An Approach to Optimise the Execution of RTM Algorithm in Multicore Machines

Sena, Alexandre C.; Nascimento, Aline P.; Boeres, Cristina; Rebello, Vinod E. F.; Bulcão, André

doi:10.1109/escience.2011.62

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…The cache-oblivious methodology (Frigo and Strumpen, 2005) has been an essential step in enhancing user-productivity for stencil implementations on modern systems. This approach has been applied with success in the context of industrial seismic applications (Etienne et al, 2014;Imbert et al, 2011;Sena et al, 2011). With the development of code generations using compiler technologies (Bondhugula et al, 2008;Grosser et al, 2014a;Henretty et al, 2013;Holewinski et al, 2012;Orozco et al, 2010;Wonnacott and Strout, 2013), formulating stencil kernels has been further facilitated.…”

Section: Prior Work and Current Contributionsmentioning

confidence: 99%

Asynchronous computations for solving the acoustic wave propagation equation

Akbudak

Ltaief

Etienne

et al. 2020

The International Journal of High Performance Computing Applica

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The aim of this study is to design and implement an asynchronous computational scheme for solving the acoustic wave propagation equation with absorbing boundary conditions (ABCs) in the context of seismic imaging applications. While the convolutional perfectly matched layer (CPML) is typically used for ABCs in the oil and gas industry, its formulation further stresses memory accesses and decreases the arithmetic intensity at the physical domain boundaries. The challenges with CPML are twofold: (1) the strong, inherent data dependencies imposed on the explicit time-stepping scheme render asynchronous time integration cumbersome and (2) the idle time is further exacerbated by the load imbalance introduced among processing units. In fact, the CPML formulation of the ABCs requires expensive synchronization points, which may hinder the parallel performance of the overall asynchronous time integration. In particular, when deployed in conjunction with the multicore-optimized wavefront diamond temporal blocking (MWD-TB) approach for the inner domain points, it results in a major performance slow down. To relax CPML’s synchrony and mitigate the resulting load imbalance, we embed CPML’s calculation into MWD-TB’s inner loop and carry on the time integration with fine-grained computations in an asynchronous, holistic way. This comes at the price of storing transient results to alleviate dependencies from critical data hazards while maintaining the numerical accuracy of the original scheme. Performance and scalability results on various x86 architectures demonstrate the superiority of MWD-TB with CPML support against the standard spatial blocking on various grid sizes. To our knowledge, this is the first practical study that highlights the consolidation of CPML ABCs with asynchronous temporal blocking stencil computations.

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Section: Prior Work and Current Contributionsmentioning

confidence: 99%

Asynchronous computations for solving the acoustic wave propagation equation

Akbudak

Ltaief

Etienne

et al. 2020

The International Journal of High Performance Computing Applica

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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%

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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“…Katagiri et al [31], [32] presented ppOpen-AT, a framework for code optimization guided by directives. Sena et al [33] proposed a method to determine a near-optimal workload chunk size by testing a set of possible values in a few time steps of an RTM and choosing the one with the shortest execution time. Andreolli et al [34], [35] introduced an approach to tune seismic applications automatically by compiling and running each set of parameters chosen by a genetic algorithm, including chunk size and compilation flags.…”

Section: Related Workmentioning

confidence: 99%

Auto-Tuning of Dynamic Scheduling Applied to 3D Reverse Time Migration on Multicore Systems

et al. 2020

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Reverse time migration (RTM) is an algorithm widely used in the oil and gas industry to process seismic data. It is a computationally intensive task that suits well in parallel computers. Methods such as RTM can be parallelized in shared memory systems through scheduling iterations of parallel loops to threads. However, several aspects, such as memory size and hierarchy, number of cores, and input size, make optimal scheduling very challenging. In this paper, we introduce a run-time strategy to automatically tune the dynamic scheduling of parallel loops iterations in iterative applications, such as the RTM, in multicore systems. The proposed method aims to reduce the execution time of such applications. To find the optimal granularity, we propose a coupled simulated annealing (CSA) based auto-tuning strategy that adjusts the chunk size of work that OpenMP parallel loops assign dynamically to worker threads during the initialization of a 3D RTM application. Experiments performed with different computational systems and input sizes show that the proposed method is consistently better than the default OpenMP schedulers, static, auto, and guided, causing the application to be up to 33% faster. We show that the possible reason for this performance is the reduction of cache misses, mainly level L3, and low overhead, inferior to 2%. Having shown to be robust and scalable for the 3D RTM, the proposed method could also improve the performance of similar wave-based algorithms, such as full-waveform inversion (FWI) and other iterative applications.

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An Approach to Optimise the Execution of RTM Algorithm in Multicore Machines

Cited by 13 publications

References 8 publications

Asynchronous computations for solving the acoustic wave propagation equation

Asynchronous computations for solving the acoustic wave propagation equation

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

Auto-Tuning of Dynamic Scheduling Applied to 3D Reverse Time Migration on Multicore Systems

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