We present distributed power control algorithms for a wireless peer-to-peer net- work with multiple channels per user. Users exchange "price" signals that indicate the negative effect of interference at the receivers in each channel. Given this set of prices, each transmitter chooses a power allocation across channels to maximize its net benefit (utility minus cost), subject to a total power constraint. We consider two specific algorithms for power and price updates, and establish global convergence for both algorithms to the unique globally optimal power allocation for a class of concave user utility functions. When the utility functions represent achievable rates, global convergence is not guaranteed; however, we show numerically that the proposed power control algorithms achieve much better performance than iterative water-filling, in which users maximize their own rates without exchanging price information.