Parallel TCP flows are broadly used in the high performance distributed computing community to enhance network throughput, particularly for large data transfers. Previous research has studied the mechanism by which parallel TCP improves aggregate throughput, but there doesn't exist any practical mechanism to predict its throughput and its impact on the background traffic. In this work, we address how to predict parallel TCP throughput as a function of the number of flows, as well as how to predict the corresponding impact on cross traffic. To the best of our knowledge, we are the first to answer the following question on behalf of a user: what number of parallel flows will give the highest throughput with less than a p% impact on cross traffic? We term this the maximum nondisruptive throughput. We begin by studying the behavior of parallel TCP in simulation to help derive a model for predicting parallel TCP through-put and its impact on cross traffic. Combining this model with some previous findings we derive a simple, yet effective, online advisor. We evaluate our advisor through extensive simulations and wide-area experimentation.