arXiv subjects

Brett Bode

Publications and source records attributed to Brett Bode.

3 recordsLinked to original sources

arXiv2025

Understanding the Landscape of Ampere GPU Memory Errors

Graphics Processing Units (GPUs) have become a de facto solution for accelerating high-performance computing (HPC) applications. Understanding their memory error behavior is an essential step toward achieving efficient and reliable HPC systems. In this work, we present a large-scale cross-supercomputer study to characterize GPU memory reliability, covering three supercomputers - Delta, Polaris, and Perlmutter - all equipped with NVIDIA A100 GPUs. We examine error logs spanning 67.77 million GPU device-hours across 10,693 GPUs. We compare error rates and mean-time-between-errors (MTBE) and highlight both shared and distinct error characteristics among these three systems. Based on these observations and analyses, we discuss the implications and lessons learned, focusing on the reliable operation of supercomputers, the choice of checkpointing interval, and the comparison of reliability characteristics with those of previous-generation GPUs. Our characterization study provides valuable insights into fault-tolerant HPC system design and operation, enabling more efficient execution of HPC applications.

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arXiv2025

Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUs

This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years of operational data (11.7 million GPU hours) on GPU errors. Our major findings include: (i) H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE for memory errors, (ii) The GPU memory error-recovery mechanisms on H100 GPUs are insufficient to handle the increased memory capacity, (iii) H100 GPUs demonstrate significantly improved GPU hardware resilience over A100 GPUs with respect to critical hardware components, (iv) GPU errors on both A100 and H100 GPUs frequently result in job failures due to the lack of robust recovery mechanisms at the application level, and (v) We project the impact of GPU node availability on larger-scales and find that significant overprovisioning of 5% is necessary to handle GPU failures.

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arXiv2017

Workload Analysis of Blue Waters

Blue Waters is a Petascale-level supercomputer whose mission is to enable the national scientific and research community to solve "grand challenge" problems that are orders of magnitude more complex than can be carried out on other high performance computing systems. Given the important and unique role that Blue Waters plays in the U.S. research portfolio, it is important to have a detailed understanding of its workload in order to guide performance optimization both at the software and system configuration level as well as inform architectural balance tradeoffs. Furthermore, understanding the computing requirements of the Blue Water's workload (memory access, IO, communication, etc.), which is comprised of some of the most computationally demanding scientific problems, will help drive changes in future computing architectures, especially at the leading edge. With this objective in mind, the project team carried out a detailed workload analysis of Blue Waters.

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