Vectorization of High-performance Scientific Calculations Using AVX-512 Intruction Set

Shabanov, B. M.; Rybakov, A. A.; Shumilin, S. S.

doi:10.1134/s1995080219050196

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…We'll show below what we did to achieve this goal. 3. Use vectorized mathematical operations as much as possible.…”

Section: Computational Hotspotsmentioning

confidence: 99%

“…Depending on the setup, we used echarge1(vec) (direct charges) + reciprocal charges or empole1(vec) (direct multipoles) + reciprocal multipoles + torque(vec). 3. time_polar, which is the time taken by polarization calculation and by far the biggest.…”

Section: Sequential Performancementioning

confidence: 99%

“…Second, it is organized to present in a pedagogical fashion our code optimization efforts, outlining an optimal strategy for vectorization of Tinker-HP. Although there have been theoretical discussions of AVX-512 vectorization [3], and a description of the effective vectorization of a Quantum Monte-Carlo production code [4], such practical examples are rarely documented and we think our experience could prove useful to other software developers.…”

Section: Introductionmentioning

confidence: 99%

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Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Jolly

Durán

Lagardère³

et al. 2019

LiveCoMS

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This living paper reviews the present High Performance Computing (HPC) capabilities of the Tinker-HP molecular modeling package. We focus here on the reference, double precision, massively parallel molecular dynamics engine present in Tinker-HP and dedicated to perform large scale simulations. We show how it can be adapted to recent Intel ® Central Processing Unit (CPU) petascale architectures. First, we discuss the new set of Intel ® Advanced Vector Extensions 512 (Intel AVX-512) instructions present in recent Intel processors (e.g., the Intel ® Xeon ® Scalable and Intel ® Xeon Phi ™ 2nd generation processors) allowing for larger vectorization enhancements. These instructions constitute the central source of potential computational gains when using the latest processors, justifying important vectorization efforts for developers. We then briefly review the organization of the Tinker-HP code and identify the computational hotspots which require Intel AVX-512 optimization and we propose a general and optimal strategy to vectorize those particular parts of the code. We present our optimization strategy in a pedagogical way so it can benefit other researchers interested in improving performances of their own software. Finally we compare the performance enhancements obtained to unoptimized code, both sequentially and at the scaling limit in parallel for classical non-polarizable (CHARMM) and polarizable force fields (AMOEBA).

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“…We'll show below what we did to achieve this goal. 3. Use vectorized mathematical operations as much as possible.…”

Section: Computational Hotspotsmentioning

confidence: 99%

Section: Sequential Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Jolly

Durán

Lagardère³

et al. 2019

LiveCoMS

View full text Add to dashboard Cite

show abstract

“…The impact of AVX on 3D collision modeling was examined by the authors of [6]. The use of AVX in data science, computational statistics and high-performance computation was studied by the authors of [7,8]. They investigated various vectorization options and available compilers to investigate the impacts on the performances of different algorithms.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Particle Swarm Optimization with AVX Instructions

Šafařík

Snášel

2023

Applied Sciences

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Parallel implementations of algorithms are usually compared with single-core CPU performance. The advantage of multicore vector processors decreases the performance gap between GPU and CPU computation, as shown in many recent pieces of research. With the AVX-512 instruction set, there will be another performance boost for CPU computations. The availability of parallel code running on CPUs made them much easier and more accessible than GPUs. This article compares the performances of parallel implementations of the particle swarm optimization algorithm. The code was written in C++, and we used various techniques to obtain parallel execution through Advanced Vector Extensions. We present the performance on various benchmark functions and different problem configurations. The article describes and compares the performance boost gained from parallel execution on CPU, along with advantages and disadvantages of parallelization techniques.

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A new type of dual-scale neighborhood based on vectorization for cellular automata models

Bin

Wang

2021

GIScience & Remote Sensing

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Vectorization of High-performance Scientific Calculations Using AVX-512 Intruction Set

Cited by 7 publications

References 26 publications

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Acceleration of Particle Swarm Optimization with AVX Instructions

A new type of dual-scale neighborhood based on vectorization for cellular automata models

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