Improving I/O Performance of Applications through Compiler-Directed Code Restructuring

Ever-increasing complexity of large-scale applications and continuous increases in sizes of the data they process make the problem of maximizing performance of such applications a very challenging task. In particular, many challenging applications from the domains of astrophysics, medicine, biology, computational chemistry, and materials science are extremely data intensive. Such applications typically use a disk system to store and later retrieve their large data sets, and consequently, their disk performance is a critical concern. Unfortunately, while disk density has significantly improved over the last couple of decades, disk access latencies have not. As a result, I/O is increasingly becoming a bottleneck for data-intensive applications, and has to be addressed at the software level if we want to extract the maximum performance from modern computer architectures.

This paper presents a compiler-directed code restructuring scheme for improving the I/O performance of data-intensive scientific applications. The proposed approach improves I/O performance by reducing the number of disk accesses through a new concept called disk reuse maximization. In this context, disk reuse refers to reusing the data in a given set of disks as much as possible before moving to other disks. Our compiler-based approach restructures application code, with the help of a polyhedral tool, such that disk reuse is maximized to the extent allowed by intrinsic data dependencies in the application code. The proposed optimization can be applied to each loop nest individually or to the entire application code. The experiments show that the average I/O improvements brought by the loop nest based version of our approach are 9.0% and 2.7%, over the original application codes and the codes optimized using conventional schemes, respectively. Further, the average improvements obtained when our approach is applied to the entire application code are 15.0% and 13.5%, over the original application codes and the codes optimized using conventional schemes, respectively. This paper also discusses how careful file layout selection helps to improve our performance gains, and how our proposed approach can be extended to work with parallel applications.