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Xinghao Pan

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4 papers

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NeurIPS Conference 2016 Conference Paper

Cyclades: Conflict-free Asynchronous Machine Learning

Xinghao Pan
Maximilian Lam
Stephen Tu
Dimitris Papailiopoulos
Ce Zhang
Michael Jordan
Kannan Ramchandran
Christopher Ré

We present Cyclades, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. Cyclades is asynchronous during model updates, and requires no memory locking mechanisms, similar to Hogwild! -type algorithms. Unlike Hogwild! , Cyclades introduces no conflicts during parallel execution, and offers a black-box analysis for provable speedups across a large family of algorithms. Due to its inherent cache locality and conflict-free nature, our multi-core implementation of Cyclades consistently outperforms Hogwild! -type algorithms on sufficiently sparse datasets, leading to up to 40% speedup gains compared to Hogwild! , and up to 5\times gains over asynchronous implementations of variance reduction algorithms.

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NeurIPS Conference 2015 Conference Paper

Parallel Correlation Clustering on Big Graphs

Xinghao Pan
Dimitris Papailiopoulos
Samet Oymak
Benjamin Recht
Kannan Ramchandran
Michael Jordan

Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, in practice KwikCluster requires a large number of clustering rounds, a potential bottleneck for large graphs. We present C4 and ClusterWild! , two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds, and provably achieve nearly linear speedups. C4 uses concurrency control to enforce serializability of a parallel clustering process, and guarantees a 3-approximation ratio. ClusterWild! is a coordination free algorithm that abandons consistency for the benefit of better scaling; this leads to a provably small loss in the 3 approximation ratio. We provide extensive experimental results for both algorithms, where we outperform the state of the art, both in terms of clustering accuracy and running time. We show that our algorithms can cluster billion-edge graphs in under 5 seconds on 32 cores, while achieving a 15x speedup.

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NeurIPS Conference 2014 Conference Paper

Parallel Double Greedy Submodular Maximization

Xinghao Pan
Stefanie Jegelka
Joseph Gonzalez
Joseph Bradley
Michael Jordan

Many machine learning problems can be reduced to the maximization of submodular functions. Although well understood in the serial setting, the parallel maximization of submodular functions remains an open area of research with recent results only addressing monotone functions. The optimal algorithm for maximizing the more general class of non-monotone submodular functions was introduced by Buchbinder et al. and follows a strongly serial double-greedy logic and program analysis. In this work, we propose two methods to parallelize the double-greedy algorithm. The first, coordination-free approach emphasizes speed at the cost of a weaker approximation guarantee. The second, concurrency control approach guarantees a tight 1/2-approximation, at the quantifiable cost of additional coordination and reduced parallelism. As a consequence we explore the trade off space between guaranteed performance and objective optimality. We implement and evaluate both algorithms on multi-core hardware and billion edge graphs, demonstrating both the scalability and tradeoffs of each approach.

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NeurIPS Conference 2013 Conference Paper

Optimistic Concurrency Control for Distributed Unsupervised Learning

Xinghao Pan
Joseph Gonzalez
Stefanie Jegelka
Tamara Broderick
Michael Jordan

Research on distributed machine learning algorithms has focused primarily on one of two extremes---algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this optimistic concurrency control'' paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment. "

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