44,535 research outputs found
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Exploration of task mappings plays a crucial role in achieving high
performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms.
The problem of optimally mapping a set of tasks onto a set of given
heterogeneous processors for maximal throughput has been known, in general, to
be NP-complete. The problem is further exacerbated when multiple applications
(i.e., bigger task sets) and the communication between tasks are also
considered. Previous research has shown that Genetic Algorithms (GA) typically
are a good choice to solve this problem when the solution space is relatively
small. However, when the size of the problem space increases, classic genetic
algorithms still suffer from the problem of long evolution times. To address
this problem, this paper proposes a novel bias-elitist genetic algorithm that
is guided by domain-specific heuristics to speed up the evolution process.
Experimental results reveal that our proposed algorithm is able to handle large
scale task mapping problems and produces high-quality mapping solutions in only
a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st
A hyper-heuristic for adaptive scheduling in computational grids
In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids. An efficient scheduling of jobs to grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.Peer ReviewedPostprint (author's final draft
A Domain Specific Approach to High Performance Heterogeneous Computing
Users of heterogeneous computing systems face two problems: firstly, in
understanding the trade-off relationships between the observable
characteristics of their applications, such as latency and quality of the
result, and secondly, how to exploit knowledge of these characteristics to
allocate work to distributed computing platforms efficiently. A domain specific
approach addresses both of these problems. By considering a subset of
operations or functions, models of the observable characteristics or domain
metrics may be formulated in advance, and populated at run-time for task
instances. These metric models can then be used to express the allocation of
work as a constrained integer program, which can be solved using heuristics,
machine learning or Mixed Integer Linear Programming (MILP) frameworks. These
claims are illustrated using the example domain of derivatives pricing in
computational finance, with the domain metrics of workload latency or makespan
and pricing accuracy. For a large, varied workload of 128 Black-Scholes and
Heston model-based option pricing tasks, running upon a diverse array of 16
Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both
the makespan and accuracy are generally within 10% of the run-time performance.
When these models are used as inputs to machine learning and MILP-based
workload allocation approaches, a latency improvement of up to 24 and 270 times
over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio
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
Run-time Spatial Mapping of Streaming Applications to Heterogeneous Multi-Processor Systems
In this paper, we define the problem of spatial mapping. We present reasons why performing spatial mappings at run-time is both necessary and desirable. We propose what is—to our knowledge—the first attempt at a formal description of spatial mappings for the embedded real-time streaming application domain. Thereby, we introduce criteria for a qualitative comparison of these spatial mappings. As an illustration of how our formalization relates to practice, we relate our own spatial mapping algorithm to the formal model
A fast, effective local search for scheduling independent jobs in heterogeneous computing environments
The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. Work with other NP-hard problems has shown that solutions found by heuristic algorithms can often be improved by applying local search procedures to the solution found. This paper describes a simple but effective local search procedure for scheduling independent jobs in HC environments which, when combined with fast construction heuristics, can find shorter schedules on benchmark problems than other solution techniques found in the literature, and in significantly less time
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