A database on so-called “Existing Chemicals” is being
developed at the European Chemicals Bureau (ECB).
The database IUCLID contains all data sets submitted by Industry
following Council Regulation (EEC)
793/93 on the “Evaluation and Control of the Risks of
Existing Substances”. The Regulation obliges
Industry
to submit all readily available data on “High Production Volume
Chemicals”, using the HEDSET software
package, to the ECB. Copies of the database are being used by the
European Commission and in the
EU-Member States Authorities as the data source for selecting priority
substances and for carrying out the
risk assessment on them. The outcome of the risk assessment
process will be a validated IUCLID data
sheet which includes a summary risk assessment report for each priority
substance. The multilingual database
is available from the ECB, in IUCLID format or as an easy to use CD-ROM
version, including the original
data submitted by Industry and later the validated data and the risk
assessment reports.
State-of-the-art system synthesis techniques employ meta-heuristic optimization techniques for Design Space Exploration (DSE) to tailor application execution, e.g., defined by a dataflow graph, for a given target platform. Unfortunately, the performance evaluation of each implementation candidate is computationally very expensive, in particular on recent multi-core platforms, as this involves compilation to and extensive evaluation on the target hardware. Applying heuristics for performance evaluation on the one hand allows for a reduction of the exploration time but on the other hand may deteriorate the convergence of the optimization technique toward performance-optimal solutions with respect to the target platform. To address this problem, we propose DSE strategies that are able to dynamically trade off between (i) approximating heuristics to guide the exploration and (ii) accurate performance evaluation, i.e., compilation of the application and subsequent performance measurement on the target platform. Technically, this is achieved by introducing a set of additional, but easily computable guiding objective functions, and varying the set of objective functions that are evaluated during the DSE adaptively. One major advantage of these guiding objectives is that they are generically applicable for dataflow models without having to apply any configuration techniques to tailor their parameters to the specific use case. We show this for synthetic benchmarks as well as a real-world control application. Moreover, the experimental results demonstrate that our proposed adaptive DSE strategies clearly outperform a state-of-the-art DSE approach known from literature in terms of the quality of the gained implementations as well as exploration times. Amongst others, we show a case for a two-core implementation where after about 3 hours of exploration time one of our proposed adaptive DSE strategies already obtains a 60% higher performance value than obtained by the state-of-the-art approach. Even when the state-of-the-art approach is given a total exploration time of more than 2 weeks to optimize this value, the proposed adaptive DSE strategy features a 20% higher performance value after a total exploration time of about 4 days.
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