Chunking loops with non-uniform workloads

Task-parallel languages such as X10 implement dynamic lightweight task-parallel execution model, where programmers are encouraged to express the ideal parallelism in the program. Prior work has used loop chunking to extract useful parallelism from ideal. Traditional loop chunking techniques assume that iterations in the loop are of similar workload, or the behavior of the first few iterations can be used to predict the load in later iterations. However, in loops with non-uniform work distribution, such assumptions do not hold. This problem becomes more complicated in the presence of atomic blocks (critical sections).

In this paper, we propose a new optimization called deep-chunking that uses a mixed compile-time and runtime technique to chunk the iterations of the parallel-for-loops, based on the runtime workload of each iteration. We propose a parallel algorithm that is executed by individual threads to efficiently compute their respective chunks so that the overall execution time gets reduced. We prove that the algorithm is correct and is a 2-factor approximation. In addition to simple parallel-for-loops, the proposed deep-chunking can also handle loops with atomic blocks, which lead to exciting challenges. We have implemented deep-chunking in the X10 compiler and studied its performance on the benchmarks taken from IMSuite. We show that on an average, deep-chunking achieves 50.48%, 21.49%, 26.72%, 32.41%, and 28.84% better performance than un-chunked (same as work-stealing), cyclic-, block-, dynamic-, and guided-chunking versions of the code, respectively.