Wave-PIM: Accelerating Wave Simulation Using Processing-in-Memory

Hanindhito, Bagus; Li, Ruihao; Gourounas, Dimitrios; Fathi, Arash; Govil, Karan; Trenev, Dimitar; Gerstlauer, Andreas; John, Lizy K.

doi:10.1145/3472456.3472512

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Basir [33] proposes a reduced-order modeling method that uses modal forms of the model to decrease the computational cost of wave propagation modeling for applications such as inverse time migration. Hanindhito [34] proposes GAPS, a GPU-accelerated PDE solver for wavelet simulations that is efficient and scalable and uses hardware-and data-motion-aware algorithms to improve the performance by up to 84.15 times over CPU implementations and 1.84 times over basic GPU implementations on average. Overall, these works suggest that several algorithms exist for performing wave propagation simulations in oil drilling and that these algorithms can be optimized to improve the performance.…”

Section: Contour Element Methods [27]mentioning

confidence: 99%

Simulation of Wave Propagation Using Finite Differences in Oil Exploration

Suárez-Carreño,

Rosales-Romero,

Salazar

et al. 2023

Applied Sciences

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This paper presents a numerical solution for the 2D acoustic wave equation, considering heterogeneous media. It has been developed through a software in Fortran 90 that uses a second-order finite difference approximation. This program generates a set of patterns to detect the presence of oil in the subsurface. The algorithm is based on a geological domain where the sources (shots) and receivers are located. Each process takes care of a subset of sources and returns to the primary method patterns and seismograms corresponding to its group of sources. In the end, an image of the resulting seismogram is shown along the analyzed geologic profile. Stability and convergence tests were performed to ensure the reliability of the results. These tests were performed using a geological profile 100,000 m long and 17,400 m deep, divided into strata. For the execution of the software, a cluster of 16 processors was used as a computational platform.

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Section: Contour Element Methods [27]mentioning

confidence: 99%

Simulation of Wave Propagation Using Finite Differences in Oil Exploration

Suárez-Carreño,

Rosales-Romero,

Salazar

et al. 2023

Applied Sciences

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“…al. uses digital processing in-memory (PIM) to improve the performance of wave simulation based on DG discretization with GLL integration scheme [14]. Although the result is promising, this emerging technology is not ready for deployment in HPC cluster in the near future.…”

Section: Related Workmentioning

confidence: 99%

Gaps

Hanindhito

Gourounas

Fathi

et al. 2022

Proceedings of the 36th ACM International Conference on Supercomputing

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Large-scale simulations of wave-type equations have many industrial applications, such as in oil and gas exploration. Realistic simulations, which involve a vast amount of data, are often performed on multiple nodes of an HPC cluster. Using GPUs for these simulations is attractive due to considerable parallelizability of the algorithms. Many industry-relevant simulations have characteristics in their physics or geometry that can be exploited to improve computational efficiency. Furthermore, the choice of simulation algorithm impacts computational efficiency significantly.In this work, we exploit these features to significantly improve performance for a class of problems. Specifically, we use the discontinuous Galerkin (DG) finite element method, along with the Gauss-Lobatto-Legendre (GLL) integration scheme on hexahedral elements with straight faces, which then greatly reduces the number of BLAS operations, and simplify the computations to Level-1 BLAS operations, reducing the turn around time for wave simulation. However, attaining peak performance of GPUs is often not possible in these codes that exacerbate bottlenecks caused by data movement, even when modern GPUs enjoying the latest highbandwidth memory are being used.We have developed GAPS, an efficient and scalable, GPUaccelerated PDE solver for Wave Simulation, by using hardwareand data-movement-aware algorithms. While significant speed-up over CPUs can be achieved, data movement still limits GPU performance. We present several optimization strategies, including kernel fusion, Look-Up-Table-based neighbor search, improved shared memory utilization, and SM-occupancy-aware register allocation. They improve performance up to 84.15x over CPU implementations and 1.84x over base GPU implementations on average. We then extend GAPS to support multi-GPUs on multi-node HPC clusters for large-scale wave simulations, and perform additional optimizations to reduce communication overhead. We also investigate the performance of several MPI libraries in order to fully overlap communication and computation. We are able to reduce the communication overhead by 70%, and achieve weak-scaling over 128 GPUs.

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CRPIM: An efficient compute-reuse scheme for ReRAM-based Processing-in-Memory DNN accelerators

Hong,

Chung

2024

Journal of Systems Architecture

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Wave-PIM: Accelerating Wave Simulation Using Processing-in-Memory

Cited by 7 publications

References 37 publications

Simulation of Wave Propagation Using Finite Differences in Oil Exploration

Simulation of Wave Propagation Using Finite Differences in Oil Exploration

Gaps

CRPIM: An efficient compute-reuse scheme for ReRAM-based Processing-in-Memory DNN accelerators

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