The model of malleable task (MT) was introduced some years ago and has been proved to be an efficient way for implementing parallel applications. It considers a target application at a larger level of granularity than in other models (corresponding typically to numerical routines) where the tasks can themselves be executed in parallel. Clusters of SMP (symmetric Multi-Processors) are a cost effective alternative to parallel supercomputers. Such hierarchical clusters are parallel systems made from m SMP composed each by k identical processors. They are more and more popular, however, designing efficient software that take full advantage of such systems remains difficult. This work describes a 2-2/k approximation algorithm for scheduling a set of independent malleable tasks for the minimization of the parallel execution time, where $k$ is a power of 2 (k > 2). For k=2, a special treatment leads to the bound of 3/2 which is the best known for non hierarchical tasks. The algorithm presented here is a fully polynomial approximation scheme running in O(nmk) time.