TX-Digital Twin: Visualizing Supercomputer GPU Performance Data Stream

TL;DR

This paper introduces an enhanced 3D visualization tool, TX-Digital Twin, for supercomputer GPU performance data, integrating new GPU metrics with minimal performance impact.

Contribution

We extend the TX-Digital Twin by integrating GPU metrics visualization, improving supercomputer monitoring with optimized rendering techniques.

Findings

Successfully integrated GPU metrics like memory, temperature, and power into the visualization.

Maintained minimal performance overhead despite added visualization complexity.

Enhanced monitoring capabilities for GPU-accelerated supercomputers.

Abstract

Supercomputers are complex, dynamic systems that serve thousands of users and are built with thousands of compute nodes. Due to the vast amounts of system and performance data needed to accurately capture their status, supercomputers require complex methods to monitor, maintain, and optimize. Data visualization is a powerful technique for overseeing these large streams of data in an easily interpretable way. The MIT Lincoln Laboratory Supercomputing Center (LLSC) enables effective monitoring through combining 3D gaming technology with compound data streams in the TX-Digital Twin, a 3D simulation of the supercomputer. The TX-Digital Twin offers both live and historical data, in visual and text formats, and tracks a multitude of revealing performance metrics. Recent increasing interest in GPU-accelerated computing has driven a need for monitoring and maintenance of GPU-accelerated resources in supercomputers. In this paper, we build on our previous solution by integrating the visualization of additional GPU metrics, such as GPU memory usage, temperature, and power draw, into the TX-Digital Twin. Using techniques in draw call optimization, we add clear and effective displays of the new metrics while keeping the effects on performance minimal.