[No Title Available]

McCallum, Cecília

doi:10.1590/s0104-93131999000100011

Cited by 3 publications

(2 citation statements)

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“…ID/250 and ID/1000 run an image denoise algorithm to remove Gaussian noise from 2D grayscale images of dimension 250 by 250 and 1000 by 1000. FBP/C1 and FBP/C3 perform belief propagation on a factor graph provided by the cora-1 and cora-3 datasets [79,91]. ALS/N runs an alternating least squares algorithm on the NPIC-500 dataset [81].…”

Section: Parallelizing Dynamic Data-graph Computationsmentioning

confidence: 99%

Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling

Kaler¹,

Hasenplaugh²,

Schardl³

et al. 2022

Massive Graph Analytics

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A data-graph computation -popularized by such programming systems as Galois, Pregel, GraphLab, PowerGraph, and GraphChi -is an algorithm that performs local updates on the vertices of a graph. During each round of a data-graph computation, an update function atomically modifies the data associated with a vertex as a function of the vertex's prior data and that of adjacent vertices. A dynamic data-graph computation updates only an active subset of the vertices during a round, and those updates determine the set of active vertices for the next round.This paper introduces PRISM, a chromatic-scheduling algorithm for executing dynamic data-graph computations. PRISM uses a vertex-coloring of the graph to coordinate updates performed in a round, precluding the need for mutual-exclusion locks or other nondeterministic data synchronization. A multibag data structure is used by PRISM to maintain a dynamic set of active vertices as an unordered set partitioned by color. We analyze PRISM using work-span analysis. Let G = (V, E) be a degree-∆ graph colored with χ colors, and suppose that Q ⊆ V is the set of active vertices in a round. Define size(Q) = |Q| + v∈Q deg(v), which is proportional to the space required to store the vertices of Q using a sparsegraph layout. We show that a P-processor execution of PRISM performs updates in Q using O(χ(lg(Q/χ) + lg ∆) + lg P) span and Θ(size(Q) + χ + P) work. These theoretical guarantees are matched by good empirical performance. We modified GraphLab to incorporate PRISM and studied seven application benchmarks on a 12-core multicore machine. PRISM executes the benchmarks 1.2-2.1 times faster than GraphLab's nondeterministic lock-based scheduler while providing deterministic behavior.This paper also presents PRISM-R, a variation of PRISM that executes dynamic data-graph computations deterministically even when updates modify global variables with associative operations. PRISM-R satisfies the same theoretical bounds as PRISM, but its implementation is more involved, incorporating a multivector data structure to maintain an ordered set of vertices partitioned by color.

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Section: Parallelizing Dynamic Data-graph Computationsmentioning

confidence: 99%

Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling

Kaler¹,

Hasenplaugh²,

Schardl³

et al. 2022

Massive Graph Analytics

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“…The LDA generative model assumes that documents contain a combination of topics, and that topics are a distribution of words; since the words in a document are known, the latent variable of topics can be estimated through Gibbs sampling. We used an implementation of the LDA algorithm provided by the Mallet package [6] adjusting one parameter (alpha~0:30) to favor fewer topics per document, since individual utterance updates tend to contain fewer topics than the typical documents (newspaper or encyclopedia articles) to which LDA is applied. Besides, in order to avoid subsequent unnecessary complexity burden while ensuring high interpretability of the results, we also reduced the size of the vocabulary of the words used by the LDA to the most frequent 500 words, excluding the stopword elements in the Reddit corpus.…”

Section: Topicmentioning

confidence: 99%

Argumentation system for intelligent assistants using fuzzy-based reasoning

Koivuaho

Ibrahim

Ummul

et al. 2018

Data Science and Knowledge Engineering for Sensing Decision Support

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This paper addresses the issue of building intelligent assistant that is able to maintain sustainable conversation with the user while taking into account his emotional, personality aspects, and, at the same time, maintaining high level focus. The approach makes use four meta-features; namely, topic, emotion, personality and dialogue-act. A sequence-tosequence recurrent neural network approach was used to learn answer prototypes from Reddit.com sport corpus, while an ANFIS based approach was developed to extrapolate from the limited configurations used by the neural network to various dialogue utterances.

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