A quantitative and qualitative analysis of tensor decompositions on spatiotemporal data

Henretty, Tom; Baskaran, Muthu Manikandan; Ezick, James; Bruns-Smith, David; Simon, Tyler A.

doi:10.1109/hpec.2017.8091028

Cited by 12 publications

(2 citation statements)

References 9 publications

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“…Chi and Kolda showed in [17] that under these assumptions a Poisson CP tensor model is an effective low-rank approximation of X. The Poisson CP tensor model has shown to be effective in analyzing latent patterns and relationships in count data across many application areas, including food production [13], network analysis [11,19], term-document analysis [16,29], email analysis [14], link prediction [18], geosptial analysis [22,28], web page analysis [39], and phenotyping from electronic health records [27,30,31] One numerical approach to fit low-rank Poisson CP tensor models to data, tensor maximum likelihood estimation, has proven to be effective. Computing a Poisson CP tensor model via tensor maximum likelihood estimation involves minimizing the following non-linear, nonconvex optimization problem:…”

Section: 4mentioning

confidence: 99%

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

Myers¹,

Dunlavy²

2022

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In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.

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Section: 4mentioning

confidence: 99%

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

Myers¹,

Dunlavy²

2022

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“…Some data analytics process large amounts of sparse, high dimensional data, such as that produced in signal processing [14] and geospatial analysis [15]. Within such areas, analysts are interested in discovering previously unknown relationships between elements in the data.…”

Section: Tensor Factorizationmentioning

confidence: 99%

An Empirical Evaluation of Allgatherv on Multi-GPU Systems

Rolinger¹,

Simon²,

Krieger³

2018

2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Applications for deep learning and big data analytics have compute and memory requirements that exceed the limits of a single GPU. However, effectively scaling out an application to multiple GPUs is challenging due to the complexities of communication between the GPUs, particularly for collective communication with irregular message sizes. In this work, we provide a performance evaluation of the Allgatherv routine on multi-GPU systems, focusing on GPU network topology and the communication library used. We present results from the OSUmicro benchmark as well as conduct a case study for sparse tensor factorization, one application that uses Allgatherv with highly irregular message sizes. We extend our existing tensor factorization tool to run on systems with different node counts and varying number of GPUs per node. We then evaluate the communication performance of our tool when using traditional MPI, CUDA-aware MVAPICH and NCCL across a suite of realworld data sets on three different systems: a 16-node cluster with one GPU per node, NVIDIA's DGX-1 with 8 GPUs and Cray's CS-Storm with 16 GPUs. Our results show that irregularity in the tensor data sets produce trends that contradict those in the OSU micro-benchmark, as well as trends that are absent from the benchmark.

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