An Open Computing Language-Based Parallel Brute Force Algorithm For Formal Concept Analysis On Heterogeneous Architectures

Algorithms for the extraction of formal concepts are widely studied in several areas of knowledge, such as finance, health, and statistics. However, these algorithms require high-performance processing due to their combinatorial characteristics. In this work, an Open computing language (OpenCL)-based Brute Force algorithm is proposed and evaluated for formal concept extraction on heterogeneous architectures (CPU+GPU and CPU+FPGA). The CPU+GPU architecture presents higher performance and scalability than other architectures when our Brute Force algorithm processes high dimensional contexts with many objects and attributes. Our parallel approach shows performance results up to 18x better than a smarter sequential algorithm called Data-Peeler. Moreover, our Brute Force algorithm running on CPU+GPU architecture has greater energy efficiency, reaching at least 1.79x more operations per energy consumption than other algorithms on different architectures explored in this work.