• Corpus ID: 249921384

TOD: GPU-accelerated Outlier Detection via Tensor Operations

@article{Zhao2021TODGO,
  title={TOD: GPU-accelerated Outlier Detection via Tensor Operations},
  author={Yue Zhao and George H. Chen and Zhihao Jia},
  journal={Proc. VLDB Endow.},
  year={2021},
  volume={16},
  pages={546-560}
}
Outlier detection (OD) is a key machine learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection. In this work, we propose TOD , the first tensor-based system for efficient and scalable outlier detection on distributed multi-GPU machines. A key idea behind TOD is decomposing complex OD applications into a small collection of basic tensor algebra operators. This decomposition enables TOD to accelerate OD… 
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References

SHOWING 1-10 OF 104 REFERENCES

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

A three-module acceleration system called SUOD (scalable unsupervised outlier detection) that focuses on three complementary aspects to accelerate (dimensionality reduction for high-dimensional data, model approximation for complex models, and execution efficiency improvement for taskload imbalance within distributed systems), while controlling detection performance degradation.

GPU Strategies for Distance-Based Outlier Detection

A family of parallel and distributed algorithms for graphic processing units (GPU) derived from two distance-based outlier detection algorithms: BruteForce and SolvingSet are proposed, which differ in the way they exploit the architecture and memory hierarchy of the GPU and guarantee significant improvements with respect to the CPU versions.

Sparx: Distributed Outlier Detection at Scale

Sparx is designed, a data-parallel OD algorithm suitable for shared-nothing infrastructures, which it is shown that existing open-source solutions fail to scale up; either by large number of points or high dimensionality, whereas Sparx yields scalable and effective performance.

Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems

This paper explores how to utilize a CUDA-based GPU implementation of the k-nearest neighbor algorithm to accelerate LOF classification and achieves more than a 100X speedup over a multi-threaded dual-core CPU implementation.

GPU-Accelerated Feature Selection for Outlier Detection Using the Local Kernel Density Ratio

This work presents a novel non-parametric evaluation criterion for filter-based feature selection which caters to outlier detection problems, and implements the algorithm on a graphics processing unit (GPU) to gain significant speedup over the serial version.

A Tensor Compiler for Unified Machine Learning Prediction Serving

HUMMINGBIRD is a novel approach to model scoring, which compiles featurization operators and traditional ML models into a small set of tensor operations, which inherently reduces infrastructure complexity and directly leverages existing investments in Neural Network compilers and runtimes to generate efficient computations for both CPU and hardware accelerators.

Real-Time Distance-Based Outlier Detection in Data Streams

This study proposes a new algorithm, CPOD, to improve the efficiency of outlier detections while reducing its memory requirements, and shows that with six real-world and one synthetic dataset,CPOD is, on average, 10, 19, and 73 times faster than M_MCOD, NETS, and MCOD, respectively, while consuming low memory.

Parallel processing for distance-based outlier detection on a multi-core CPU

A new parallelization model for the parallel processing of Orca-based outlier detection on a multi-core CPU that utilizes data parallelism and a Multi-thread model and outperforms conventional parallelization models.

Distributed Local Outlier Detection in Big Data

This work presents the first distributed solution for the Local Outlier Factor (LOF) method, and proposes a data assignment strategy which ensures that each machine is self-sufficient in all stages of the LOF pipeline, while minimizing the number of data replicas.

Estimating GPU memory consumption of deep learning models

DNNMem employs an analytic estimation approach to systematically calculate the memory consumption of both the computation graph and the DL framework runtime, and shows that DNNMem is effective in estimating GPU memory consumption.
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