The Tiny-Tasks Granularity Trade-Off: Balancing overhead vs. performance in parallel systems

TL;DR

This paper investigates the trade-off in parallel systems between task granularity and overhead, proposing models and experiments that optimize performance and stability when splitting jobs into tiny tasks.

Contribution

It introduces a four-parameter overhead model, analytical bounds, and approximations for tiny tasks in parallel systems, validated through extensive Spark experiments.

Findings

Tiny tasks improve system stability and performance.

Overhead modeling accurately predicts sojourn times.

Experimental results match analytical approximations well.

Abstract

Models of parallel processing systems typically assume that one has $l$ workers and jobs are split into an equal number of $k=l$ tasks. Splitting jobs into $k > l$ smaller tasks, i.e. using ``tiny tasks'', can yield performance and stability improvements because it reduces the variance in the amount of work assigned to each worker, but as $k$ increases, the overhead involved in scheduling and managing the tasks begins to overtake the performance benefit. We perform extensive experiments on the effects of task granularity on an Apache Spark cluster, and based on these, developed a four-parameter model for task and job overhead that, in simulation, produces sojourn time distributions that match those of the real system. We also present analytical results which illustrate how using tiny tasks improves the stability region of split-merge systems, and analytical bounds on the sojourn and waiting time distributions of both split-merge and single-queue fork-join systems with tiny tasks. Finally we combine the overhead model with the analytical models to produce an analytical approximation to the sojourn and waiting time distributions of systems with tiny tasks which include overhead. Though no longer strict analytical bounds, these approximations matched the Spark experimental results very well in both the split-merge and fork-join cases.