3,443 research outputs found
Algorithms for Hierarchical and Semi-Partitioned Parallel Scheduling
We propose a model for scheduling jobs in a parallel machine setting that takes into account the cost of migrations by assuming that the processing time of a job may depend on the specific set of machines among which the job is migrated. For the makespan minimization objective, the model generalizes classical scheduling problems such as unrelated parallel machine scheduling, as well as novel ones such as semi-partitioned and clustered scheduling. In the case of a hierarchical family of machines, we derive a compact integer linear programming formulation of the problem and leverage its fractional relaxation to obtain a polynomial-time 2-approximation algorithm. Extensions that incorporate memory capacity constraints are also discussed
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
Minimisation of energy consumption variance for multi-process manufacturing lines through genetic algorithm manipulation of production schedule
Typical manufacturing scheduling algorithms do not consider the energy consumption of each job, or its variance, when they generate a production schedule. This can become problematic for manufacturers when local infrastructure has limited energy distribution capabilities. In this paper, a genetic algorithm based schedule modification algorithm is presented. By referencing energy consumption models for each job, adjustments are made to the original schedule so that it produces a minimal variance in the total energy consumption in a multi-process manufacturing production line, all while operating within the constraints of the manufacturing line and individual processes. Empirical results show a significant reduction in energy consumption variance can be achieved on schedules containing multiple concurrent jobs
Components and Interfaces of a Process Management System for Parallel Programs
Parallel jobs are different from sequential jobs and require a different type
of process management. We present here a process management system for parallel
programs such as those written using MPI. A primary goal of the system, which
we call MPD (for multipurpose daemon), is to be scalable. By this we mean that
startup of interactive parallel jobs comprising thousands of processes is
quick, that signals can be quickly delivered to processes, and that stdin,
stdout, and stderr are managed intuitively. Our primary target is parallel
machines made up of clusters of SMPs, but the system is also useful in more
tightly integrated environments. We describe how MPD enables much faster
startup and better runtime management of parallel jobs. We show how close
control of stdio can support the easy implementation of a number of convenient
system utilities, even a parallel debugger. We describe a simple but general
interface that can be used to separate any process manager from a parallel
library, which we use to keep MPD separate from MPICH.Comment: 12 pages, Workshop on Clusters and Computational Grids for Scientific
Computing, Sept. 24-27, 2000, Le Chateau de Faverges de la Tour, Franc
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