High performance declarative memory systems through MapReduce

This paper describes the acceleration of the declarative knowledge retrieval system of a cognitive architecture, namely ACT-R. The core of ACT-R's retrieval mechanism, activation calculation, is accelerated through leveraging the speed of C++ and the MapReduce program model. Work described in this paper represents an extension of previous Erlang-based concurrent activation. ACT-R's retrieval process is re-examined and optimized in this solution. Concurrency available in the execution platform is exploited to maximize the acceleration of declarative retrieval. The resulting implementation, referred to as Accelerated Declarative Memory (ADM), presents a high-performance activation calculation that enables practical use of more massive declarative memories. ADM presents new mechanisms to access and traverse declarative memory to reduce the overhead of executing retrievals. This solution offers retrieval latencies 20 times faster than the previous Erlang solution.