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Clustering algorithms are an important part of unsupervised machine learning. With Big Data, applying clustering algorithms such as KMeans has become a challenge due to the significantly larger volume of data and the computational complexity of the standard approach, Lloyd's algorithm. This work aims to tackle this challenge by transforming the classic clustering KMeans algorithm to be highly scalable and to be able to operate on Big Data. We leverage the distributed computing environment of the HPCC Systems platform. The presented KMeans algorithm adopts a hybrid parallelism method to achieve a massively scalable parallel KMeans. Our approach can save a significant amount of time of researchers and machine learning practitioners who train hundreds of models on a daily basis. The performance is evaluated with different size datasets and clusters and the results show a significant scalabilty of the scalable parallel KMeans algorithm. 

Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data related but not limited to scientific discovery. Traditional methods such as Dynamic Topic Modeling (DTM) do not lend themselves well to direct parallelization because of dependencies from one time step to another. In this paper, we introduce and empirically analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting dynamic latent topics from a collection of documents. Our approach is based on data decomposition in which the data is partitioned into segments, followed by topic modeling on the individual segments. The resulting local models are then combined into a global solution using clustering. The decomposition and resulting parallelization leads to very fast runtime even on very large datasets. Our approach furthermore provides insight into how the composition of topics changes over time and can also be applied using other data partitioning strategies over any discrete features of the data, such as geographic features or classes of users. In this paper CLDA is applied successfully to seventeen years of NIPS conference papers (2,484 documents and 3,280,697 words), seventeen years of computer science journal abstracts (533,588 documents and 46,446,184 words), and to forty years of the PubMed corpus (4,025,976 documents and 386,847,695 words). On the PubMed corpus, we demonstrate the versatility of CLDA by segmenting the data by both time and by journal. Our runtime on this corpus demonstrates an ability to function on very large scale datasets. 

In this paper we describe how high performance computing in the Google Cloud Platform can be utilized in an urgent and emergency situation to process large amounts of traffic data efficiently and on demand. Our approach provides a solution to an urgent need for disaster management using massive data processing and high performance computing. The traffic data used in this demonstration is collected from the public camera systems on Interstate highways in the Southeast United States. Our solution launches a parallel processing system that is the size of a Top 5 supercomputer using the Google Cloud Platform. Results show that the parallel processing system can be launched in a few hours, that it is effective at fast processing of high volume data, and can be de-provisioned in a few hours. We processed 211TB of video utilizing 6,227,593 core hours over the span of about eight hours with an average cost of around $0.008 per vCPU hour, which is less than the cost of many on-premise HPC systems. 

In this paper, we study the impacts of latency variation versus latency mean on application runtime, library performance, and packet delivery. Our contributions include the design and implementation of a network latency injector that is suitable for most QLogic and Mellanox InfiniBand cards. We fit statistical distributions of latency mean and variation to varying levels of network contention for a range of parallel application workloads. We use the statistical distributions to characterize the latency variation impacts to application degradation. The level of application degradation caused by variation in network latency depends on application characteristics, and can be significant. Observed degradation varies from no degradation for applications without communicating processes to 3.5 times slower for communication-intensive parallel applications. We support our results with statistical analysis of our experimental observations. For communication-intensive high performance computing applications, we show statistically significant evidence that changes in performance are more highly correlated with changes of variation in network latency than with changes of mean network latency alone.