An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Vydyanathan, Nagavijayalakshmi; Krishnamoorthy, Sriram; Sabin, Gerald; Çatalyürek, Ümit V.; Kurç, Tahsin; Sadayappan, P.; Saltz, Joel

doi:10.1109/tpds.2008.219

Cited by 38 publications

(19 citation statements)

References 30 publications

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“…For the application that presents the scheduling problem via dependency graph, also known as directed acyclic graph (DAG), we developed scheduling techniques that optimize latency while taking throughput as a constraint [24] and vice versa [25]. We also investigated duplication--based scheduling [26], and scheduling of mixed--parallel applications [27]. In addition, we developed an extensive survey of pipelined workflow scheduling [28].…”

Section: Schedulingmentioning

confidence: 99%

SciDAC Institute: Combinatorial Scientific Computing and Petascale Simulations (CSCAPES) (Final Report)

Çatalyürek¹

2014

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Section: Schedulingmentioning

confidence: 99%

SciDAC Institute: Combinatorial Scientific Computing and Petascale Simulations (CSCAPES) (Final Report)

Çatalyürek¹

2014

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“…In other words, a task is assigned a single core. The techniques presented in [2]- [5] take into account data parallelism within tasks as well as task parallelism. Their methods take a task graph as input and perform task scheduling and mapping simultaneously, aiming at minimization of schedule length or maximization of pipeline throughput.…”

Section: Related Workmentioning

confidence: 99%

Static Mapping of Multiple Data-Parallel Applications on Embedded Many-Core SoCs

Kaida

Hara-Azumi

Hieda

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper studies the static mapping of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the manycore architecture. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experiments show the effectiveness of the proposed techniques.

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“…Vydyanathan et. al [17] proposed scheduling algorithms that determine what tasks should be run concurrently and how many processors should be allocated to each task. Zhang et.…”

Section: Related Workmentioning

confidence: 99%

DEMB: Cache-Aware Scheduling for Distributed Query Processing

Liu

Eom

Sussman

et al. 2013

Job Scheduling Strategies for Parallel Processing

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Abstract. Leveraging data in distributed caches for large scale query processing applications is becoming more important, given current trends toward building large scalable distributed systems by connecting multiple heterogeneous less powerful machines rather than purchasing expensive homogeneous and very powerful machines. As more servers are added to such clusters, more memory is available for caching data objects across the distributed machines. However the cached objects are dispersed and traditional query scheduling policies that take into account only load balancing do not effectively utilize the increased cache space. We propose a new multi-dimensional range query scheduling policy for distributed query processing frameworks, called DEMB, that employs a probability distribution estimation derived from recent queries. DEMB accounts for both load balancing and the availability of distributed cached objects to both improve the cache hit rate for queries and thereby decrease query turnaround time and throughput. We experimentally demonstrate that DEMB produces better query plans and lower query response times than other query scheduling policies.

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An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Cited by 38 publications

References 30 publications

SciDAC Institute: Combinatorial Scientific Computing and Petascale Simulations (CSCAPES) (Final Report)

SciDAC Institute: Combinatorial Scientific Computing and Petascale Simulations (CSCAPES) (Final Report)

Static Mapping of Multiple Data-Parallel Applications on Embedded Many-Core SoCs

DEMB: Cache-Aware Scheduling for Distributed Query Processing

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