Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms

Joseph Schuchart

doi:10.1145/3712285.3769439

Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms

Joseph Schuchart

Institute for Advanced Computational Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This reproducibility report provides details about the artifact evaluation done with regards to the Artifact Description and Evaluation appendix of SC25 paper “X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms” by Sajal Dash, Minjia Zhang, Feiyi Wang, Jianping Li, Yueming Yuan, and Ahan Gupta. The work was done as part of the Reproducibility Initiative of SC25. The author is a member of the SC25 Reproducibilty Committee.

Original language	English
Title of host publication	Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025
Publisher	Association for Computing Machinery, Inc
Pages	2272-2273
Number of pages	2
ISBN (Electronic)	9798400714665
DOIs	https://doi.org/10.1145/3712285.3769439
State	Published - Nov 15 2025
Event	2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025 - St. Louis, United States Duration: Nov 16 2025 → Nov 21 2025

Publication series

Name	Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025

Conference

Conference	2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025
Country/Territory	United States
City	St. Louis
Period	11/16/25 → 11/21/25

Keywords

Artifact Evaluation
Reproducibility

Access to Document

10.1145/3712285.3769439

Cite this

Schuchart, J. (2025). Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025 (pp. 2272-2273). (Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025). Association for Computing Machinery, Inc. https://doi.org/10.1145/3712285.3769439

Schuchart, Joseph. / Reproducibility Report for SC25 Paper X-MoE : Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025. Association for Computing Machinery, Inc, 2025. pp. 2272-2273 (Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025).

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title = "Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms",

abstract = "This reproducibility report provides details about the artifact evaluation done with regards to the Artifact Description and Evaluation appendix of SC25 paper “X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms” by Sajal Dash, Minjia Zhang, Feiyi Wang, Jianping Li, Yueming Yuan, and Ahan Gupta. The work was done as part of the Reproducibility Initiative of SC25. The author is a member of the SC25 Reproducibilty Committee.",

keywords = "Artifact Evaluation, Reproducibility",

author = "Joseph Schuchart",

note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025 ; Conference date: 16-11-2025 Through 21-11-2025",

year = "2025",

month = nov,

day = "15",

doi = "10.1145/3712285.3769439",

language = "English",

series = "Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025",

publisher = "Association for Computing Machinery, Inc",

pages = "2272--2273",

booktitle = "Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025",

}

Schuchart, J 2025, Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms. in Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025, Association for Computing Machinery, Inc, pp. 2272-2273, 2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025, St. Louis, United States, 11/16/25. https://doi.org/10.1145/3712285.3769439

Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms. / Schuchart, Joseph.
Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025. Association for Computing Machinery, Inc, 2025. p. 2272-2273 (Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Reproducibility Report for SC25 Paper X-MoE

T2 - 2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025

AU - Schuchart, Joseph

PY - 2025/11/15

Y1 - 2025/11/15

N2 - This reproducibility report provides details about the artifact evaluation done with regards to the Artifact Description and Evaluation appendix of SC25 paper “X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms” by Sajal Dash, Minjia Zhang, Feiyi Wang, Jianping Li, Yueming Yuan, and Ahan Gupta. The work was done as part of the Reproducibility Initiative of SC25. The author is a member of the SC25 Reproducibilty Committee.

AB - This reproducibility report provides details about the artifact evaluation done with regards to the Artifact Description and Evaluation appendix of SC25 paper “X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms” by Sajal Dash, Minjia Zhang, Feiyi Wang, Jianping Li, Yueming Yuan, and Ahan Gupta. The work was done as part of the Reproducibility Initiative of SC25. The author is a member of the SC25 Reproducibilty Committee.

KW - Artifact Evaluation

KW - Reproducibility

UR - https://www.scopus.com/pages/publications/105023970958

U2 - 10.1145/3712285.3769439

DO - 10.1145/3712285.3769439

M3 - Conference contribution

AN - SCOPUS:105023970958

T3 - Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025

SP - 2272

EP - 2273

BT - Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025

PB - Association for Computing Machinery, Inc

Y2 - 16 November 2025 through 21 November 2025

ER -

Schuchart J. Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025. Association for Computing Machinery, Inc. 2025. p. 2272-2273. (Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025). doi: 10.1145/3712285.3769439

Reproducibility Report for SC25 Paper X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms

Abstract

Publication series

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Keywords

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