Constraint Based System-Level Diagnosis of Multiprocessors

Massively parallel multiprocessors induce new requirements for system-level fault diagnosis, like handling a huge number of processing elements in an inhomogeneous system. Traditional diagnostic models (like PMC, BGM, etc.) are insufficient to fulfill all of these requirements. This paper presents a novel modelling technique, based on a special area of artificial intelligence (AI) methods: constraint satisfaction (CS). The constraint based approach is able to handle functional faults in a similar way to the Russel-Kime model. Moreover, it can use multiple-valued logic to deal with system components having multiple fault modes. The resolution of the produced models can be adjusted to fit the actual diagnostic goal. Consequently, constrint based methods are applicable to a much wider range of multiprocessor architectures than earlier models. The basic problem of system-level diagnosis, syndrome decoding, can be easily transformed into a constraint satisfaction problem (CSP). Thus, the diagnosis algorithm can be derived from the related constraint solving algorithm. Different abstraction leveles can be used for the various diagnosis resolutions, employing the same methodology. As examples, two algorithms are described in the paper; both of them is intended for the Parsytec GCel massively parallel system. The centralized method uses a more elaborate system model, and provides detailed diagnostic information, suitable for off-line evaluation. The distributed method makes fast decisions for reconfiguration control, using a simplified model. Keywords system-level self-diagnosis, massively parallel computing systems, constraint satisfaction, diagnostic models, centralized and distributed diagnostic algorithms

Strategic directions in constraint programming

An abstract is not available

Recombinase technology: applications and possibilities

The use of recombinases for genomic engineering is no longer a new technology. In fact, this technology has entered its third decade since the initial discovery that recombinases function in heterologous systems (Sauer in Mol Cell Biol 7(6):2087–2096, 1987). The random insertion of a transgene into a plant genome by traditional methods generates unpredictable expression patterns. This feature of transgenesis makes screening for functional lines with predictable expression labor intensive and time consuming. Furthermore, an antibiotic resistance gene is often left in the final product and the potential escape of such resistance markers into the environment and their potential consumption raises consumer concern. The use of site-specific recombination technology in plant genome manipulation has been demonstrated to effectively resolve complex transgene insertions to single copy, remove unwanted DNA, and precisely insert DNA into known genomic target sites. Recombinases have also been demonstrated capable of site-specific recombination within non-nuclear targets, such as the plastid genome of tobacco. Here, we review multiple uses of site-specific recombination and their application toward plant genomic engineering. We also provide alternative strategies for the combined use of multiple site-specific recombinase systems for genome engineering to precisely insert transgenes into a pre-determined locus, and removal of unwanted selectable marker genes