Scalable tasking runtime with parallelized builders for explicit message passing architectures

The sequential task flow (STF) model introduces implicit data dependences to exploit task-based parallelism, simplifying programming but also introducing non-negligible runtime overhead. On emerging cache-less, explicit inter-core message passing (EMP) architectures, the long latency of memory access further amplifies the runtime overhead of the traditional STF model, resulting in unsatisfactory performance.

This paper addresses two main components in the STF tasking runtime. We uncover abundant concurrency in the task dependence graph (TDG) building process through three sufficient conditions, put forward PBH, a parallelized TDG building algorithm with helpers which mixes pipeline parallelism and data parallelism to overcome the TDG building bottleneck for fine-grained tasks. We also introduce a centralized, lock-less task scheduler, EMP-C, based on the EMP interface, and propose three optimizations. These two techniques are implemented and evaluated on a product processor with EMP support, i.e. SW26010. Experimental results show that compared to traditional techniques, PBH achieves an average speedup of 1.55 for fine-grained task workloads, and the EMP-C scheduler brings speedups as high as 1.52 and 2.38 for fine-grained and coarse-grained task workloads, respectively. And the combination of these two techniques significantly improves the granularity scalability of the runtime, reducing the minimum effective task granularity (METG) to 0.1 ms and achieving an order of magnitude decrease in some cases.