Cross-layer Visualization and Profiling of Network and I/O Communication for HPC Clusters

TL;DR

This paper introduces a holistic, real-time visualization and profiling tool for HPC communication stacks, enabling better understanding of cross-layer interactions and I/O behavior to identify bottlenecks.

Contribution

The paper presents a novel cross-layer visualization method and a low-overhead I/O profiling approach integrated into HPC communication libraries.

Findings

Holistic visualization provides real-time insights into HPC communication.

Cross-stack analysis reveals correlations between I/O traffic and MPI communication.

The approach effectively detects communication bottlenecks in HPC systems.

Abstract

Understanding and visualizing the full-stack performance trade-offs and interplay between HPC applications, MPI libraries, the communication fabric, and the file system is a challenging endeavor. Designing a holistic profiling and visualization method for HPC communication networks is challenging since different levels of communication coexist and interact with each other on the communication fabric. A breakdown of traffic is essential to understand the interplay of different layers along with the application's communication behavior without losing a general view of network traffic. Unfortunately, existing profiling tools are disjoint and either focus on only profiling and visualizing a few levels of the HPC stack, which limits the insights they can provide, or they provide extremely detailed information which necessitates a steep learning curve to understand. We target our profiling tool visualization to provide holistic and real-time insights into HPC communication stacks. In this paper, we propose and implement our visualization methods to enable holistic insight for representing the cross-stack metrics. Moreover, we propose and implement a low-overhead I/O profiling inside the communication library, collect and store the profiling information, and then study the correlation and evaluation of I/O traffic with MPI communication using a cross-stack approach by INAM. Through experimental evaluations and use cases, we demonstrate novel benefits of our cross-stack communication analysis in real-time to detect bottlenecks and understand communication performance.