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Joseph M. Hellerstein

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2 papers

1 author row

ICRA Conference 2021 Conference Paper

Serverless Multi-Query Motion Planning for Fog Robotics

Raghav Anand
Jeffrey Ichnowski
Chenggang Wu 0001
Joseph M. Hellerstein
Joseph E. Gonzalez
Ken Goldberg

Robots in semi-structured environments such as homes and warehouses sporadically require computation of high-dimensional motion plans. Cloud and fog-based parallelization of motion planning can speed up planning. This can be further made efficient by the use of "serverless" on-demand computing as opposed to always-on high end computers. This paper explores parallelizing the computation of a sampling-based multi-query motion planner based on asymptotically-optimal Probabilistic Road Maps (PRM*) using the simultaneous execution of 100s of cloud-based serverless functions. We propose an algorithm to overcome the communication and bandwidth limitations of serverless computing and use different work-sharing techniques to further optimize the cost and run time. Additionally, we provide proofs of probabilistic completeness and asymptotic optimality. In experiments on synthetic benchmarks and on a physical Fetch robot performing a sequence of decluttering motions, we observe up to a 50x speedup relative to a 4 core edge computer with only a marginally higher cost.

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UAI Conference 2010 Conference Paper

GraphLab: A New Framework For Parallel Machine Learning

Yucheng Low
Joseph E. Gonzalez
Aapo Kyrola
Danny Bickson
Carlos Guestrin
Joseph M. Hellerstein

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

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