Many-core hardware is well adopted in scientific computing for a number of applications in an academic setting. Uncertainty about upcoming architectures and large development times for this hardware result in a modest acceptance in industry for commercial use. An upcoming turn from language-based many-core programming towards directive-based frameworks, similar to OpenMP, is an attempt to tackle these issues. We present a case study for a many-core acceleration of a large-scale commercial CFD solver by means of such frameworks. We achieved a local acceleration of up to 45 for hot spots with recent hardware but the global speedup remains below 2. The main obstacle for an efficient instrumentation is the design and the complexity of the original software. Further, restrictions given by the hardware and the frameworks exist. Based on the results we sketch a long term plan for a further acceleration.

Abstract. We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw conclusions that may not be 100% accurate. Removing all of the defective products, or halting production in extreme cases, will reduce costs and eliminate embarrassing and expensive recalls. We use the knowledge that experts have accumulated over many years, dynamic data derived from networks of smart sensors using both audio and chemical spectral signatures, multiple scales to look at individual products and larger quantities of products, and finally adaptive models and algorithms.

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