# Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph

@article{Hao2016TestingFP, title={Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph}, author={Ning Hao and Amirreza Oghbaee and Mohammad Rostami and Nate Derbinsky and Jos{\'e} Bento}, journal={2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}, year={2016}, pages={835-844} }

There is an ongoing effort to develop tools that apply distributed computational resources to tackle large problems or reduce the time to solve them. [... ] Key Method We show that this scheme, an interpretation of the ADMM as a message-passing algorithm on a factor-graph, can automatically exploit fine-grained parallelism both in GPUs and shared-memory multi-core computers and achieves significant speedup in such diverse application domains as combinatorial optimization, machine learning, and optimal control… Expand

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## References

SHOWING 1-10 OF 33 REFERENCES

Distributed Non-Convex ADMM-inference in Large-scale Random Fields

- Computer Science
- 2014

This work proposes a parallel and distributed algorithm for solving discrete labeling problems in large scale random fields using a tree-based decomposition of the original optimization problem which is solved using a non convex form of the method of alternating direction method of multipliers (ADMM).

Distributed Non-convex ADMM-based inference in large-scale random fields

- Computer ScienceBMVC
- 2014

This work proposes a parallel and distributed algorithm for solving discrete labeling problems in large scale random fields using a tree-based decomposition of the original optimization problem which is solved using a non convex form of the method of alternating direction method of multipliers (ADMM).

GPU computing in discrete optimization. Part II: Survey focused on routing problems

- Computer ScienceEURO J. Transp. Logist.
- 2013

A tutorial style introduction to modern PC architectures and GPU programming and a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems are given.

MPC Toolbox with GPU Accelerated Optimization Algorithms

- Computer Science
- 2012

It is demonstrated that using GPUs for solving MPC problems can provide a speedup in solution time, and a case study is presented in which GPUs are utilized for a Linear Programming Interior Point Method to solve a test case where a series of power plants must be controlled to minimize the cost of power production.

An efficient GPU implementation of the revised simplex method

- Computer Science2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW)
- 2010

This paper presents an efficient GPU implementation of a very popular algorithm for linear programming, the revised simplex method, and describes how to carry out the steps of the revisedsimplex method to take full advantage of the parallel processing capabilities of a GPU.

SnapVX: A Network-Based Convex Optimization Solver

- Computer ScienceJ. Mach. Learn. Res.
- 2017

SnapVX is a high-performance solver for convex optimization problems defined on networks that combines the capabilities of two open source software packages: Snap.py and CVXPY.

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

- Computer ScienceISCA
- 2010

This paper discusses optimization techniques for both CPU and GPU, analyzes what architecture features contributed to performance differences between the two architectures, and recommends a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.

D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization

- Computer ScienceIEEE Transactions on Signal Processing
- 2013

D-ADMM is proven to converge when the network is bipartite or when all the functions are strongly convex, although in practice, convergence is observed even when these conditions are not met.

Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture

- Computer ScienceInf. Sci.
- 2011

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

- Computer ScienceFound. Trends Mach. Learn.
- 2011

It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.