Abstract: We study online scheduling of parallel jobs on $m$ identical processors to minimize maximum flow time. Each arriving job is represented by a directed acyclic graph (DAG) whose vertices are unit-time subjobs and whose edges specify precedence constraints. We consider non-clairvoyant algorithms: the DAG is not known when a job arrives, and each subjob is revealed only when it becomes ready. Agrawal, Moseley, Newman, and Pruhs (SPAA 2024) showed that First-In-First-Out (FIFO) has competitive ratio $Ω(\log m)$ even when every job is an out-tree. They also proved that FIFO is $O(\log m)$-competitive in several natural settings and asked whether this guarantee extends to general instances. More broadly, they asked whether any non-clairvoyant algorithm can be $O(1)$-competitive. We answer both questions in the negative by proving a lower bound of $Ω(\min\{m,\mathrm{OPT}\})$ for every non-clairvoyant online algorithm, where $\mathrm{OPT}$ is the maximum flow time of an optimal offline schedule. In particular, every non-clairvoyant algorithm has competitive ratio $Ω(m)$ on some instance with $\mathrm{OPT} \ge m$. The lower bound holds even when every job is an out-forest. We complement these lower bounds with an asymptotically optimal non-clairvoyant algorithm. FIFO is $O(m)$-competitive, but can have competitive ratio $Ω(m)$ even when $\mathrm{OPT}=O(1)$. For instances with small $\mathrm{OPT}$, we design a Small-Frontier-First algorithm that is $O(\mathrm{OPT})$-competitive. Combining the two algorithms yields an $O(\min\{m,\mathrm{OPT}\})$-competitive non-clairvoyant algorithm.