Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.

Machine learning has seen tremendous growth in recent years thanks to two key advances in technology: massive data generation and highly-parallel accelerator architectures. The rate that data is being generated is exploding across multiple domains, including medical research, environmental science, web-search, and e-commerce. Many of these advances have benefited from emergent web-based applications, and improvements in data storage and sensing technologies. Innovations in parallel accelerator hardware, such as GPUs, has made it possible to process massive amounts of data in a timely fashion. Given these advanced data acquisition technology and hardware, machine learning researchers are equipped to generate and sift through much larger and complex datasets quickly. In this work, we focus on accelerating Kernel Dimension Alternative Clustering algorithms using GPUs. We conduct a thorough performance analysis by using both synthetic and real-world datasets, while also modifying both the structure of the data, and the size of the datasets. Our GPU implementation reduces execution time from minutes to seconds, which enables us to develop a web-based application for users to, interactively, view alternative clustering solutions.

In recent years, the availability of data-driven analytics has become a key tool in discovery in public health and environmental science research. As a result, these communities have looked to leverage recent advances in machine learning algorithms. This class of algorithms are able to find hidden patterns and develop new knowledge in complex data, accelerating the rate of discovery in multiple research domains. In this paper, we present our methodology of applying machine learning algorithms to health outcomes, chemical exposures, and social behavior data from expectant mothers, as part of the NIEHS-supported PROTECT Center. The ultimate goal is to determine the dominant factors/features potentially responsible for the high rate of premature births in Puerto Rico.Many commonly-used machine learning algorithms can be used for feature selection. However, given the imbalance in our birth outcome data, with many more term (i.e., 37 weeks or longer) versus preterm pregnancies (i.e., less than 37 weeks), analysis of the PROTECT dataset presents many unique challenges. In addition to outcome imbalance, our database contains both quantitative and categorical data variables, adding some complexity to the analytical methods used. Applying straightforward correlation or regression analysis would be insufficient. Our datasets also contain a significant amount of missing data (incomplete records), providing noisy input to our algorithms. A further challenge is that we are working with a relatively limited set of complex data (only 2000 participants to date), so our models must be able to be built with a relatively small number of data samples.To overcome these challenges, we have implemented a cus-tomized end-to-end analytical toolchain which forms a pre-processing pipeline. Our framework performs general data filtering and handles missing data fields using a similarity-based approach. Next, we apply one of a number of different machine learning algorithms, including Linear Correlation, Normalized Mutual Information, Logistic Regression, and Decision Trees. We use these during both feature selection and model performance evaluation. Finally, we present top-ranked features produced by our model as potential key contributors of high preterm birth rates in Puerto Rico, and discuss results across these algorithms.

GPUs have become the primary choice of accelerators for high-end data centers and cloud servers, which can host thousands of disparate applications. With the growing demands for GPUs on clusters, there arises a need for efficient co-execution of applications on the same accelerator device. However, the resource contention among co-executing applications causes interference which leads to degradation in execution performance, impacts QoS requirements of applications and lowers overall system throughput. While previous work has proposed techniques for detecting interference, the existing solutions are either developed for CPU clusters, or use static profiling approaches which can be computationally intensive and do not scale well. We present Mystic, an interference-aware scheduler for efficient co-execution of applications on GPU-based clusters and cloud servers. The most important feature of Mystic is the use of learning-based analytical models for detecting interference between applications. We leverage a collaborative filtering framework to characterize an incoming application with respect to the interference it may cause when co-executing with other applications while sharing GPU resources. Mystic identifies the similarities between new applications and the executing applications, and guides the scheduler to minimize the interference and improve system throughput. We train the learning model with 42 CUDA applications, and consider another separate set of 55 diverse, real-world GPU applications for evaluation. Mystic is evaluated on a live GPU cluster with 32 NVIDIA GPUs. Our framework achieves performance guarantees for 90.3% of the evaluated applications. When compared with state-of-the art interference-oblivious schedulers, Mystic improves the system throughput by 27.5% on average, and achieves a 16.3% improvement on average in GPU utilization.