dynamic
electricity price markets. In this paper, we present a metascheduling algorithm
to optimize the placement of jobs in a compute grid which consumes electricity
from the day-ahead wholesale market. We formulate the scheduling problem as a
Minimum Cost Maximum Flow problem and leverage queue waiting time and
electricity price predictions to accurately estimate the cost of job execution
at a system. Using trace based simulation with real and synthetic workload
traces, and real electricity price data sets, we demonstrate our approach on
two currently operational grids, XSEDE and NorduGrid. Our experimental setup
collectively constitute more than 433K processors spread across 58 compute
systems in 17 geographically distributed locations. Experiments show that our
approach simultaneously optimizes the total electricity cost and the average
response time of the grid, without being unfair to users of the local batch
systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System
EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud
Cloud computing has become more popular in provision of computing resources
under virtual machine (VM) abstraction for high performance computing (HPC)
users to run their applications. A HPC cloud is such cloud computing
environment. One of challenges of energy efficient resource allocation for VMs
in HPC cloud is tradeoff between minimizing total energy consumption of
physical machines (PMs) and satisfying Quality of Service (e.g. performance).
On one hand, cloud providers want to maximize their profit by reducing the
power cost (e.g. using the smallest number of running PMs). On the other hand,
cloud customers (users) want highest performance for their applications. In
this paper, we focus on the scenario that scheduler does not know global
information about user jobs and user applications in the future. Users will
request shortterm resources at fixed start times and non interrupted durations.
We then propose a new allocation heuristic (named Energy-aware and Performance
per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to
choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS
per Watt). Using information from Feitelson's Parallel Workload Archive to
model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on
heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF
can reduce significant total energy consumption in comparison with state of the
art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced
Computing and Applications, Journal of Science and Technology, Vietnamese
Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201
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