Performance modeling of a geophysics application to accelerate over‐decomposition parameter tuning through simulation

Tesser, Rafael Keller; Schnorr, Lucas Mello; Legrand, Arnaud; Heinrich, Franz; Dupros, Fabrice; Navaux, Philippe O. A.

doi:10.1002/cpe.5012

Cited by 3 publications

(1 citation statement)

References 28 publications

(56 reference statements)

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“…Partial differential equations (PDEs) are crucial for the understanding of the behaviour of several phenomena [18] in engineering [14], geophysical exploration [24], and fluido-dynamics [38]. Since it is not always possible to compute the analytic solution of PDEs, numerical approaches discretise PDEs and generally require the solution to linear systems [33].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs

Galizia¹,

Cammarasana²,

Clematis³

et al. 2022

Preprint

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Partial Differential Equations (PDEs) describe several problems relevant to many fields of applied sciences, and their discrete counterparts typically involve the solution of sparse linear systems. In this context, we focus on the analysis of the computational aspects related to the solution of large and sparse linear systems with HPC solvers, by considering the performances of direct and iterative solvers in terms of computational efficiency, scalability, and numerical accuracy. Our aim is to identify the main criteria to support application-domain specialists in the selection of the most suitable solvers, according to the application requirements and available resources. To this end, we discuss how the numerical solver is affected by the regular/irregular discretisation of the input domain, the discretisation of the input PDE with piecewise linear or polynomial basis functions, which generally result in a higher/lower sparsity of the coefficient matrix, and the choice of different initial conditions, which are associated with linear systems with multiple right-hand side terms. Finally, our analysis is independent of the characteristics of the underlying computational architectures, and provides a methodological approach that can be applied to different classes of PDEs or with approximation problems.

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Section: Introductionmentioning

confidence: 99%

Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs

Galizia¹,

Cammarasana²,

Clematis³

et al. 2022

Preprint

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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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Using Visualization of Performance Data to Investigate Load Imbalance of a Geophysics Parallel Application

Solórzano

Nesi

Schnorr

2020

Practice and Experience in Advanced Research Computing

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Performance modeling of a geophysics application to accelerate over‐decomposition parameter tuning through simulation

Cited by 3 publications

References 28 publications

Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs

Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs

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

Using Visualization of Performance Data to Investigate Load Imbalance of a Geophysics Parallel Application

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