Accelerating relax-ordered task-parallel workloads using multi-level dependency checking

Work-efficient task-parallel algorithms enforce ordered execution of tasks using priority schedulers. These algorithms suffer from limited parallelism due to data movement and synchronization bottlenecks. State-of-the-art priority schedulers relax the ordering of tasks to avoid false dependencies generated by strict queuing constraints, thus unlocking task parallelism. However, relaxing task dependencies results in shared data races among cores that lead to redundant task computations in concurrently executing threads. Although static algorithm optimizations have been shown to reduce redundant work, they do not exploit the tradeoff between parallelism and work efficiency that is only exposed during runtime. This paper proposes a task dependency checking mechanism that dynamically tracks the monotonic property of parent-child relationships across multiple levels from any given task. Since shared memory writes are known to be slower than concurrent reads, the multi-level checks effectively detect task dependency races to prune redundant tasks. Evaluation of relax-ordered algorithms on a 40-core Intel Xeon multicore shows an average of 44% performance improvement over the Galois obim scheduler.