Photonic reservoir computing uses recent advances in machine learning, and in particular the reservoir computing algorithm, to carry out complex computations optically. Experimental demonstrations with performance comparable to state of the art digital implementations have been reported. However, most experiments so far were based on sequential processing using time-multiplexing. Parallel architectures promise considerable speedup. Recently, a reservoir computing architecture based on frequency parallelism was proposed by our laboratory, and a preliminary demonstration was carried out using optical fibres. In this system the reservoir is linear and the nonlinearity is provided by readout photodiodes. Here, we study in simulation an implementation of this frequency parallel architecture on an InP chip using a generic integration platform. This would dramatically reduce the footprint and cost of the reservoir. The input signal is encoded by modulating the frequency comb produced by a mode locked laser with a repetition rate of 10GHz. The update rate of the input is 2.5GHz. The reservoir, an active cavity with a time delay of 0.4ns, contains a phase modulator which is driven by a 10GHz RF signal, and a semiconductor amplifier to compensate the losses in the cavity. Readout is carried out by measuring the intensity of individual frequency combs and linearly combining them. We performed time domain simulations on a standard channel equalization task. The simulation takes in to account the phase and amplitude noise of the laser source, and the amplifier noise. The power leakage between neighboring channels at the de-multiplexer is also included. To evaluate the system performance, noise is added as a global parameter on the input signal to assess the SNR requirements. Simulation results show that the laser phase noise is far more important that other types of noise, hence the laser source design/operation should be optimized to achieve low phase noise comb.