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In Figure 1, coupling analysis between flow and heat conduction in objects are realized by changing a flow solver to a heat conduction solver in some blocks which are assigned to the object material. With this framework, solvers inside each block can be changed by block( Figure 1). The parallel procedures and multi-block/overset data control procedures are clearly separated from the CFD solver modules so that the solver can be modified without considering the parallel processes and the multi-block/overset data handling as if the solver modules are only for single block problems. The second is inside-one-process using memory copy, exactly speaking, pointer switching. The first is process-to-process using MPI library. Two kinds of data transfer for inter-block communication can be considered. The information about faulty-clusters, which is gathered in this way, is used for resource allocation for the new incoming programs.īased upon the connection and overset information obtained from the pre-process programs createConnect3D and createOversetIndex, necessary data which are required for calculation at each block are transferred. Notice that instead of being idle, these parts of the massivelly parallel system may run diagnostic examination while the rest of the (fault-free) system runs programs. Īt each moment, one may expect some of the nodes to be idle (due to fragmentatation of the system). In some cases, a trade-off between r and d is possible. The complexity of the local diagnostic procedure is independent of system size, but in a complicated relation with the distribution and the number of faults. Given the probability of faults, however, one can estimate the fault-cluster distribution and consequently r and d. Here, the maximal examination radius r of a fault-cluster and the minimal distance d between fault-clusters are usually not known at the beginning of the diagnostic procedure. As a result, for an initiator to achive k-diagnosibility, a communication k’-neighborhood must be examined, where k ′ ≤ k + / 2 ] ( r − 2 ) + r (see ). To properly diagnose a fault-cluster, it is necessary to test all communication links on the boundary of the fault-cluster. We define k-diagnosibility to be the ability of a fault-free node to properly diagnose all nodes and links within its physical k-neighborhood. Here, physical ( communication) k-neighborhood of a node is defined to be the set of all nodes having their physical (communication) distance at most k. To do this, each initiator issues the following two-stage broadcast/reduction operation: (1) the diagnostic request is broadcast from the initiator into its communication k-neighborhood, (2) the diagnostic results are reduced back to the initiator. To locate these faults, we designed a local run-time diagnosis, i.e., a parallel procedure in which several nodes, referred to as initiatiors, may start examining their corresponding neighborhoods concurrently using only the available local communication circuitry. Our work is based on the assumption that both production and run-time faults may be present in the system. Roman Trobec, in Advances in Parallel Computing, 1998 2 DIAGNOSIS EXAMINATION The company has a lot of tutorials and FAQs.Borut Robič. They are easy to contact and are quick to respond.
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