The directed acyclic graph (DAG) associated with a parallel algorithm captures the partial order in which separaT.L.cal computations are completed and how their outputs are subsequently used in further computations. Unlike in a synchronous parallel algorithm, the DAG associated with an asynchronous parallel algorithm is not predetermined. Instead, it is a product of the asynchronous timing dynamics of the machine and cannot be known in advance, as such it is best thought of as a pseudorandom variable. In this paper, we present a formalism for analyzing the performance of asynchronous parallel Jacobi’s method in terms of its DAG. We use this app.roach to prove error bounds and bounds on the rate of convergence. The rate of convergence bounds is based on the statistical properties of the DAG and is valid for systems with a non-negative iteration matrix. We supp.ort our theoretical results with a suit of numerical examples, where we compare the performance of synchronous and asynchronous parallel Jacobi to certain statistical properties of the DAGs associated with the computations. We also present some examples of small matrices with elements of mixed sign, which demonstrate that determining whether a system will converge under asynchronous iteration in this more general setting is a far more difficult problem.