Overview

The aim of this project is to design and develop a comprehensive benchmark suite for carrying out performance evaluation of modern cluster networking and I/O technologies.

Description

Currently the benchmark suite contains the following benchmarks that can be used for performance evaluation of clusters with modern networking technologies, compute platforms and MPI libraries:

Evaluation of Networking Technologies only
- Send/Recv (Latency and Bandwidth)
- RDMA (Latency and Bandwidth)
Evaluation of Compute Node Architectures and MPI Libraries
- Latency
- Bandwidth (single-pair and multi-pair)
- Bidirectional Bandwidth (single-pair and multi-pair)
- Messaging Rate (single-pair and multi-pair)
Evaluation of Compute Node Architectures, MPI Libraries and Networking Technlogies
- Two-sided Operations
  - Latency
  - Bandwidth (single-pair and multi-pair)
  - Bidirectional Bandwidth (single-pair and multi-pair)
  - Messaging Rate (single-pair and multi-pair)
  - Bandwidth with different Window sizes
- One-sided Operations
  - Put: latency, bandwidth and bidirectional bandwidth
  - Get: latency, bandwidth and bidirectional bandwidth
  - Accumulate: latency
- Collective Operation
  - Broadcast/Multicast Latency
- Multi-threading
  - Multi-threaded latency
- Computation and Communication Overlap
  - Send/Recv
  - RDMA Write
  - RDMA Read
Evaluation of Network Topology
- Multi-hop latency
- Displaced Ring Communication
- Fan-out
- Fan-in
Evaluation of Multi-rail Network Configurations
- Latency
- Bandwidth
- Bidirectional Bandwidth

Some of these benchmarks are available for download here. The complete set of banchmarks will be available from this web page in the near future.

Software Distribution

Link to Performance Evaluation of Cluster Networking and I/O Technologies

Conferences & Workshops (8)
1	Profiling, Storing and Monitoring HPC Communication Data at Scale by OSU INAM P. Kousha, H. Subramoni, DK Panda, M. Tatineni, and P. Mulrooney, ISC HIGH PERFORMANCE 2024, May 2024 [Research Poster] [Bib - Plain]
2	Benchmarking Modern Databases for Storing and Profiling Very Large Scale HPC Communication Data P. Kousha, Q. Zhou, H. Subramoni, and DK Panda, The 15th BenchCouncil International Symposium On Benchmarking, Measuring And Optimizing, Dec 2023 [Bib - Plain]
3	"Hey CAI" - Enhancing User Productivity through a Conversational AI Enabled User Interface for HPC Tools P. Kousha, A. Jain, A. Kolli, S. Prasanna, S. Miriyala, H. Subramoni, A. Shafi, and DK Panda, ISC HIGH PERFORMANCE 2022, May 2022 [Bib - Plain]
4	INAM: Cross-stack Profiling and Analysis of Communication in MPI-based Applications P. Kousha, Kamal Raj Sankarapandian Dayala Ganesh Ram, M. Kedia, H. Subramoni, A. Jain, A. Shafi, DK Panda, Trey Dockendorf, Heechang Na, and K. Tomko, Practice and Experience in Advanced Research Computing 2021, Jul 2021 [Bib - Plain]
5	Accelerated Real-time Network Monitoring and Profiling at Scale using OSU INAM P. Kousha, S. D. Kamal Raj, H. Subramoni, DK Panda, H. Na, T. Dockendorf, and K. Tomko, Practice and Experience in Advanced Research Computing 2020, Jul 2020 [Bib - Plain]
6	OSU INAM: Profiling and Visualization Tool for Scalable and In-Depth Analysis of High-Performance GPU-enabled HPC Clusters P. Kousha, Kamal Raj Sankarapandian Dayala Ganesh Ram, H. Subramoni, and DK Panda, ISC HIGH PERFORMANCE 2020, Jun 2020 [Best Poster Award] [Bib - Plain]
7	Designing a Profiling and Visualization Tool for Scalable and In-Depth Analysis of High-Performance GPU Clusters P. Kousha, B. Ramesh, K. Suresh, C. Chu, A. Jain, N. Sarkauskas, H. Subramoni, and DK Panda, 26th IEEE International Conference on High Performance Computing, Data, Analytics and Data Science, Dec 2019 [Bib - Plain]
8	Characterization of Big Data Stream Processing Pipeline: A Case Study using Flink and Kafka M. H. Javed, X. Lu, and DK Panda, 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Dec 2017 [Bib - Plain]

NOWLAB: Network Based Computing Lab

Performance Evaluation of Cluster Networking and I/O Technologies

Overview

Description

Software Distribution

Conferences & Workshops (8)

Profiling, Storing and Monitoring HPC Communication Data at Scale by OSU INAM

Benchmarking Modern Databases for Storing and Profiling Very Large Scale HPC Communication Data

"Hey CAI" - Enhancing User Productivity through a Conversational AI Enabled User Interface for HPC Tools

INAM: Cross-stack Profiling and Analysis of Communication in MPI-based Applications

Accelerated Real-time Network Monitoring and Profiling at Scale using OSU INAM

OSU INAM: Profiling and Visualization Tool for Scalable and In-Depth Analysis of High-Performance GPU-enabled HPC Clusters

Designing a Profiling and Visualization Tool for Scalable and In-Depth Analysis of High-Performance GPU Clusters

Characterization of Big Data Stream Processing Pipeline: A Case Study using Flink and Kafka

Sponsors