Predicting the performance impact of different fat-tree configurations-Reference-Cited by-同舟云学术

Predicting the performance impact of different fat-tree configurations

Published:2017-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
language:
Short-container-title:

Author:

Jain Nikhil¹,Bhatele Abhinav¹,Howell Louis H.¹,Böhme David¹,Karlin Ian¹,León Edgar A.¹,Mubarak Misbah²,Wolfe Noah³,Gamblin Todd¹,Leininger Matthew L.¹

Affiliation:

1. Lawrence Livermore National Laboratory

2. Argonne National Laboratory

3. Rensselaer Polytechnic Institute

Funder

U.S. Department of Energy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3126908.3126967

Reference43 articles.

1. 2015. Performance Scaled Messaging 2 (Intel). https://www.intel.com/content/dam/support/us/en/documents/network/omni-adptr/sb/Intel_PSM2_PG_H76473_v1_0.pdf. (2015). 2015. Performance Scaled Messaging 2 (Intel). https://www.intel.com/content/dam/support/us/en/documents/network/omni-adptr/sb/Intel_PSM2_PG_H76473_v1_0.pdf. (2015).

2. 2015. Score-P User Manual. https://silc.zih.tu-dresden.de/scorep-current/pdf/scorep.pdf. (2015). 2015. Score-P User Manual. https://silc.zih.tu-dresden.de/scorep-current/pdf/scorep.pdf. (2015).

3. 2017. Catalyst (LLNL). https://hpc.llnl.gov/hardware/platforms/catalyst. (2017). 2017. Catalyst (LLNL). https://hpc.llnl.gov/hardware/platforms/catalyst. (2017).

4. 2017. MFEM: Modular finite element methods. mfem.org. (2017). 2017. MFEM: Modular finite element methods. mfem.org. (2017).

5. 2017. MULARD. (2017). https://codesign.llnl.gov/mulard.php. 2017. MULARD. (2017). https://codesign.llnl.gov/mulard.php.

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Graph Analytics on Jellyfish topology;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

2. Trade-off topology design for hierarchical network based on job characteristics;CCF Transactions on High Performance Computing;2024-05-21

3. Analysis and prediction of performance variability in large-scale computing systems;The Journal of Supercomputing;2024-03-28

4. Understanding Node Allocation on Leadership-Class Supercomputers with Graph Analytics;2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2023-12-17

5. A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks;IEEE Transactions on Services Computing;2023-07-01