16,153 research outputs found
Analysis of Dynamic Memory Bandwidth Regulation in Multi-core Real-Time Systems
One of the primary sources of unpredictability in modern multi-core embedded
systems is contention over shared memory resources, such as caches,
interconnects, and DRAM. Despite significant achievements in the design and
analysis of multi-core systems, there is a need for a theoretical framework
that can be used to reason on the worst-case behavior of real-time workload
when both processors and memory resources are subject to scheduling decisions.
In this paper, we focus our attention on dynamic allocation of main memory
bandwidth. In particular, we study how to determine the worst-case response
time of tasks spanning through a sequence of time intervals, each with a
different bandwidth-to-core assignment. We show that the response time
computation can be reduced to a maximization problem over assignment of memory
requests to different time intervals, and we provide an efficient way to solve
such problem. As a case study, we then demonstrate how our proposed analysis
can be used to improve the schedulability of Integrated Modular Avionics
systems in the presence of memory-intensive workload.Comment: Accepted for publication in the IEEE Real-Time Systems Symposium
(RTSS) 2018 conferenc
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
Structure-Aware Dynamic Scheduler for Parallel Machine Learning
Training large machine learning (ML) models with many variables or parameters
can take a long time if one employs sequential procedures even with stochastic
updates. A natural solution is to turn to distributed computing on a cluster;
however, naive, unstructured parallelization of ML algorithms does not usually
lead to a proportional speedup and can even result in divergence, because
dependencies between model elements can attenuate the computational gains from
parallelization and compromise correctness of inference. Recent efforts toward
this issue have benefited from exploiting the static, a priori block structures
residing in ML algorithms. In this paper, we take this path further by
exploring the dynamic block structures and workloads therein present during ML
program execution, which offers new opportunities for improving convergence,
correctness, and load balancing in distributed ML. We propose and showcase a
general-purpose scheduler, STRADS, for coordinating distributed updates in ML
algorithms, which harnesses the aforementioned opportunities in a systematic
way. We provide theoretical guarantees for our scheduler, and demonstrate its
efficacy versus static block structures on Lasso and Matrix Factorization
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