Home Page for Francisco de Jesus Coral Sabido | Parasol Laboratory


Picture Francisco de Jesus Coral Sabido
PhD Student

Parasol Laboratory url: http://parasollab.web.illinois.edu/~fdc/
Department of Computer Science email:
University of Illinois at Urbana-Champaign office: 2407 Siebel Center
Urbana, IL 61801, USA


CV

Howdy!, I am a PhD student from Texas A&M visiting UIUC. I joined the lab during Spring 2015. My research interests are Parallel Algorithms and High Performance Computing. I am currently working on the STAPL project.


Publications

Fast Approximate Distance Queries in Unweighted Graphs using Bounded Asynchrony, Adam Fidel, Francisco Coral, Colton Riedel, Nancy M. Amato, Lawrence Rauchwerger, Workshop on Languages and Compilers for Parallel Computing (LCPC 2016). Lecture Notes in Computer Science, vol 10136. Springer, Cham., pp. 40-54, Rochester NY, USA, Jan 2017. DOI: 10.1007/978-3-319-52709-3_4
Keywords: Approximate Algorithms, Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@inproceedings{
author="Adam Fidel and Francisco Coral Sabido and Colton Riedel and Nancy M. Amato and Lawrence Rauchwerger",
title="Fast Approximate Distance Queries in Unweighted Graphs using Bounded Asynchrony",
year=2017,
booktitle="Languages and Compilers for Parallel Computing (LCPC 2016)",
series="Lecture Notes in Computer Science",
volume="10136",
publisher="Springer, Cham",
note = "DOI: https://doi.org/10.1007/978-3-319-52709-3_4"
}


Abstract

We introduce a new parallel algorithm for approximate breadth-first ordering of an unweighted graph by using bounded asynchrony to parametrically control both the performance and error of the algorithm. This work is based on the k-level asynchronous (KLA) paradigm that trades expensive global synchronizations in the level-synchronous model for local synchronizations in the asynchronous model, which may result in redundant work. Instead of correcting errors introduced by asynchrony and redoing work as in KLA, in this work we control the amount of work that is redone and thus the amount of error allowed, leading to higher performance at the expense of a loss of precision. Results of an implementation of this algorithm are presented on up to 32,768 cores, showing 2.27x improvement over the exact KLA algorithm and 3.8x improvement over the level-synchronous version with minimal error on several graph inputs.