Radiation Environment and Medicine 2023 Vol.12, No.2 107–110

ReportExpectation for Utilizing Supercomputers
in Natural Radiation ResearchKazuki Iwaoka1*, Masahiro Hosoda 2 and Shinji Tokonami2
1National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan
2Hirosaki University, 66-1 Honcho, Hirosaki, Aomori 036-8564, Japan
Received 30 August, 2022; revised 5 January, 2023; accepted 27 January, 2023

In Japan, there are 33 types of supercomputers with registered specifications. One of these is
jointly owned by the National Institutes for Quantum Science and Technology (QST) and theJapan Atomic Energy Agency (JAEA). This supercomputer has been used in some studies,and information on using it in other research (i.e., natural radiation research) is limited. This
supercomputer was used to perform two cases related to natural radiation research (Case 1–Monte
Carlo radiation transport calculation and Case 2–Building Artificial Intelligence image recognitionmodel) in this study. This study describes the expected benefits and drawbacks of using thissupercomputer from the viewpoint of general natural radiation researchers.
Key words: natural radiation, supercomputer, radiation transport simulation, image recognition

1. Introduction
Supercomputers are developed by various institutions andare occasionally used for calculations in various researches.
Recently, there are 33 types of supercomputers withregistered specifications in Japan1). One of these is jointly
owned by the National Institutes for Quantum Science andTechnology (QST) and the Japan Atomic Energy Agency(JAEA). This supercomputer has been used in somestudies (e.g., atmospheric diffusion analysis) for various
computational calculatuion2, 3). This supercomputer could
also be used in natural radiation research other than theabove research, as follows.
Case 1–Monte Carlo radiation transport calculation:
Although the dose rate (e.g., air kerma rate) for naturalradiation sources is often evaluated using Monte Carlo*Kazuki Iwaoka: National Institutes for Quantum Science and Technology, 4-9-1
Anagawa, Inage, Chiba 263-8555, Japan
E-mail: iwaoka.kazuki@qst.go.jp
https://doi.org/10.51083/radiatenvironmed.12.2_107
Copyright © 2023 by Hirosaki University. All rights reserved.

radiation transport calculations, it generally takes time
on a common PC. This supercomputer may be useful in
reducing the processing time for its calculation.
Case 2–Building Artificial Intelligence (AI) image
recognition model: Image recognition is often used in AI
research and can be used in natural radiation research(e.g., a radiation phenomenon observed on an image, i.e.,
the tracks of nuclear particles on solid state detector).
However, building an AI image recognition model on
a common PC is a time consuming and complicatedprocess. This supercomputer could be useful in reducing
the processing time for building the model.
In this study, two cases related to natural radiation
research (Case 1 and 2) were examined using thissupercomputer to determine the practical benefits ofusing supercomputers, such as processing speed.
2. Materials and methods
2.1. Performance of supercomputer in this study
For this study, a supercomputer (known as HPE SGI8600)

108

Kazuki Iwaoka et al. / Radiation Environment and Medicine 2023 Vol.12, No.2 107–110
Table 1. Expediential conﬁguration of the radiation transport calculation.
Item
Source

Tally
Material
Calculation

Remarks

Fig. 1. Photo of the supercomputer (HPE SGI8600) in this study.

jointly owned by the QST and the JAEA and has been in
operation since 2020, was used (Fig. 1). According to theexplanation for the performance of this supercomputer4),
this supercomputer mainly comprises a General-Purpose
computing on Graphics Processing Unit (GPU) andCentral Processing Unit (CPU) sections for normal use.
The total theoretical peak performance for the GPU
section is 9.739 Peta Floating-Point Operations Per Second(PFLOPS), total number of computing nodes is 272, total
number of cores is 13056 based on Intel Xeon Gold 6248R(3.0 GHz and 24 core), total number of units is 1088 basedon NVIDIA Tesla V100 SXM2 (32 GB), and total RAM
capacity is 102 TB. The total theoretical peak performanceof CPU section is 2.801 PFLOPS, total number ofcomputing nodes is 706, total number of cores is 28240
based on Intel Xeon Gold 6242R (3.1 GHz and 20 core),and total RAM capacity is 132.375 TB. The CPU section
is used for all operations, such as calculation, whereas theGPU section is effective for specific operations such asimage recognition.
2.2. Case and condition
Two simple cases for natural radiation research, i.e., Case
1–Monte Carlo radiation transport calculation and Case 2–
Building AI image recognition model, were performed by
the CPU and GPU sections, respectively.
Case 1–Monte Carlo radiation transport calculation: In one
case, for convenience, a simple calculation of photon fluencein the air at a distance of 1 m from the natural radiation
source was assigned 40K of 1 Bq in this supercomputer.

Configuration
Type: point source
Nuclides: 40K of 1 Bq
Emission direction: 4 π sr
Particle: photon
Position: distance of 1 m from a source
Target: fluence
Whole space: airMonte Carlo tool: PHITS version 3.265)
History per batch: 10000000
Batch number: adjusted to be 5 processes
Operational validation: It was confirmed in advance thatthe air kerma rate obtained from the fluence underthese configurations was 1.85 × 10-5 nGy/h, which was
99% or more identical to that in the literature6).

Details on expediential configuration are shown in Table 1.
The CPU section of this supercomputer with the above
configuration ran under various supercomputerʼs job
conditions. The total number of cores was adjusted by
setting the number of nodes. A common local computer
was also used as a reference case. To avoid the effects
of unexpected initial extra work during the first process,the processing time of the second process was used forperformance comparison.
Case 2–Building AI image recognition model: In one case,for convenience, a simple build of a two-dimensional (2D)
image recognition model for squares, triangles, circles,
and hexagons was assigned to this supercomputer.
Details on expediential configuration are shown in
Table 2. The GPU section of this supercomputer withthe above configuration ran under two supercomputerʼsjob conditions, namely, GPU enabled and GPU disabled.
The GPU environment was built using Compute Unified
Device Architecture (CUDA), a platform and programon NVIDIA GPUs that can accelerate computing
applications9). A common local computer was also used
as a reference case. For performance comparison, theprocessing time in the second loop (epoch) was used toavoid the effects of unexpected initial extra work during
the first loop.
3. Results and discussion
Case 1–Monte Carlo radiation transport calculation: The
results of this supercomputer and a common local
computer are shown in Table 3. As shown by theresults in each supercomputerʼs job condition, it waspossible to speed up by increasing the number of OpenMultiprocessing (OMP) threads, but this was not asignificant difference compared with increasing thenumber of Message Passing Interface (MPI) processes.

Kazuki Iwaoka et al. / Radiation Environment and Medicine 2023 Vol.12, No.2 107–110

109

Table 2. Expediential conﬁguration for building a simple model.
ItemImages

Leaning

Remarks

Configuration
Size: 100×100 pixels
Color: blue and white
Number: square 1000, triangle 1000, circle 1000, and hexagon 1000
(90% for learning images and 10% for test images)
Platform: TensorFlow version 2.4.3-gpu7)
Loop number (Epoch): 20
Containerization: singularity8)
Operational validation: It was confirmed in advance that the accuracy of the recognition
ability under these configurations was ≥ 99%.

Table 3. Results of supercomputer and a common local computer.
Reference2(Local computer)
Job condition
Processing section1
CPUCPU
CPUCPUCPU
Total core number3
110202080
MPI process number
1112080
8 (Maximum performance)
OMP thread number
11020
11
ResultProcessing time per one batch
34 s
15 s
14 s
1.9 s
0.50 s14 s
1: Allocation was set to be exclusive. 2: A common notebook (Letʼs note CF-LV7, Panasonic Corp., Japan) was used for the local
computer. 3: Total core number = OMP thread number×MPI process number.
Job name

SPC1

SPC2

SPC3

SPC4

SPC5

Table 4. Results of supercomputer and a common local computer.
Reference2(Local computer)
Job
Processing section1
GPUGPUcondition
Operation of CUDA
Yes
No
No
(i.e., GPU enabled)(i.e., GPU disabled)(i.e., GPU disabled)
ResultProcessing time per one epoch0.72 s
8.5 s
49 s
1: Allocation was set to be exclusive. The number of GPU, core, MPI, and OMP is 1, 20, 1, and 1, respectively. 2: A common
notebook (Letʼs note CF-LV9, Panasonic Corp., Japan) was used for the local computer.
Job name

SPG1

When used for MPI processes instead of OMP threads,
the cores can be more effective at speeding up. In thisstudy, a limited number of MPI processes were used toprevent unnecessary competition among users. However,in theory, the number of MPI processes can be increased
by increasing the number of cores and nodes in theCPU section of this supercomputer (approximately 7500
MPI processes as a maximum for general users), which
would significantly speed up the calculation. This is aconsiderable advantage for shortening the calculation
time for natural radiation research like this case because
only a few MPI processes are available on common local
computers (Table 3). However, this supercomputer has
a disadvantage in that it requires additional work (e.g.,command line operations, specific script preparation, anddata transfer). These additional works are generally nottroublesome, but some natural radiation researchers who
are unfamiliar with computer literacy may consider theseadditional works as serious problems.
Case 2–Building AI image recognition model: The results

SPG2of this supercomputer and a common local computerare shown in Table 4. As shown in the results in eachsupercomputerʼs job condition, the GPU environment
with operation on CUDA (GPU-CUDA) could build
the model at a higher speed than that without GPUCUDA. Generally, local common PCs do not have a GPUCUDA environment because GPU-CUDA is a specificand expensive option; it takes time for the common PC
to build the model (Table 4). This supercomputer has asignificant advantage in studies aimed at its application tonatural radiation research, such as this case because theGPU-CUDA environment is available as the default state.
However, this supercomputer also has a disadvantage
in that a singularity container is required because users
are restricted from installing any software on thissupercomputer on their own. Some natural radiation
researchers may have difficulty preparing the container.

110

Kazuki Iwaoka et al. / Radiation Environment and Medicine 2023 Vol.12, No.2 107–110

4. Conclusions

Author contribution

For natural radiation research, such as this study, the useof this supercomputer can accelerate computer processing
time. However, common natural radiation researchers,
especially those in experimental fields, are unfamiliar
with computer literacy, and mastering how to use, set up,
and operate this supercomputer would take a long time.
It is necessary to have opportunities to teach and shareskills about this supercomputer with each other. Theauthor is considering opportunities to hold study sessions,particularly for young researchers. These efforts areexpected to increase the number of supercomputer users
in the natural radiation research community, resulting
in the production of new knowledge, technology anddiversity in natural radiation research. In this regard, forexample in terms of future technological development,
since this supercomputer has many GPUs, considerationof improving GPU parallelization may be expected as aninteresting research.

Kazuki Iwaoka performed the study and wrote themanuscript. All authors contributed extensively in thediscussion of the work and the review of the manuscript.

Acknowledgment
This work was partially supported by JSPS KAKENHI
Gra nt Numbers JP17K K 0162, JP18K 07741, a ndJP21K12360, as well as Environmental Radioactivity
Research Network Center F-22-46. The authors would
like to thank Yuto Tomisaka for kind support regarding
natural radiation research.
Disclosure
The authors declare that they have no conflicts ofinterest.

References1.

TOP 500. [Internet]. The 59th TOP500 list [cited 2022 July 1].
Available from: https://www.top500.org/lists/top500/.

2.

QST. Research Reports Using Supercomputer Systems in FY2020,QST-R-21. Chiba: National Institutes for Quantum Science andTechnology; 2021. Japanese.

3.

JAEA. Summaries of Research and Development Activities byUsing Supercomputer System of JAEA in FY2020, JAEA-Review
2021-022. Ibaraki: Japan Atomic Energy Agency; 2022. Japanese.

4.

JAEA. [Internet]. HPE SGI8600 [cited 2022 July 1]. Available from:https://ccse.jaea.go.jp/computer/index_eng.html.

5.

Sato T, Iwamoto Y, Hashimoto S, Ogawa T, Furuta T, Abe S, etal. Features of Particle and Heavy Ion Transport code System(PHITS) version 3.02. J. Nucl. Sci. Technol. 2018;55:684–90.

6.

JRIA. Radioisotope Pocket Data Book 11th edition. Tokyo: Japan
Radioisotope Association; 2011. Japanese.

7.

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C,
et al. [Internet]. TensorFlow: large-scale machine learning onheterogeneous distributed systems [cited 2022 July 4]. Available
from: https://www.tensorflow.org/.

8.

Kurtzer GM, Sochat V, Bauer MW. Singularity: scientificcontainers for mobility of compute. PLoS One. 2017;12:e0177459.

9.

NVIDIA. [Internet]. NVIDIA developer [cited 2022 July 1].
Available from: https://developer.nvidia.com/cuda-zone.