pith. sign in

arxiv: 1509.05492 · v2 · pith:FHRKEO25new · submitted 2015-09-18 · 💻 cs.DC

Challenges and Considerations for Utilizing Burst Buffers in High-Performance Computing

classification 💻 cs.DC
keywords burstbufferscaseschallengescomputingmemorydatahigh-performance
0
0 comments X
read the original abstract

As high-performance computing (HPC) moves into the exascale era, computer scientists and engineers must find innovative ways of transferring and processing unprecedented amounts of data. As the scale and complexity of the applications running on these machines increases, the cost of their interactions and data exchanges (in terms of latency, energy, runtime, etc.) can increase exponentially. In order to address I/O coordination and communication issues, computing vendors are developing an intermediate layer between compute nodes and the parallel file system composed of different types of memory (NVRAM, DRAM, SSD). These large scale memory appliances are being called 'burst buffers.' In this paper, we envision advanced memory at various levels of HPC hardware and derive potential use cases for how to take advantage of it. We then present the challenges and issues that arise when utilizing burst buffers in next-generation supercomputers and map the challenges to the use cases. Lastly, we discuss the emerging state-of-the-art burst buffer solutions that are expected to become available by the end of the year in new HPC systems and which use cases these implementations may satisfy.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Rethinking Burst Buffer Optimization: Enabling Layout Heterogeneity via Hybrid Analysis and LLM Guidance

    cs.DC 2026-06 unverdicted novelty 4.0

    Proteus combines static code analysis, runtime signals, and LLM guidance to select burst-buffer data layouts, claiming 91.3% decision accuracy and up to 3.24x/2.9x speedups on write- and metadata-intensive HPC workloads.