This paper addresses the problem of orchestrating and scheduling
parallelism at multiple levels of granularity on heterogeneous
multicore processors. We present policies and mechanisms for adaptive
exploitation and scheduling of multiple layers of parallelism on the
Cell Broadband Engine. Our policies combine event-driven task
scheduling with malleable loop-level parallelism, which is exposed
from the runtime system whenever task-level parallelism leaves cores
idle. We present a runtime system for scheduling applications with
layered parallelism on Cell and investigate its potential with RAxML,
a computational biology application which infers large phylogenetic
trees, using the Maximum Likelihood (ML) method. Our experiments show
that the Cell benefits significantly from dynamic parallelization
methods, that selectively exploit the layers of parallelism in the
system, in response to workload characteristics. Our runtime
environment outperforms naive parallelization and scheduling based on
MPI and Linux by up to a factor of 2.6. We are able to execute RAxML
on one Cell four times faster than on a dual-processor system with
Hyperthreaded Xeon processors, and 5--10\% faster than on a
single-processor system with a dual-core, quad-thread IBM Power5
processor