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1. MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

   https://doi.org/10.1007/978-3-030-34356-9_37  

   Nichols, D.; Tomov, N.; Betancourt, F.; Tomov, S.; Wong, K.; Dongarra, J. (December 2019, Lecture notes in computer science)

   In this paper, we present work towards the development of a new data analytics and machine learning (ML) framework, called MagmaDNN. Our main goal is to provide scalable, high-performance data analytics and ML solutions for scientific applications running on current and upcoming heterogeneous many-core GPU-accelerated architectures. To this end, since many of the functionalities needed are based on standard linear algebra (LA) routines, we designed MagmaDNN to derive its performance power from the MAGMA library. The close integration provides the fundamental (scalable high-performance) LA routines available in MAGMA as a backend to MagmaDNN. We present some design issues for performance and scalability that are specific to ML using Deep Neural Networks (DNN), as well as the MagmaDNN designs towards overcoming them. In particular, MagmaDNN uses well established HPC techniques from the area of dense LA, including task-based parallelization, DAG representations, scheduling, mixed-precision algorithms, asynchronous solvers, and autotuned hyperparameter optimization. We illustrate these techniques and their incorporation and use to outperform other frameworks, currently available. 

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2. Batched one-sided factorizations of tiny matrices using GPUs: Challenges and countermeasures

   Abdelfattahah, Ahmad; Haidar, Azzam; Tomov, Stanimire; Dongarra, Jack (May 2018, Journal of computational science)

   The use of batched matrix computations recently gained a lot of interest for applications, where the same operation is applied to many small independent matrices. The batched computational pattern is frequently encountered in applications of data analytics, direct/iterative solvers and preconditioners, computer vision, astrophysics, and more, and often requires specific designs for vectorization and extreme parallelism to map well on today's high-end many-core architectures. This has led to the development of optimized software for batch computations, and to an ongoing community effort to develop standard interfaces for batched linear algebra software. Furthering these developments, we present GPU design and optimization techniques for high-performance batched one-sided factorizations of millions of tiny matrices (of size 32 and less). We quantify the effects and relevance of different techniques in order to select the best-performing LU, QR, and Cholesky factorization designs. While we adapt common optimization techniques, such as optimal memory traffic, register blocking, and concurrency control, we also show that a different mindset and techniques are needed when matrices are tiny, and in particular, sub-vector/warp in size. The proposed routines are part of the MAGMA library and deliver significant speedups compared to their counterparts in currently available vendor-optimized libraries. Notably, we tune the developments for the newest V100 GPU from NVIDIA to show speedups of up to 11.8×. 

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3. Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies.

   https://doi.org/10.1371/journal.pone.020267x4  

   Marino S, Xu J (January 2018, PloS one)

   The theoretical foundations of Big Data Science are not fully developed, yet. This study proposes a new scalable framework for Big Data representation, high-throughput analytics (variable selection and noise reduction), and model-free inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Data sets. Compressive Big Data analytics (CBDA) iteratively generates random (sub)samples from a big and complex dataset. This subsampling with replacement is conducted on the feature and case levels and results in samples that are not necessarily consistent or congruent across iterations. The approach relies on an ensemble predictor where established model-based or model-free inference techniques are iteratively applied to preprocessed and harmonized samples. Repeating the subsampling and prediction steps many times, yields derived likelihoods, probabilities, or parameter estimates, which can be used to assess the algorithm reliability and accuracy of findings via bootstrapping methods, or to extract important features via controlled variable selection. CBDA provides a scalable algorithm for addressing some of the challenges associated with handling complex, incongruent, incomplete and multi-source data and analytics challenges. Albeit not fully developed yet, a CBDA mathematical framework will enable the study of the ergodic properties and the asymptotics of the specific statistical inference approaches via CBDA. We implemented the high-throughput CBDA method using pure R as well as via the graphical pipeline environment. To validate the technique, we used several simulated datasets as well as a real neuroimaging-genetics of Alzheimer’s disease case-study. The CBDA approach may be customized to provide generic representation of complex multimodal datasets and to provide stable scientific inference for large, incomplete, and multisource datasets. 

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4. Panorama: a data system for unbounded vocabulary querying over video

   https://doi.org/10.14778/3372716.3372721  

   Zhang, Yuhao; Kumar, Arun (December 2019, Proceedings of the VLDB Endowment)

   null (Ed.)

   Deep convolutional neural networks (CNNs) achieve state-of-the-art accuracy for many computer vision tasks. But using them for video monitoring applications incurs high computational cost and inference latency. Thus, recent works have studied how to improve system efficiency. But they largely focus on small "closed world" prediction vocabularies even though many applications in surveillance security, traffic analytics, etc. have an ever-growing set of target entities. We call this the "unbounded vocabulary" issue, and it is a key bottleneck for emerging video monitoring applications. We present the first data system for tacking this issue for video querying, Panorama. Our design philosophy is to build a unified and domain-agnostic system that lets application users generalize to unbounded vocabularies in an out-of-the-box manner without tedious manual re-training. To this end, we synthesize and innovate upon an array of techniques from the ML, vision, databases, and multimedia systems literature to devise a new system architecture. We also present techniques to ensure Panorama has high inference efficiency. Experiments with multiple real-world datasets show that Panorama can achieve between 2x to 20x higher efficiency than baseline approaches on in-vocabulary queries, while still yielding comparable accuracy and also generalizing well to unbounded vocabularies. 

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5. Reproducible and Portable Big Data Analytics in the Cloud

   https://doi.org/10.1109/TCC.2023.3245081  

   Wang, Xin; Guo, Pei; Li, Xingyan; Gangopadhyay, Aryya; Busart, Carl; Freeman, Jade; Wang, Jianwu (January 2023, IEEE Transactions on Cloud Computing)

   Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud. The first is how to automate end-to-end scalable execution of analytics including distributed environment provisioning, analytics pipeline description, parallel execution, and resource termination. The second is that an application developed for one cloud is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automated data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using four big data analytics applications that run on virtual CPU/GPU clusters. The experiments show our toolkit can achieve good execution performance, scalability, and efficient reproducibility for cloud-based big data analytics. 

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