2,972 research outputs found
Parallel memetic algorithms for independent job scheduling in computational grids
In this chapter we present parallel implementations of Memetic Algorithms (MAs) for the problem of scheduling independent jobs in computational grids. The problem of scheduling in computational grids is known for its high demanding computational time. In this work we exploit the intrinsic parallel nature of MAs as well as the fact that computational grids offer large amount of resources, a part of which could be used to compute the efficient allocation of jobs to grid resources.
The parallel models exploited in this work for MAs include both fine-grained and coarse-grained parallelization and their hybridization. The resulting schedulers have been tested through different grid scenarios generated by a grid simulator to match different possible configurations of computational grids in terms of size (number of jobs and resources) and computational characteristics of resources. All in all, the result of this work showed that Parallel MAs are very good alternatives in order to match different performance requirement on fast scheduling of jobs to grid resources.Peer ReviewedPostprint (author's final draft
Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems
We present a number of novel algorithms, based on mathematical optimization
formulations, in order to solve a homogeneous multiprocessor scheduling
problem, while minimizing the total energy consumption. In particular, for a
system with a discrete speed set, we propose solving a tractable linear
program. Our formulations are based on a fluid model and a global scheduling
scheme, i.e. tasks are allowed to migrate between processors. The new methods
are compared with three global energy/feasibility optimal workload allocation
formulations. Simulation results illustrate that our methods achieve both
feasibility and energy optimality and outperform existing methods for
constrained deadline tasksets. Specifically, the results provided by our
algorithm can achieve up to an 80% saving compared to an algorithm without a
frequency scaling scheme and up to 70% saving compared to a constant frequency
scaling scheme for some simulated tasksets. Another benefit is that our
algorithms can solve the scheduling problem in one step instead of using a
recursive scheme. Moreover, our formulations can solve a more general class of
scheduling problems, i.e. any periodic real-time taskset with arbitrary
deadline. Lastly, our algorithms can be applied to both online and offline
scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c);
definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations
were correct only for special case of p_i=d_
A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem
Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration
methods for solving to optimality combinatorial optimization problems. In this
paper, we investigate the use of GPU computing as a major complementary way to
speed up those methods. The focus is put on the bounding mechanism of B&B
algorithms, which is the most time consuming part of their exploration process.
We propose a parallel B&B algorithm based on a GPU-accelerated bounding model.
The proposed approach concentrate on optimizing data access management to
further improve the performance of the bounding mechanism which uses large and
intermediate data sets that do not completely fit in GPU memory. Extensive
experiments of the contribution have been carried out on well known FSP
benchmarks using an Nvidia Tesla C2050 GPU card. We compared the obtained
performances to a single and a multithreaded CPU-based execution. Accelerations
up to x100 are achieved for large problem instances
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
Lattice QCD Thermodynamics on the Grid
We describe how we have used simultaneously nodes of the
EGEE Grid, accumulating ca. 300 CPU-years in 2-3 months, to determine an
important property of Quantum Chromodynamics. We explain how Grid resources
were exploited efficiently and with ease, using user-level overlay based on
Ganga and DIANE tools above standard Grid software stack. Application-specific
scheduling and resource selection based on simple but powerful heuristics
allowed to improve efficiency of the processing to obtain desired scientific
results by a specified deadline. This is also a demonstration of combined use
of supercomputers, to calculate the initial state of the QCD system, and Grids,
to perform the subsequent massively distributed simulations. The QCD simulation
was performed on a lattice. Keeping the strange quark mass at
its physical value, we reduced the masses of the up and down quarks until,
under an increase of temperature, the system underwent a second-order phase
transition to a quark-gluon plasma. Then we measured the response of this
system to an increase in the quark density. We find that the transition is
smoothened rather than sharpened. If confirmed on a finer lattice, this finding
makes it unlikely for ongoing experimental searches to find a QCD critical
point at small chemical potential
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