Abstract
Efficient execution of parallel scientific applications requires high-performance storage systems designed to meet their I/O requirements. Most high-performance I/O intensive applications access multiple layers of the storage stack during their disk operations. A typical I/O request from these applications may include accesses to high-level libraries such as MPI I/O, executing on clustered parallel file systems like PVFS2, which are in turn supported by native file systems like Linux. In order to design and implement parallel applications that exercise this I/O stack, it is important to understand the flow of I/O calls through the entire storage system. Such understanding helps in identifying the potential performance and power bottlenecks in different layers of the storage hierarchy. To trace the execution of the I/O calls and to understand the complex interactions of multiple user-libraries and file systems, we propose an automatic code instrumentation technique, which enables us to collect detailed statistics of the I/O stack. Our proposed I/O tracing tool traces the flow of I/O calls across different layers of an I/O stack, and can be configured to work with different file systems and user-libraries. It also analyzes the collected information to generate output in terms of different user-specified metrics of interest.
This work is supported in part by NSF grants 0937949, 0621402, 0724599, 0821527, 0833126, 0720749, 0621443, 0724599, and 0833131 and DOE grants DEAC02-06CH11357, DE-FG02-08ER25848, DE-SC0002156, and DESC0001283.
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Kim, S.J. et al. (2010). Automated Tracing of I/O Stack. In: Keller, R., Gabriel, E., Resch, M., Dongarra, J. (eds) Recent Advances in the Message Passing Interface. EuroMPI 2010. Lecture Notes in Computer Science, vol 6305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15646-5_8
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