Title
Deploying large fixed file datasets with SquashFS and Singularity
Abstract
Shared high-performance computing (HPC) platforms, such as those provided by XSEDE and Compute Canada, enable researchers to carry out large-scale computational experiments at a fraction of the cost of the cloud. Most systems require the use of distributed filesystems (e.g. Lustre) for providing a highly multi-user, large capacity storage environment. These suffer performance penalties as the number of files increases due to network contention and metadata performance. We demonstrate how a combination of two technologies, Singularity and SquashFS, can help developers, integrators, architects, and scientists deploy large datasets (O(10M) files) on these shared systems with minimal performance limitations. The proposed integration enables more efficient access and indexing than normal file-based dataset installations, while providing transparent file access to users and processes. Furthermore, the approach does not require administrative privileges on the target system. While the examples studied here have been taken from the field of neuroimaging, the technologies adopted are not specific to that field. Currently, this solution is limited to read-only datasets. We propose the adoption of this technology for the consumption and dissemination of community datasets across shared computing resources.
Year
DOI
Venue
2020
10.1145/3311790.3401776
PEARC '20: Practice and Experience in Advanced Research Computing Portland OR USA July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-6689-2
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Rioux Pierre100.34
Kiar Gregory200.34
Hutton Alexandre300.34
Alan C. Evans43045574.95
Shawn T. Brown5168.10