The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
정적 인과관계 발견은 인과관계가 밝혀지지 않은 관찰변수들 사이의 인과관계를 추론하는 접근방식이다. 인과관계 발견을 위한 알고리즘인 LiNGAM(Linear Non-Gaussian Acycling Model)은 변수의 독립성분이 비가우시안이라고 가정하면 고유하게 인과관계를 계산할 수 있습니다. 그러나 LiNGAM의 사용 사례는 다음과 같은 이유로 제한됩니다. O(d3x) 계산 복잡성, 여기서 dx 변수의 개수입니다. 본 논문에서는 LiNGAM 인과관계 발견을 가속화하기 위한 두 가지 접근법, 즉 LiNGAM의 수학적 행렬 연산을 위한 SIMD 활용과 MPI 병렬화를 보여줍니다. 우리는 슈퍼컴퓨터 Fugaku를 사용한 구현을 평가합니다. Fugaku의 96개 노드를 사용하여 개선된 버전은 원래 OSS 구현보다 17,531배 빠른 속도를 달성할 수 있습니다(17.7시간 만에 완료).
Kazuhito MATSUDA
Fujitsu Limited
Kouji KURIHARA
Fujitsu Limited
Kentaro KAWAKAMI
Fujitsu Limited
Masafumi YAMAZAKI
Fujitsu Limited
Fuyuka YAMADA
Fujitsu Limited
Tsuguchika TABARU
Fujitsu Limited
Ken YOKOYAMA
Fujitsu Limited
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Kazuhito MATSUDA, Kouji KURIHARA, Kentaro KAWAKAMI, Masafumi YAMAZAKI, Fuyuka YAMADA, Tsuguchika TABARU, Ken YOKOYAMA, "Accelerating LiNGAM Causal Discovery with Massive Parallel Execution on Supercomputer Fugaku" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 12, pp. 2032-2039, December 2022, doi: 10.1587/transinf.2022PAP0007.
Abstract: Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022PAP0007/_p
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@ARTICLE{e105-d_12_2032,
author={Kazuhito MATSUDA, Kouji KURIHARA, Kentaro KAWAKAMI, Masafumi YAMAZAKI, Fuyuka YAMADA, Tsuguchika TABARU, Ken YOKOYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Accelerating LiNGAM Causal Discovery with Massive Parallel Execution on Supercomputer Fugaku},
year={2022},
volume={E105-D},
number={12},
pages={2032-2039},
abstract={Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).},
keywords={},
doi={10.1587/transinf.2022PAP0007},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - Accelerating LiNGAM Causal Discovery with Massive Parallel Execution on Supercomputer Fugaku
T2 - IEICE TRANSACTIONS on Information
SP - 2032
EP - 2039
AU - Kazuhito MATSUDA
AU - Kouji KURIHARA
AU - Kentaro KAWAKAMI
AU - Masafumi YAMAZAKI
AU - Fuyuka YAMADA
AU - Tsuguchika TABARU
AU - Ken YOKOYAMA
PY - 2022
DO - 10.1587/transinf.2022PAP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2022
AB - Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).
ER -