Hardware Accelerated Mining of Domain Knowledge

Agent-based decision aids can improve their performance by mining domain knowledge captured in cognitive domain ontologies (CDOs). The CDOs can be specialized to represent knowledge from any of a large variety of domains, including network security. Search algorithms mine data from these CDOs using constraints. While complex CDOs increase the capabilities of decision agents, they are computationally expensive to mine. Complex CDOs can function as powerful decision aids if they can be processed in realistic time frames. To achieve this, it is necessary to parallelize and accelerate the CDO knowledge mining algorithms. This chapter introduces CDOs and examines how they can be transformed into constraint networks for processing on high performance compute platforms. The constraint networks were solved using a parallelized generate and test exhaustive depth first search algorithm. Two compute platforms for acceleration are examined: Intel Xeon multicore processors, and NVIDIA graphics processors (GPGPUs). This chapter shows that 1 NVIDIA Tesla C2070 GPGPU can provide a speed-up of 100 times over 1 Xeon CPU core for the search algorithm. The scaling of the algorithm on a high performance GPGPU cluster achieved estimated speed-ups of over 1,000 times.