Efficient I/O is essential for good performance in data-intensive applications. Often, the file system is a substantial bottleneck on HPC systems, because CPU and memory technology has improved much more drastically in the last few decades than I/O technology.
Parallel I/O libraries such as MPI-IO, HDF5 and netCDF can help parallelize, aggregate and efficiently manage I/O operations. HDF5 and netCDF also have the benefit of using self-describing binary file formats that support complex data models and provide system portability. However, some simple guidelines can be used for almost any type of I/O on Oscar:
Try to aggregate small chunks of data into larger reads and writes.
For the GPFS file systems, reads and writes in multiples of 512KB
provide the highest bandwidth.
Avoid using ASCII representations of your data. They will usually
require much more space to store, and require conversion to/from
binary when reading/writing.
Avoid creating directory hierarchies with thousands or millions of
files in a directory. This causes a significant overhead in managing
While it may seem convenient to use a directory hierarchy for managing large sets of very small files, this causes severe performance problems due to the large amount of file metadata. A better approach might be to implement the data hierarchy inside a single HDF5 file using HDF5's grouping and dataset mechanisms. This single data file would exhibit better I/O performance and would also be more portable than the directory approach.