An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads

Abstract

Traditionally, distributed storage systems have relied upon the interfaces provided by OS kernels to interact with storage hardware. However, much research has shown that OSes impose serious overheads on every I/O operation, especially on high-performance storage and networking hardware. In this work, we provide a comprehensive evaluation of DAOS, a distributed storage system re-architected for modern hardware, and compare it with traditional storage stacks using scientific applications.

Connected work

Deepio

Citation

@inproceedings{logan2023evaluation,
  author = {Logan, Luke and Lofstead, Jay and Sun, Xian-He and Kougkas, Anthony},
  booktitle = {Proceedings of the 3rd Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems},
  title = {An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads},
  year = {2023},
  month = may,
  publisher = {ACM},
  volume = {},
  number = {},
  pages = {9--16},
  keywords = {Storage Architectures, I/O Benchmarking, High-Performance Computing, Data Movement Optimization},
  doi = {10.1145/3578353.3589542},
  url = {https://doi.org/10.1145/3578353.3589542},
}