We look into the design of a parallel adaptive algorithm embedded in a master-slave scheme. The adaptive algorithm under study selects online and in parallel for each slave-node one algorithm from a portfolio. Indeed, many open questions still arise when designing an online distributed strategy that attributes optimally algorithms to distribute resources. We suggest to analyze the relevance of existing sequential adaptive strategies related to multi-armed bandits to the master-slave distributed framework. In particular, the comprehensive experimental study focuses on the gain of computing power, the adaptive ability of selection strategies, and the communication cost of the parallel system. In fact, we propose an adaptive batch mode in which a sequence of algorithms is submitted to each slave computing node to face a possibly high communication cost.