Throttling I/O Streams to Accelerate File-I/O Performance

To increase the scale and performance of scientific applications, scientists commonly distribute computation over multiple processors. Often without realizing it, file I/O is parallelized with the computation. An implication of this I/O parallelization is that multiple compute tasks are likely to concurrently access the I/O nodes of an HPC system. When a large number of I/O streams concurrently access an I/O node, I/O performance tends to degrade. In turn, this impacts application execution time. This paper presents experimental results that show that controlling the number of synchronous file-I/O streams that concurrently access an I/O node can enhance performance. We call this mechanism file-I/O stream throttling. The paper (1) describes this mechanism and demonstrates how it can be applied either at the application or system software layers, and (2) presents results of experiments driven by the cosmology application benchmark MADbench, executed on a variety of computing systems, that demonstrate the effectiveness of file-I/O stream throttling. The results imply that dynamic selection of the number of synchronous file-I/O streams that are allowed to access an I/O node can result in improved application performance. Note that the I/O pattern of MADbench resembles that of a large class of HPC applications