Sparse matrix vector multiplication (SpMV) is an important kernel in many applications and is often the major performance bottleneck. The storage format of sparse matrices critically affects the performance of SpMV. Although there have been previous studies on selecting the appropriate format for a given matrix, they have ignored the influence of runtime prediction overhead and format conversion overhead. For many common uses of SpMV, such overhead is part of the execution times and may outweigh the benefits of new formats. Ignoring them makes the predictions from previous solutions frequently suboptimal and sometimes inferior. On the other hand, the overhead is difficult to consider, as it, along with the benefits of having a new format, varies from matrix to matrix, and from application to application. This work proposes a solution. It first explores the pros and cons of various possible treatments to the overhead in the format selection problem. It then presents an explicit approach which involves several regression models for capturing the influence of the overhead and benefits of format conversions on the overall program performance. It proposes a two-stage lazy-and-light scheme to help control the risks in the format predictions and at the same time maximize the overall format conversion benefits. Experiments show that the technique outperforms previous techniques significantly. It improves the overall performance of applications by 1.14X to 1.43X, significantly larger than the 0.82X to 1.24X upperbound speedups overhead-oblivious methods could give.

Feedback-driven optimization (FDO) is an important component in mainstream compilers. By allowing the compiler to reoptimize the program based on some profiles of the program's dynamic behaviors, it often enhances the quality of the generated code substantially. A barrier for using FDO is that it often requires many training runs to collect enough profiles to amortize the sensitivity of program optimizations to program input changes. Various sampling techniques have been explored to alleviate this time-consuming process. However, the lowered profile accuracy caused by sampling often hurts the benefits of FDO. This article gives the first systematic study in how sampling rates affect the accuracy of collected profiles and how the accuracy correlates with the usefulness of the profile for modern FDO. Studying basic block and edge profiles for FDO in two mature compilers reveals several counterintuitive observations, one of which is that profiling accuracy does not strongly correlate with the benefits of the FDO. A detailed analysis identifies three types of sampling-caused errors that critically impair the quality of the profiles for FDO. It then introduces a simple way to rectify profiles based on the findings. Experiments demonstrate that the simple rectification fixes most of those critical errors in sampled profiles and significantly enhances the effectiveness of FDO.

Function versioning is an approach to addressing input-sensitivity of program optimizations. A major side effect of it is notable code size increase, which has been hindering its broad applications to large code bases and space-stringent environments. In this paper, we initiate a systematic exploration into the problem, providing answers to some fundamental questions: Given a space constraint, to which function we should apply versioning? How many versions of a function should we include in the final executable? Is the optimal selection feasible to do in polynomial time? This study proves selecting the best set of versions under a space constraint is NP-complete and proposes a heuristic algorithm named CHoGS which yields near optimal results in quadratic time. We implement the algorithm and conduct experiments through the IBM XL compilers. We observe significant performance enhancement with only slight code size increase; the results from CHoGS show factors of higher space efficiency than those from traditional hotness-based methods.