Proposal of a multi-core processor from the viewpoint of evolutionary computation

Abstract

The use of low-cost multi-core processors that target small-scale parallelism of several or several tens of processing units has recently spread to general-purpose personal computers. As part of this trend, research on converting evolutionary computation to parallel processing on multi-core processors has begun. In our research, we place importance on proposing multi-core processor architectures from the viewpoint of evolutionary computation and on proposing indices for identifying chips that exhibit high affinity with evolutionary computation from among existing multi-core processors. This paper takes a first step in this direction by focusing on local memory built into a multi-core processor and proposing a specification that allows the contents of local memory in any core to be directly accessed by any other core without using main memory. We describe a desktop comparison with singlecore processors and general multi-core processors and show that a speed-up effect should be expected with the proposed specification for both fine-grain and coarse-grained parallelization techniques. We also describe an evaluation on actual multicore processor for the case of fine-grain parallelization and show that the speed-up effect is also useful in reducing energy consumption.

Acceleration of genetic algorithms for sudoku solution on many-core processors

“…In previous experiments that involved the evaluation of benchmark tests implemented on multi-core processors [10], tables for random number generation were placed on each core to reduce the time required for random number generation. On the other hand, the shared memory of the GTX 460 is small, with a maximum of 48 KB, so if random number tables are placed in each block, the required number of individuals cannot be defined.…”

“…The features of many-core processors include suitability for small and medium scale parallelization of from several to several hundreds of nodes, and low-cost compared to massively parallel computing systems. This environment has stimulated research on parallelization of genetic computing on many-core processors [6][7][8][9][10]. Current reports, however, focus on benchmark tests for genetic computing using typical GPUs.…”