Lecture Notes in Computational Science and Engineering - Bioserver

10 Gudula Rünger
The hierarchical radiosity method is one of the most challenging problems
concerning irregularity of data access and dynamic behavior of load and
communication. Communication is required for remote data access as well as
for load balancing between nodes. Task pool teams provide an appropriate
tool for handling load imbalances on SMP nodes as well as on entire cluster
platforms. Load balance between SMP nodes is realized explicitly offering
additional possibilities for optimizing the expensive redistribution of data or
migration of tasks.
The programming model of task pool teams also has been used to realize
different parallel variants of the simulation of diffusion processes using random
Sierpinski Carpets [9,20]. In this application the task pool team approach
is especially suitable to efficiently realize the boundary update phase of the
algorithm which is necessary after time steps. The first variant has a synchronous
parallel update phase which exploits the specific communication thread
provided by task pool teams; the exchange of boundary information is started
by collective communication of the worker threads and the communication
thread is responsible for the processing of the data received. The second implementation
variant realizes an asynchronous boundary-update using only
the task pool team’s communication mechanism. Measurements of the execution
time on a Xeon-Cluster with SCI network and a Beowulf cluster with
Fast-Ethernet show that the synchronous approach is slightly better.
The central aim of using task pool teams is to support communication
protocols that are suitable for dynamic and asynchronous communication,
but which do not rely on specific attributes of the MPI library such as thread
safety [17]. Thus, the implementation provides great flexibility concerning the
underlying communication libraries, the parallel platform used, and specific
application algorithms.
3 Data and communication management
for adaptive algorithms
Adaptive algorithms adjust their behavior according to the specified properties
of the result to be computed. This includes an adaption of computation
and/or data and is usual guided by a required precision of a solution. A typical
example is the adaptive finite element method for solving partial differential
equations numerically.
The finite element method uses a discretization of the physical space into
a mesh of finite elements. The numerical solution of the given problem is
then computed iteratively on the mesh. The resulting numerical algorithm
has mainly local dependencies since only values stored for neighboring mesh
cells are used for the computation. Because of these local dependencies, a
useful parallel implementation strategy exploits a decomposition of the mesh
into parts of equal size in order to obtain load balance [3]. Communication is