The Potential of Synergistic Static, Dynamic and Speculative Loop Nest Optimizations for Automatic Parallelization

TL;DR

This paper explores combining static, dynamic, and speculative loop nest optimizations within the polyhedral framework to enhance automatic parallelization, addressing complex challenges in scalable many-core processor execution.

Contribution

It introduces a novel approach to integrate dynamic and speculative techniques into polyhedral loop transformations for improved parallelization.

Findings

Demonstrates the potential of hybrid static-dynamic schemes in loop optimization.

Shows how dynamic information can be embedded into affine transformation spaces.

Uses real-world benchmarks to validate the proposed techniques.

Abstract

Research in automatic parallelization of loop-centric programs started with static analysis, then broadened its arsenal to include dynamic inspection-execution and speculative execution, the best results involving hybrid static-dynamic schemes. Beyond the detection of parallelism in a sequential program, scalable parallelization on many-core processors involves hard and interesting parallelism adaptation and mapping challenges. These challenges include tailoring data locality to the memory hierarchy, structuring independent tasks hierarchically to exploit multiple levels of parallelism, tuning the synchronization grain, balancing the execution load, decoupling the execution into thread-level pipelines, and leveraging heterogeneous hardware with specialized accelerators. The polyhedral framework allows to model, construct and apply very complex loop nest transformations addressing most of the parallelism adaptation and mapping challenges. But apart from hardware-specific, back-end oriented transformations (if-conversion, trace scheduling, value prediction), loop nest optimization has essentially ignored dynamic and speculative techniques. Research in polyhedral compilation recently reached a significant milestone towards the support of dynamic, data-dependent control flow. This opens a large avenue for blending dynamic analyses and speculative techniques with advanced loop nest optimizations. Selecting real-world examples from SPEC benchmarks and numerical kernels, we make a case for the design of synergistic static, dynamic and speculative loop transformation techniques. We also sketch the embedding of dynamic information, including speculative assumptions, in the heart of affine transformation search spaces.