Energy Efficiency of a New Parallel PIC Code for Numerical Simulation of Plasma Dynamics in Open Trap

Igor Chernykh (), Igor Kulikov, Vitaly Vshivkov, Ekaterina Genrikh, Dmitry Weins, Galina Dudnikova, Ivan Chernoshtanov and Marina Boronina
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Igor Chernykh: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Igor Kulikov: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Vitaly Vshivkov: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Ekaterina Genrikh: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Dmitry Weins: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Galina Dudnikova: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Ivan Chernoshtanov: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Marina Boronina: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia

Mathematics, 2022, vol. 10, issue 19, 1-9

Abstract: The generation of energy-efficient parallel scientific codes became very important in the time of carbon footprint reduction. In this paper, we briefly present our latest particle-in-cell code with the results of a numerical simulation of plasma dynamics in an open trap. This code can be auto-vectorized by the Fortran compiler for Intel Xeon processors with AVX-512 instructions such as Intel Xeon Phi and the highest series of all generations of Intel Xeon Scalable processors. Efficient use of processor architecture is the main feature of an energy-efficient solution. We present a step-by-step methodology of energy consumption calculation using Intel hardware features and Intel VTune software. We also give an estimated value of carbon footprint with the impact of high-performance water cooled hardware. The Power Usage Effectiveness (PUE) in the case of high-performance water cooled hardware is equal to 1.03–1.05, and is up to 1.3 in the case of air-cooled systems. This means that power consumption of liquid cooled systems is lower than that air-cooled ones by up to 25%. All these factors play an important role in the carbon footprint reduction problem.

Keywords: high performance computing; particle-in-cell method; energy efficient algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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