Asynchronous Parallel Greedy Coordinate Descent

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.

Authors

• Yang You Shanghai Jiao Tong University
• Xiangru Lian
• Ji Liu Advanced Micro Devices, Inc.
• Hsiang-Fu Yu
• Inderjit Dhillon
• James Demmel
• Cho-Jui Hsieh University of California, Los Angeles

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