Section: New Results

Automatic Parallelization of Nonlinear Loops

Participants : Aravind Sukumaran-Rajam, Philippe Clauss.

Runtime code optimization and speculative execution are becoming increasingly prominent to leverage performance in the current multi- and many-core era. However, a wider and more efficient use of such techniques is mainly hampered by the prohibitive time overhead induced by centralized data race detection, dynamic code behavior modeling, and code generation. Most of the existing Thread Level Speculation (TLS) systems rely on naively slicing the target loops into chunks and trying to execute the chunks in parallel with the help of a centralized performance-penalizing verification module that takes care of data races. Due to the lack of a data dependence model, these speculative systems are not capable of doing advanced transformations, and, more importantly, the chances of rollback are high. The polyhedral model is a well- known mathematical model to analyze and optimize loop nests. The current state-of-art tools limit the application of the polyhedral model to static control codes. Thus, none of these tools can generally handle codes with while loops, indirect memory accesses, or pointers. Apollo (Automatic POLyhedral Loop Optimizer) is a framework that goes one step beyond and applies the polyhedral model dynamically by using TLS. Apollo can predict, at runtime, whether the codes are behaving linearly or not, and it applies polyhedral transformations on-the-fly.

Apollo has been extended to handle codes whose memory accesses and loop bounds are not necessarily linear [23] , [14] . The proposed extension consists of modeling memory addresses that are accessed either as "tubes" obtained through linear regression, or as ranges. More generally, this approach expands the applicability of the polyhedral model at runtime to a wider class of codes. Plugging together both linear and nonlinear accesses to the dependence prediction model enables the application of polyhedral loop optimizing transformations even for nonlinear code kernels while also allowing a low-cost speculation verification.

This work takes part of Aravind Sukumaran-Rajam's PhD thesis that has been defended November the 5th, 2015 [13] .