  Abstract machines for strong evaluation of the $\lambda$-calculus enter into
arguments and have a set of transitions for backtracking out of an evaluated
argument. We study a new abstract machine which avoids backtracking by
splitting the run of the machine in smaller jobs, one for argument, and that
jumps directly to the next job once one is finished.
  Usually, machines are also deterministic and implement deterministic
strategies. Here we weaken this aspect and consider a light form of
non-determinism, namely the diamond property, for both the machine and the
strategy. For the machine, this introduces a modular management of jobs,
parametric in a scheduling policy. We then show how to obtain various
strategies, among which leftmost-outermost evaluation.
