## 1 Introduction  
The unit-commitment (UC) problem aims at determining the optimal scheduling of generating units to minimize costs or maximize revenues while satisfying local and system-wide constraints [1]. In its deterministic form, UC still poses a challenge to operators and researchers due to the large sizes of the systems and the increasing modeling details necessary to represent the system operation. For instance, in the Brazilian case, the current practice is to set a limit of 2 h for the solution of the deterministic UC [2], while the Midcontinent Independent System Operator (MISO) sets a time limit of 20 min for its UC [3]. (Note that the Brazilian system and the MISO are different from a physical, as well as from a market-based, viewpoint, but the problems being solved in these two cases share the same classical concept of the UC.) Nonetheless, the growing presence of intermittent generation hasadded yet more difficulty to the problem, giving rise to what is called uncertain UC [4]. The latter is considerably harder to solve than its deterministic counterpart, and one of the reasons for its lack of adoption in the industry is precisely its computational burden: Large-scale uncertain UC takes a prohibitively long time to be solved. In this context, efficient solution methods for the uncertain UC that can take full advantage of the computational resources at hand are both desirable and necessary to help system operators cope with uncertain resources. In particular, to model the uncertainty arising from renewable sources, one of two approaches is generally employed: robust optimization or stochastic programming [4]. The latter is by far the most employed, both in its chance-constrained and recourse variants. In stochastic programs with recourse, uncertainty is, in general, represented by finite-many scenarios, and the problem is formulated either in a two-stage or multistage setting. In two-stage stochastic problems, the first-stage variables must be decided before uncertainty is revealed. Once the uncertain information becomes known, recourse actions are taken to best accommodate the first-stage decisions [5]. In stochastic hydrothermal unit-commitment (SHTUC) problems, the sources of uncertainties are related to renewable resources, spot prices, load, and equipment availability [1; 4]. The commitment decisions are usually modeled as first-stage variables, while dispatch decisions are the recourse actions (second-stage variables). Given the mixed-integer nature of commitment decisions, SHTUC problems in a two-stage formulation give rise to large-scale mixed-integer optimization models whose numerical solution by off-the-shelf solvers is often prohibitive due to time requirements or limited computing resources. Consequently, decomposition techniques must come into play [1; 4; 6; 7]. Benders decomposition (BD) and Lagrangian relaxation (LR) are the most used techniques to handle SHTUC problems. While the BD deals with the primal problem [8], LR is a dual procedure employed to compute the best lower bound for the SHTUC problem [7; 9]. Primal-recovery heuristics are employed to compute primal-feasible points, which are not, in general, optimal solutions. This is the main shortcoming of LR-based techniques. Decomposition techniques yield models that are amenable for parallelization [5]. A common strategy for solving problems simultaneously is to use a master/worker framework with pre-specified synchronization points [10], which we call synchronous computing (SYN). In this framework, the master chooses new iterates and sends them to workers, who, in turn, are responsible for solving one or more subproblems. Examples of SYN implementations for UC are given in [11; 12; 13; 14]. An aspect of SYN is that, at predetermined points of the algorithm, the master must wait for all workers to respond to resume the iterative process: the synchronization points. However, the times for workers to finish their respective tasks might vary significantly.