Symmetry/Asymmetry in Numerical Analysis and Scientific Computing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 994

Special Issue Editors


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Guest Editor
Centro de Desarrollo Aeroespacial, Instituto Politécnico Nacional, Mexico City 06610, Mexico
Interests: GPU computing; MPI; OpenMP; CUDA; parallel programming

E-Mail Website
Guest Editor
Centro de Desarrollo Aeroespacial, Instituto Politécnico Nacional, Mexico City 06610, Mexico
Interests: HPC; potential methods

Special Issue Information

Dear Colleagues,

This Special Issue of Symmetry explores the role of symmetry and asymmetry in numerical analysis and parallel computing algorithms, focusing on their impact on performance optimization and computational efficiency. Symmetry is fundamental in balancing loads in GPU and multi-GPU architectures, ensuring efficient resource utilization while minimizing bottlenecks in parallel processing.

Furthermore, maintaining balance and symmetry is critical for optimizing performance in numerical analysis and parallel programming models, such as OpenMP, SYCL, OpenACC, and MPI, as well as their hybrid implementations. Effective load-balancing strategies, guided by considerations regarding symmetry, help distribute workloads evenly across processing units, leading to scalable and high-performance computing solutions. Symmetry helps optimize performance; asymmetry also plays a role, especially in heterogeneous architectures, where different computing resources (e.g., CPUs, GPUs, and accelerators) have varying performance characteristics. Addressing asymmetric workloads in numerical methods and parallel computing is an ongoing research challenge, requiring adaptive scheduling and load-balancing techniques.

This Special Issue invites contributions that analyze theoretical and practical aspects of symmetry in parallel algorithms, explore innovative approaches for handling asymmetry in numerical analysis and heterogeneous computing environments, and propose novel methodologies for improving efficiency in modern high-performance computing (HPC) applications.

Dr. Carlos Couder-Castañeda
Dr. José Carlos Ortiz Alemán
Guest Editors

Manuscript Submission Information

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Keywords

  • numerical analysis
  • parallel computing
  • symmetry in algorithms
  • asymmetry in computing
  • load balancing
  • GPU computing
  • multi-GPU systems
  • heterogeneous computing
  • high-performance computing (HPC)
  • OpenMP
  • SYCL
  • OpenACC
  • MPI
  • hybrid parallel programming
  • performance optimization
  • scalability in parallel systems
  • task scheduling
  • algorithm efficiency
  • computational workload distribution
  • resource allocation

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Published Papers (1 paper)

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Research

20 pages, 23906 KB  
Article
Improved Depth Imaging of the Chicxulub Impact Crater by GPU-Accelerated Adjoint Reverse Time Migration
by Jesús Antonio Herrera-Pérez, Jose Carlos Ortíz-Alemán, Sebastián López-Juárez, Jhonatan Fernando Eulopa-Hernandez, Carlos Couder-Castañeda, Isaac Medina-Sanchez, Jairo Olguin-Roque and Diego Alfredo Padilla-Pérez
Symmetry 2026, 18(4), 658; https://doi.org/10.3390/sym18040658 - 15 Apr 2026
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Abstract
Reverse time migration (RTM) exploits time-reversal symmetry and adjoint duality to focus wavefields and reconstruct subsurface reflectivity, but large surveys remain limited by the cost of forward and backward propagation. We present a Graphics Processing Unit (GPU)-accelerated adjoint RTM workflow for depth imaging [...] Read more.
Reverse time migration (RTM) exploits time-reversal symmetry and adjoint duality to focus wavefields and reconstruct subsurface reflectivity, but large surveys remain limited by the cost of forward and backward propagation. We present a Graphics Processing Unit (GPU)-accelerated adjoint RTM workflow for depth imaging of the Chicxulub impact structure using the marine A0/A1 composite profile (1996). The processed stacked section contains 14,172 traces with 6.25 m Common Depth Point (CDP) spacing, 1 ms sampling, and 18 s record length. Forward and adjoint wavefields are computed with a staggered-grid finite-difference scheme (fourth order in space, second in time) and Convolutional Perfectly Matched Layers (CPMLs), which provide stable finite-domain simulations while introducing controlled symmetry breaking through absorption. The solver is verified with the Lamb half-space analytical benchmark and applied through five interpretation-guided velocity/density updates. The final depth image improves reflector continuity and interpretability of crater-scale elements, including post-impact sedimentary fill, melt and breccia units, terrace fault blocks, and deep uplift-related structure. Compute Unified Device Architecture (CUDA) acceleration reduces runtime from ∼32.36 h on a CPU baseline to ∼34.10 min on an RTX 3070 (≈56.9×), enabling practical, reproducible iterative RTM on accessible hardware. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Numerical Analysis and Scientific Computing)
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