In this paper, we show that in a parallel processing system, if a partial order is induced among the local states visited by a node, then synchronization cost can be eliminated. As a result of this partial order, a DAG is induced among the global states. Specifically, we show that in such systems, correctness is preserved even if the nodes execute asynchronously and read old information of other nodes. We present two variations for inducing DAGs -- \textit{DAG-inducing problems}, where the problem definition itself induces a DAG, and \textit{DAG-inducing algorithms}, where a DAG is induced by the algorithm. We demonstrate that the dominant clique (DC) problem and shortest path (SP) problem are DAG-inducing problems. Among these, DC allows self-stabilization, whereas the algorithm that we present for SP does not. We demonstrate that maximal matching (MM) is not a DAG-inducing problem. However, a DAG-inducing algorithm can be developed for it. The algorithm for MM allows self-stabilization. This algorithm converges in $2n$ moves and does not require a synchronous environment, which is an improvement over the existing algorithms in the literature. The algorithm for DC converges in $2m$ moves, and the algorithm for SP converges in $\mathcal{D}$ rounds. ($n$ is the number of nodes and $m$ is the number of edges in the input graph, and $\mathcal{D}$ is its diameter.) We also note that DAG-inducing problems are more general than, and encapsulate, lattice linear problems (Garg, SPAA 2020). Similarly, DAG-inducing algorithms encapsulate lattice linear algorithms (Gupta and Kulkarni, SSS 2022). We also show that a partial order induced among the local states visited by a node, as discussed above, is a necessary and sufficient condition to allow asynchrony.

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