230,415 research outputs found
Algorithmic patterns for -matrices on many-core processors
In this work, we consider the reformulation of hierarchical ()
matrix algorithms for many-core processors with a model implementation on
graphics processing units (GPUs). matrices approximate specific
dense matrices, e.g., from discretized integral equations or kernel ridge
regression, leading to log-linear time complexity in dense matrix-vector
products. The parallelization of matrix operations on many-core
processors is difficult due to the complex nature of the underlying algorithms.
While previous algorithmic advances for many-core hardware focused on
accelerating existing matrix CPU implementations by many-core
processors, we here aim at totally relying on that processor type. As main
contribution, we introduce the necessary parallel algorithmic patterns allowing
to map the full matrix construction and the fast matrix-vector
product to many-core hardware. Here, crucial ingredients are space filling
curves, parallel tree traversal and batching of linear algebra operations. The
resulting model GPU implementation hmglib is the, to the best of the authors
knowledge, first entirely GPU-based Open Source matrix library of
this kind. We conclude this work by an in-depth performance analysis and a
comparative performance study against a standard matrix library,
highlighting profound speedups of our many-core parallel approach
The study of probability model for compound similarity searching
Information Retrieval or IR system main task is to retrieve relevant documents according to the users query. One of IR most popular retrieval model is the Vector Space Model. This model assumes relevance based on similarity, which is defined as the distance between query and document in the concept space. All currently existing chemical compound database systems have adapt the vector space model to calculate the similarity of a database entry to a query compound. However, it assumes that fragments represented by the bits are independent of one another, which is not necessarily true. Hence, the possibility of applying another IR model is explored, which is the Probabilistic Model, for chemical compound searching. This model estimates the probabilities of a chemical structure to have the same bioactivity as a target compound. It is envisioned that by ranking chemical structures in decreasing order of their probability of relevance to the query structure, the effectiveness of a molecular similarity searching system can be increased. Both fragment dependencies and independencies assumption are taken into consideration in achieving improvement towards compound similarity searching system. After conducting a series of simulated similarity searching, it is concluded that PM approaches really did perform better than the existing similarity searching. It gave better result in all evaluation criteria to confirm this statement. In terms of which probability model performs better, the BD model shown improvement over the BIR model
Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]
Scalable probabilistic modeling and prediction in high dimensional
multivariate time-series is a challenging problem, particularly for systems
with hidden sources of dependence and/or homogeneity. Examples of such problems
include dynamic social networks with co-evolving nodes and edges and dynamic
student learning in online courses. Here, we address these problems through the
discovery of hierarchical latent groups. We introduce a family of Conditional
Latent Tree Models (CLTM), in which tree-structured latent variables
incorporate the unknown groups. The latent tree itself is conditioned on
observed covariates such as seasonality, historical activity, and node
attributes. We propose a statistically efficient framework for learning both
the hierarchical tree structure and the parameters of the CLTM. We demonstrate
competitive performance in multiple real world datasets from different domains.
These include a dataset on students' attempts at answering questions in a
psychology MOOC, Twitter users participating in an emergency management
discussion and interacting with one another, and windsurfers interacting on a
beach in Southern California. In addition, our modeling framework provides
valuable and interpretable information about the hidden group structures and
their effect on the evolution of the time series
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