We just received the good news that Hazem’s article “ Reducing the Complexity of Dataflow Graphs using Slack-based Merging” has been accepted for publication in ACM Transactions on Design Automation of Electronic Systems (TODAES). The article addresses an important problem when working with synchronous data-flow (SDF) graphs, namely that the size of the graph explodes when transforming it to its equivalent homogeneous (HSDF) representation, which prevents any design or analysis algorithms requiring this transformation as a first step from scaling to larger graphs. In the scope of Hazem’s work, this has caused problems when converting an SDF graph into a set of independent periodic real-time tasks.
This article proposes a heuristic algorithm to reduce the size of the resulting HSDF graph prior to analysis by merging actors in the graph, thereby speeding up analysis algorithms using the resulting graph. Three key properties of the algorithm are: 1) it cannot violate the latency or throughput requirements of the original graph, 2) it cannot cause deadlock in the resulting merged graph, and 3) only HSDF actors corresponding to firings of the same SDF actor can be merged to enable the resulting merged graph to be efficiently used by mapping algorithms. The behavior of the algorithm is evaluated with applications from the SDF3 benchmark suite and it is compared to results of an optimal exhaustive merging algorithm for smaller graphs.