Many programs which solve complicated problems can be seen as inversions of other, much simpler, programs. One particular example is transforming verifiers into solvers, which can be achieved with low effort by implementing the verifier in a relational language and then executing it in the backward direction. Unfortunately, as it is common with inverse computations, interpretation overhead may lead to subpar performance compared to direct program inversion. In this paper we discuss functional conversion aimed at improving relational miniKanren specifications with respect to a known fixed direction. Our preliminary evaluation demonstrates a significant performance increase for some programs which exemplify the approach.

The longest common subsequence (LCS) problem on a pair of strings is a classical problem in string algorithms. Its extension, the semi-local LCS problem, provides a more detailed comparison of the input strings, without any increase in asymptotic running time. Several semi-local LCS algorithms have been proposed previously; however, to the best of our knowledge, none have yet been implemented. In this paper, we explore a new hybrid approach to the semi-local LCS problem. We also propose a novel bit-parallel LCS algorithm. In the experimental part of the paper, we present an implementation of several existing and new parallel LCS algorithms and evaluate their performance.

While GPU utilization allows one to speed up computations to the orders of magnitude, memory management remains the bottleneck making it often a challenge to achieve the desired performance. Hence, different memory optimizations are leveraged to make memory being used more effectively. We propose an approach automating memory management utilizing partial evaluation, a program transformation technique that enables data accesses to be pre-computed, optimized, and embedded into the code, saving memory transactions. An empirical evaluation of our approach shows that the transformed program could be up to 8 times as efficient as the original one in the case of CUDA C naïve string pattern matching algorithm implementation.

Any expression M in ULC (the untyped λ-calculus) can be compiled into a rather low-level language we call LLL, whose programs contain none of the traditional implementation devices for functional languages: environments, thunks, closures, etc. A compiled program is first-order functional and has a fixed set of working variables, whose number is independent of M. The generated LLL code in effect traverses the subexpressions of M.