Symbolic Multi-Level Loop Mapping of Loop Programs for Massively Parallel Processor Arrays

摘要

Today's MPSoCs (multiprocessor systems-on-chip) have brought up massively parallel processor array accelerators that may achieve a high computational efficiency by exploiting multiple levels of parallelism and different memory hierarchies. Such parallel processor arrays are perfect targets, particularly for the acceleration of nested loop programs due to their regular and massively parallel nature. However, existing loop parallelization techniques are often unable to exploit multiple levels of parallelism and are either I/O or memory bounded. Furthermore, if the number of available processing elements becomes only known at runtime-as in adaptive systems-static approaches fail. In this article, we solve some of these problems by proposing a hybrid compile/runtime multi-level symbolic parallelization technique that is able to: (a) exploit multiple levels of parallelism as well as (b) different memory hierarchies, and (c) to match the I/O or memory capabilities of the target architecture for scenarios where the number of available processing elements is only known at runtime. Our proposed technique consists of two compile-time transformations: (a) symbolic hierarchical tiling followed by (b) symbolic multi-level scheduling. The tiling levels scheduled in parallel exploit different levels of parallelism, whereas the sequential one, different memory hierarchies. Furthermore, by tuning the size of the tiles on the individual levels, a tradeoff between the necessary I/O-bandwidth and memory is possible, which facilitates obeying resource constraints. The resulting schedules are symbolic with respect to the problem size and tile sizes. Thus, the number of processing elements to map onto does not need to be known at compile time. At runtime, when the number of available processors becomes known, a simple prologue chooses a feasible schedule with respect to I/O and memory constraints that is latency-optimal for the chosen tile size. In summary, our approach determines the set of feasible, latency-optimal symbolic loop schedule candidates at compile time, from which one is dynamically selected at runtime. This approach exploits multiple levels of parallelism, is independent of the problem size of the loop nest, and thereby avoids any expensive re-compilation at runtime. This is particularly important for low cost and memory-scarce embedded MPSoC platforms that may not afford to host a just-in-time compiler.