  This paper is aimed at designing efficient parallel matrix-product algorithms
for heterogeneous master-worker platforms. While matrix-product is
well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm
and ScaLAPACK outer product algorithm), there are three key hypotheses that
render our work original and innovative:
  - Centralized data. We assume that all matrix files originate from, and must
be returned to, the master.
  - Heterogeneous star-shaped platforms. We target fully heterogeneous
platforms, where computational resources have different computing powers.
  - Limited memory. Because we investigate the parallelization of large
problems, we cannot assume that full matrix panels can be stored in the worker
memories and re-used for subsequent updates (as in ScaLAPACK).
  We have devised efficient algorithms for resource selection (deciding which
workers to enroll) and communication ordering (both for input and result
messages), and we report a set of numerical experiments on various platforms at
Ecole Normale Superieure de Lyon and the University of Tennessee. However, we
point out that in this first version of the report, experiments are limited to
homogeneous platforms.
