Proc. 2nd Int'l Conference of the ACPC, Gmunden, Austria, October 4-6, 1993.

We explore time and space optimization problems involved in the mapping of parallel algorithms onto a honeycomb architecture. When a well-known mapping is used, mapped algorithms generally exhibit execution slow-down and required too large area. We design several optimization techniques and enhance the mapping process. Experimental results show more than 50% saving in processor resources and 30% saving in execution time, on average. Since computing performances are improved, also the applicability of the honeycomb architecture is wider.