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Abstract <p>SIMD optimization plays a crucial role in the acceleration of scientific plasma physics codes, where large arrays and repetitive numerical operations dominate computation. Using SIMD instructions, multiple data elements can be processed simultaneously within a single CPU cycle, greatly reducing runtime compared to scalar implementations. This efficiency enables researchers to perform higher-resolution plasma simulations within practical time limits. Modern CPUs offer wide SIMD registers, allowing near-GPU-level performance for certain workloads when used effectively. Without SIMD optimization, a significant portion of processor computational power remains untapped. To fully benefit from these capabilities, careful attention must be paid to data alignment and memory layout. In addition to improving performance, the well-vectorized code also decreases energy consumption per calculation. Consequently, mastering SIMD optimization has become an essential skill for developing efficient, high-performance software in computational plasma physics. In this paper, we will show how vectorization affects plasma physics code performance.</p>

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Optimization of a 3D Plasma Physics Code for the Numerical Simulation of a Diamagnetic ‘‘Bubble’’ in Linear Traps

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • I. G. Chernykh,
  • V. A. Vshivkov,
  • I. S. Chernoshtanov,
  • I. M. Kulikov,
  • M. A. Lebedev,
  • N. V. Snytnikov,
  • T. V. Liseykina,
  • M. A. Boronina

摘要

Abstract

SIMD optimization plays a crucial role in the acceleration of scientific plasma physics codes, where large arrays and repetitive numerical operations dominate computation. Using SIMD instructions, multiple data elements can be processed simultaneously within a single CPU cycle, greatly reducing runtime compared to scalar implementations. This efficiency enables researchers to perform higher-resolution plasma simulations within practical time limits. Modern CPUs offer wide SIMD registers, allowing near-GPU-level performance for certain workloads when used effectively. Without SIMD optimization, a significant portion of processor computational power remains untapped. To fully benefit from these capabilities, careful attention must be paid to data alignment and memory layout. In addition to improving performance, the well-vectorized code also decreases energy consumption per calculation. Consequently, mastering SIMD optimization has become an essential skill for developing efficient, high-performance software in computational plasma physics. In this paper, we will show how vectorization affects plasma physics code performance.