Search results for "BACKTRACKING"
showing 8 items of 8 documents
CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing
2014
We present CUSHAW2-GPU to accelerate the CUSHAW2 algorithm using compute unified device architecture (CUDA)-enabled GPUs. Two critical GPU computing techniques, namely intertask hybrid CPU-GPU parallelism and tile-based Smith-Waterman map backtracking using CUDA, are investigated to facilitate fast alignments. By aligning both simulated and real reads to the human genome, our aligner yields comparable or better performance compared to BWA-SW, Bowtie2, and GEM. Furthermore, CUSHAW2-GPU with a Tesla K20c GPU achieves significant speedups over the multithreaded CUSHAW2, BWA-SW, Bowtie2, and GEM on the 12 cores of a high-end CPU for both single-end and paired-end alignment.
Counting by Statistics on Search Trees: Application to Constraint Satisfaction Problems
1997
In 1975, Knuth proposed a simple statistical method for investigating search trees. We use this technique for estimating the number of solutions of constraint satisfaction problem CSP and boolean satisfiability problem SAT instances. We show that, depending on domain reductions, tree-based estimates have a lower variance than estimates based on uniform sampling from the search space. Nevertheless, because the variance remains extremely high in the general case, a confidence interval cannot be derived, but a lower bound of the number of solutions. These results are confirmed by many experiments.
Algoritmi krustvārdu mīklu veidošanai
2020
Darba mērķis ir izpētīt, kādi algoritmi spēj izveidot krustvārdu mīklas, un salīdzināt iegūtos rezultātus pēc dažādiem kritērijiem. Krustvārdu mīklu izveidei tiek padoti vārdi ar definīcijām un tiek veidots jauns režģis no padotajiem vārdiem. Tiks salīdzināti dažādi algoritmi pēc kritērijiem, kā piemēram, cik kompakta ir izveidotā krustvārdu mīkla, cik daudz krustpunktu ir izveidoti starp vārdiem, kāda ir malu proporcija u.tml. Darbā iegūtie algoritmi, kas tiks programmēti programmēšanas valodā Python, varētu veidot krustvārdu mīklas, lai cilvēkiem tas nebūtu jādara pašiem manuāli, kā arī tas ietaupītu cilvēku laiku.
Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games
2017
We study quantum algorithms on search trees of unknown structure, in a model where the tree can be discovered by local exploration. That is, we are given the root of the tree and access to a black box which, given a vertex $v$, outputs the children of $v$. We construct a quantum algorithm which, given such access to a search tree of depth at most $n$, estimates the size of the tree $T$ within a factor of $1\pm \delta$ in $\tilde{O}(\sqrt{nT})$ steps. More generally, the same algorithm can be used to estimate size of directed acyclic graphs (DAGs) in a similar model. We then show two applications of this result: a) We show how to transform a classical backtracking search algorithm which exam…
RDF* Graph Database as Interlingua for the TextWorld Challenge
2019
This paper briefly describes the top-scoring submission to the First TextWorld Problems: A Reinforcement and Language Learning Challenge. To alleviate the partial observability problem, characteristic to the TextWorld games, we split the Agent into two independent components: Observer and Actor, communicating only via the Interlingua of the RDF* graph database. The RDF* graph database serves as the “world model” memory incrementally updated by the Observer via FrameNet informed Natural Language Understanding techniques and is used by the Actor for the efficient exploration and planning of the game Action sequences. We find that the deep-learning approach works best for the Observer componen…
The Deterministic Annealing Filter: A new clustering method for gamma-ray tracking algorithms
2010
A new method of clustering for forward-tracking algorithms has been developed to reconstruct the tracks of gamma-rays in high-resolution detector systems such as AGATA (Advanced GAmma Tracking Array). This technique, called Deterministic Annealing Filter (DAF), comes from statistical physics and is used in high-energy physics. After a description of the DAF method and of the forward-tracking algorithm, the performance of this clustering method is discussed in terms of photopeak efficiency and peak-to-total ratio obtained with GEANT4 simulations for the AGATA geometry. A comparison with the standard so-called "cone clustering method" shows similar performances with a better photopeak efficie…
Recruitment of Xrn1 to stress-induced genes allows efficient transcription by controlling RNA polymerase II backtracking
2020
A new paradigm has emerged proposing that the crosstalk between nuclear transcription and cytoplasmic mRNA stability keeps robust mRNA levels in cells under steady-state conditions. A key piece in this crosstalk is the highly conserved 5′–3′ RNA exonuclease Xrn1, which degrades most cytoplasmic mRNAs but also associates with nuclear chromatin to activate transcription by not well-understood mechanisms. Here, we investigated the role of Xrn1 in the transcriptional response of Saccharomyces cerevisiae cells to osmotic stress. We show that a lack of Xrn1 results in much lower transcriptional induction of the upregulated genes but in similar high levels of their transcripts because of parallel …
Faster GPU-Accelerated Smith-Waterman Algorithm with Alignment Backtracking for Short DNA Sequences
2014
In this paper, we present a GPU-accelerated Smith-Waterman (SW) algorithm with Alignment Backtracking, called GSWAB, for short DNA sequences. This algorithm performs all-to-all pairwise alignments and retrieves optimal local alignments on CUDA-enabled GPUs. To facilitate fast alignment backtracking, we have investigated a tile-based SW implementation using the CUDA programming model. This tiled computing pattern enables us to more deeply explore the powerful compute capability of GPUs. We have evaluated the performance of GSWAB on a Kepler-based GeForce GTX Titan graphics card. The results show that GSWAB can achieve a performance of up to 56.8 GCUPS on large-scale datasets. Furthermore, ou…