Function Merging by Sequence Alignment

Abstract

Resource-constrained devices for embedded systems are becoming increasingly important. In such systems, memory is highly restrictive, making code size in most cases even more important than performance. Compared to more traditional platforms, memory is a larger part of the cost and code occupies much of it. Despite that, compilers make little effort to reduce code size. One key technique attempts to merge the bodies of similar functions. However, production compilers only apply this optimization to identical functions, while research compilers improve on that by merging the few functions with identical control-flow graphs and signatures. Overall, existing solutions are insufficient and we end up having to either increase cost by adding more memory or remove functionality from programs. We introduce a novel technique that can merge arbitrary functions through sequence alignment, a bioinformatics algorithm for identifying regions of similarity between sequences. We combine this technique with an intelligent exploration mechanism to direct the search towards the most promising function pairs. Our approach is more than 2.4x better than the state-of-the-art, reducing code size by up to 25%, with an overall average of 6%, while introducing an average compilation-time overhead of only 15%. When aided by profiling information, this optimization can be deployed without any significant impact on the performance of the generated code.

Early DSE and Automatic Generation of Coarse Grained Merged Accelerators

“…For the second parameter in f1, we proceed with the remaining parameters in f2. [22] has shown this parameter merging approach to be very effective by intentionally modifying parameter merging taking into account the function body, and we observe similar area benefits in exploiting parameter merging of about 7%.…”

“…FMSA and SalSSa [21,22] were recently introduced as LLVM [14] compilation transformations that target code reduction for embedded devices. Work prior to FMSA is only able to merge equal functions whereas FMSA is able to merge functions with different argument lists, returned values and references as well as differing control flow.…”

“…Finally, neither FMSA nor other function merging approaches have been used for accelerator merging. Both the function merging and the Early-DSE related-work [11,21,22] demonstrate their results on the SPEC CPU2006 suite, and we include these applications in this paper for comparison.…”

“…Once the merge is performed, we can apply the models described in Section 4.1 on the input functions and the merged function to determine with high precision whether the merge is profitable. In this example we discuss how merging is being performed by following the methodology in [22] with some differences that make Function Merging amenable for accelerator merging and system design instead of code compression, which is the focus of the Function Merging related work [22] [21].…”

“…Function Merging starts by merging parameters depending on their types, not depending on their names. A direct approach is being taken in [22] where for each parameter, in declaration order, in the first function, a parameter of the same type is being searched, in declaration order, in the second function. If a match is found, we proceed with trying to match new parameters and if not, the currently analyzed parameter in the first function will not be merged.…”