Dynamic Load Balancing Algorithm for Heterogeneous Multi-core Processors Cluster

Sharma, Rajnish; Kanungo, Priyesh

doi:10.1109/csnt.2014.63

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…asynchronous one-sided WS with distributed decen-work-commu-commu-PGAS MPI global load RTM memory tralized stealing nication nication RMA information Barros et al [24] Andreolli et al [26] Andreolli et al [27] Sena et al [28] x Hofmeyr et al [29] x Tchiboukdjian et al [30] x Imam and Sarkar [31] x x x Khaitan et al [32] x Tesser et al [33] x Tesser et al [34] x Tesser et al [35] x Padoin et al [36] x Padoin et al [37] x Sharma and Kanungo [38] x x x Zheng et al [39] x x x Martinez et al [40] x x x x Khaitan and Mccalley [41] x x x x Mor and Maillard [42] x x x x Li et al [43] x x x x x x Kumar et al [44] x x x x x x Dinan et al [21] x x x x x x Vishnu and Agarwal [49] x…”

Section: Discussionmentioning

confidence: 99%

“…To avoid losses of performance caused by a centralized load balancing element, some authors proposed decentralized load balancing strategies. Sharma and Kanungo [38] presented a technique to balance the computational load in heterogeneous multicore clusters, where no prior knowledge about the computational resources is required. Zheng et al [39] introduced a periodic load balancing strategy, where the balancing decisions are taken hierarchically in a tree fashion.…”

Section: Related Workmentioning

confidence: 99%

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Distributed-Memory Load Balancing With Cyclic Token-Based Work-Stealing Applied to Reverse Time Migration

et al. 2019

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Reverse time migration (RTM) is a prominent technique in seismic imaging. Its resulting subsurface images are used in the industry to investigate with higher confidence the existence and the conditions of oil and gas reservoirs. Because of its high computational cost, RTM must make use of parallel computers. Balancing the workload distribution of an RTM is a growing challenge in distributed computing systems. The competition for shared resources and the differently-sized tasks of the RTM are some of the possible sources of load imbalance. Although many load balancing techniques exist, scaling up for large problems and large systems remains a challenge because synchronization overhead also scales. This paper proposes a cyclic token-based work-stealing (CTWS) algorithm for distributed memory systems applied to RTM. The novel cyclic token approach reduces the number of failed steals, avoids communication overhead, and simplifies the victim selection and the termination strategy. The proposed method is implemented as a C library using the one-sided communication feature of the message passing interface (MPI) standard. Results obtained by applying the proposed technique to balance the workload of a 3D RTM system present a factor of 14.1 % speedup and reductions of the load imbalance of 78.4 % when compared to the conventional static distribution. 1The migration of seismic data is the process that attempts to build an image of the Earth's interior from recorded field data. Migration places these data into their actual geological position in the subsurface using numerical approximations of either wave-theoretical or ray-theoretical approaches to simulate the propagation of seismic waves [1].The wave-theoretical approach to the propagation of seismic waves employs the finite difference method (FDM) [2,3] to numerically solve the equation describing the movement of the waves [1,4]. This approach is prevalent among the geophysical community, due to its capacity of dealing with substantial velocity variations in complex geology (e.g., pre-salt).Reverse time migration (RTM) [5-9] implements this approach. It is one of the most known FDM-based migration methods. RTM is computationally intensive in terms of data storage and handling, and its use of high-complexity algorithms. Therefore, exploiting parallelism is mandatory for RTM implementations in 3D Earth models (3D RTM) [10].Parallel architectures can be classified as shared memory, when there is a single memory address space available to all processing units (e.g., nodes or cores), or distributed memory otherwise [11]. Many scientific and industrial computational resources are distributed memory systems composed of multiprocessor nodes with shared memory systems. A hybrid parallel application works at these two levels of parallelism. It can distribute the total workload among the nodes of a distributed memory system. Each node, then, distributes its subset of the workload among the processing units of its shared memory system. Parallel machines can also be described as het...

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed-Memory Load Balancing With Cyclic Token-Based Work-Stealing Applied to Reverse Time Migration

et al. 2019

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“…al [12] also proposed an optimal diffusion policy for heterogeneous network and they developed the convergence theory of the local Extrapolated Diffusion (EDF) method for heterogeneous torus networks. Sharma and Kanungo [11] presented a dynamic load balancing algorithm for heterogeneous multi-core processor cluster. Devine et al [10] provided a survey of dynamic load balancing technologies.…”

Section: Introductionmentioning

confidence: 99%

Parameter-invariant models for load balancing on heterogeneous networks

Chen

2015

Linear Algebra and its Applications

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Several schemes for homogeneous networks have been generalized for load balancing (LB) on heterogeneous networks. However, LB parameters must be recalculated with the change of weight distribution among network nodes in known schemes and this increases the computational complexity and weakens numerical stability on the base of original schemes in which LB parameters are only decided by communication topology. The known load balancing strategies are redesigned to contain a diffusion matrix M * with parameters independent of network heterogeneity. In the proposed strategies, LB parameters are only decided by network topology which is identical with the situation of homogeneous network. This leads to lower overhead of computing LB parameters and fewer LB steps than in the known schemes. Furthermore, the proposed schemes have little variation in the number of LB steps to distinct load distributions, so that they have an enhanced numerical stability. Theoretical and experimental results verify these improvements.

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“…The slower nature of some cores in a heterogeneous multicore system negatively affects the overall performance of the system. The cores in heterogeneous systems operate at different computing speeds, thus, even after equal distribution of the workload, the slowest core might end up being overloaded while, at the same time, the fastest core remains idle, doing nothing [7].…”

Section: Introductionmentioning

confidence: 99%

Affinity-Based Task Scheduling on Heterogeneous Multicore Systems Using CBS and QBICTM

et al. 2021

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This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.

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Dynamic Load Balancing Algorithm for Heterogeneous Multi-core Processors Cluster

Cited by 11 publications

References 6 publications

Distributed-Memory Load Balancing With Cyclic Token-Based Work-Stealing Applied to Reverse Time Migration

Distributed-Memory Load Balancing With Cyclic Token-Based Work-Stealing Applied to Reverse Time Migration

Parameter-invariant models for load balancing on heterogeneous networks

Affinity-Based Task Scheduling on Heterogeneous Multicore Systems Using CBS and QBICTM

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