<?tight?>Fork-join systems play a pivotal role in the analysis of distributed systems, telecommunication infrastructures, and storage systems. In this article, we consider a fork-join system consisting of K parallel servers, each of which works on one of the K tasks that form each job. The system allocates a fixed amount of computational resources among the K servers, hence determining their service speed. The goal of this article is that of studying the resource allocation policies among the servers. We assume that the queueing disciplines of the fork- and join-queues are First Come First Served. At each epoch, at most K tasks are in service while the others wait in the fork-queues. We propose an algorithm with a very simple implementation that allocates the computational resources in a way that aims at minimizing the join-queue lengths, and hence at reducing<?brk?> the expected job service time. We study its performance in saturation and under exponential service time. The model has an elegant closed-form stationary distribution. Moreover, we provide an algorithm to numerically or symbolically derive the marginal probabilities for the join-queue lengths. Therefore, the expressions for the expected join-queue length and the expected response time under immediate join can be derived. Finally, we compare the performance of the proposed resource allocation algorithm with that of other strategies.