Dissecting sequential programs for parallelization—An approach based on computational units

Abstract

Summary When trying to parallelize a sequential program, programmers routinely struggle during the first step: finding out which code sections can be made to run in parallel. While identifying such code sections, most of the current parallelism discovery techniques focus on specific language constructs. In contrast, we propose to concentrate on the computations performed by a program. In our approach, a program is treated as a collection of computations communicating with one another using a number of variables. Each computation is represented as a computational unit (CU). A CU contains the inputs and outputs of a computation, and the three phases of a computation are read, compute, and write. Based on the notion of CU, which ensures that the read phase executes before the write phase, we present a unified framework to identify both loop parallelism and task parallelism in sequential programs. We conducted a range of experiments on 23 applications from four different benchmark suites. Our approach accurately identified the parallelization opportunities in benchmark applications based on comparison with their parallel versions. We have also parallelized the opportunities identified by our approach that were not implemented in the parallel versions of the benchmarks and reported the speedup.

Improving Loop Parallelization by a Combination of Static and Dynamic Analyses in HLS

“…All of these user-directed approaches require the user to refactor the code and to actively select the optimizations to be applied, or even implement them themselves. Selecting which optimizations can be applied where in the code, without endangering correctness, is a challenge for developers and requires expertise [4,17]. Additionally, estimating whether an optimization potentially yields a benefit requires a lot of knowledge in the field of HLS.…”

High‐level parallel programming in a heterogeneous world

“…The problem of parallelizing legacy code starts with the selection of what parts of the code can be run in parallel. In their contribution, Atre et al propose a solution to this problem based on the concept of Computational Unit, helping to identify both task‐ and loop‐parallelization opportunities. The experimental results, including several benchmark suites, demonstrate the usefulness of their method.…”