Reconstruction of molecular phylogenetic trees is quite important for molecular evolutionary studies. The molecular phylogenetic tree is one of the evolutionary models, which presents us how organisms evolved at the molecular level [4, 6].
The maximum likelihood method [1] is known to be relatively robust among many methods for reconstruction of molecular phylogenetic trees [3, 7]. In this method, the concept likelihood is defined as the measure for closeness between given data and a hypothesis. We explore the hypothesis space, and select one hypothesis which gives the maximum likelihood.
Unfortunately, however, this method requires extremely high computational cost [2]. One of practical resolutions for this problem is parallel execution. Program fastDNAml [5] is a maximum likelihood method implemented into parallel environment, however, it is expected that parallel logic programming provides us more appropriate concept for parallel execution for this problem. That is, the codes written with parallel logic programming is not only efficient in execution but also comprehensive for human.
ICOT (Institute for New Generation Computer Technology) developed an excellent parallel logic programming language, KL1. Using this language, we can easily write efficient codes for parallelism. KLIC also made it possible to implement programs written in KL1 to many different environments, from massively parallel computer to PC. It is expected that the maximum likelihood method in KLIC/KL1 leads us new discoveries through efficient and accurate data analysis.