Dichotomy for Symmetric Boolean PCSPs

In one of the most actively studied version of Constraint Satisfaction Problem, a CSP is defined by a relational structure called a template. In the decision version of the problem the goal is to determine whether a structure given on input admits a homomorphism into this template. Two recent independent results of Bulatov [FOCS\u2717] and Zhuk [FOCS\u2717] state that each finite template defines CSP which is tractable or NP-complete. In a recent paper Brakensiek and Guruswami [SODA\u2718] proposed an extension of the CSP framework. This extension, called Promise Constraint Satisfaction Problem, includes many naturally occurring computational questions, e.g. approximate coloring, that cannot be cast as CSPs. A PCSP is a combination of two CSPs defined by two similar templates; the computational question is to distinguish a YES instance of the first one from a NO instance of the second. The computational complexity of many PCSPs remains unknown. Even the case of Boolean templates (solved for CSP by Schaefer [STOC\u2778]) remains wide open. The main result of Brakensiek and Guruswami [SODA\u2718] shows that Boolean PCSPs exhibit a dichotomy (PTIME vs. NPC) when "all the clauses are symmetric and allow for negation of variables". In this paper we remove the "allow for negation of variables" assumption from the theorem. The "symmetric" assumption means that changing the order of variables in a constraint does not change its satisfiability. The "negation of variables" means that both of the templates share a relation which can be used to effectively negate Boolean variables. The main result of this paper establishes dichotomy for all the symmetric boolean templates. The tractability case of our theorem and the theorem of Brakensiek and Guruswami are almost identical. The main difference, and the main contribution of this work, is the new reason for hardness and the reasoning proving the split

Analysis of Effectiveness of Selected Parallel Algorithms on GPU

Omówienie, porównanie i analiza wraz z implementacją wybranych współbieżnych algorytmów sortowania na GPU: bitonic sortu, odd-even sortu, radix sortu i quick sortu (Cedermana i Tsigasa) oraz próba udzielenia odpowiedzi na pytania: który algorytm jest bardziej wydajny i kiedy opłaca się sortować na GPU, a kiedy na CPU.Overview, comparison and analysis with the implementation of selected parallel sorting algorithms on GPU: bitonic sort, odd-even sort, radix sort and quick sort (by Cederman and Tsigas), and an attempt to answer questions: which algorithm is more efficient and when is it better to sort on GPU than on CPU