Timezone: »

 
Poster
Asynchronous Parallel Greedy Coordinate Descent
Yang You · Xiangru Lian · Ji Liu · Hsiang-Fu Yu · Inderjit Dhillon · James Demmel · Cho-Jui Hsieh

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #65
in Wednesday Posters »

n this paper, we propose and study an Asynchronous parallel Greedy Coordinate Descent (Asy-GCD) algorithm for minimizing a smooth function with bounded constraints. At each iteration, workers asynchronously conduct greedy coordinate descent updates on a block of variables. In the first part of the paper, we analyze the theoretical behavior of Asy-GCD and prove a linear convergence rate. In the second part, we develop an efficient kernel SVM solver based on Asy-GCD in the shared memory multi-core setting. Since our algorithm is fully asynchronous---each core does not need to idle and wait for the other cores---the resulting algorithm enjoys good speedup and outperforms existing multi-core kernel SVM solvers including asynchronous stochastic coordinate descent and multi-core LIBSVM.

Keywords: Large Scale Learning and Big Data · Convex Optimization
[ PDF] 

Author Information

Yang You (UC Berkeley)
Xiangru Lian (University of Rochester)
Ji Liu (Kwai Inc.)
Hsiang-Fu Yu (University of Texas at Austin)
Inderjit Dhillon (Google & UT Austin)
James Demmel (UC Berkeley)
Cho-Jui Hsieh (UCLA)

More from the Same Authors