Incremental Pattern Matching in Graph-Based State Space Exploration

Graph pattern matching is among the most costly operations in any graph transformation system. Incremental pattern matching aims at reducing this cost by incrementally updating, as opposed to totally recalculating, the possible matches of rules in the graph grammar at each step of the transformation. In this paper an implementation of one such algorithm is discussed with respect to the GROOVE toolset, with a special emphasis put on state space exploration. Specifically, we shall discuss exploration strategies that could better harness the positive aspects of incremental pattern matching in order to gain better performance

Latency Minimization for Synchronous Data Flow Graphs

Synchronous Data Flow Graphs (SDFGs) are a very useful means for modeling and analyzing streaming applications. Some performance indicators, such as throughput, have been studied before. Although throughput is a very useful performance indicator for concurrent real-time applications, another important metric is latency. Especially for applications such as video conferencing, telephony and games, latency beyond a certain limit cannot be tolerated. This paper proposes an algorithm to determine the minimal achievable latency, providing an execution scheme for executing an SDFG with this latency. In addition, a heuristic is proposed for optimizing latency under a throughput constraint. Experimental results show that latency computations are efficient despite the theoretical complexity of the problem. Substantial latency improvements are obtained, of 24-54 % on average for a synthetic benchmark of 900 models, and up to 37 % for

A parameterized compositional multi-dimensional multiple-choice knapsack heuristic for CMP run-time management

Modern embedded systems typically contain chip-multiprocessors (CMPs) and support a variety of applications. Applications may run concurrently and can be started and stopped over time. Each application may typically have multiple feasible configurations, trading off quality aspects (energy consumption, audio-visual quality) with resource usage for various types of resources. Overall system quality needs to be guaranteed and optimized at all times. This leads to the need for a run-time management solution that selects an appropriate system configuration from all the application configurations of active applications. This run-time management problem can be phrased as a multi-dimensional multiplechoice knapsack (MMKP) problem. We present a compositional heuristic to solve MMKP, that due to the compositionality is better suited to CMP run-time management than existing heuristics that are all not compositional. Our heuristic outperforms the best-known heuristic to date. The heuristic is parameterized, leading to the additional advantage that it allows to trade off execution time vs. solution quality, and to bound the time needed to compute a solution. The latter makes it particularly well-suited for resource-constrained embedded platforms

Liveness and boundedness of synchronous data flow graphs

Synchronous Data Flow Graphs (SDFGs) have proven to be suitable for specifying and analyzing streaming applications that run on single- or multi-processor platforms. Streaming applications essentially continue their execution indefinitely. Therefore, one of the key properties of an SDFG is liveness, i.e., whether all parts of the SDFG can run infinitely often. Another elementary requirement is whether an implementation of an SDFG is feasible using a limited amount of memory. In this paper, we study two interpretations of this property, called boundedness and strict boundedness, that were either already introduced in the SDFG literature or studied for other models. A third and new definition is introduced, namely self-timed boundedness, which is very important to SDFGs, because self-timed execution results in the maximal throughput of an SDFG. Necessary and sufficient conditions for liveness in combination with all variants of boundedness are given, as well as algorithms for checking those conditions. As a by-product, we obtain an algorithm to compute the maximal achievable throughput of an SDFG that relaxes the requirement of strong connectedness in earlier work on throughput analysis