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1. Building and steering binned template fits with cabinetry

   https://doi.org/10.1051/epjconf/202125103067  

   Cranmer, Kyle; Held, Alexander (January 2021, EPJ Web of Conferences)

   Biscarat, C.; Campana, S.; Hegner, B.; Roiser, S.; Rovelli, C.I.; Stewart, G.A. (Ed.)

   The cabinetry library provides a Python-based solution for building and steering binned template fits. It tightly integrates with the pythonic High Energy Physics ecosystem, and in particular with pyhf for statistical inference. cabinetry uses a declarative approach for building statistical models, with a JSON schema describing possible configuration choices. Model building instructions can additionally be provided via custom code, which is automatically executed when applicable at key steps of the workflow. The library implements interfaces for performing maximum likelihood fitting, upper parameter limit determination, and discovery significance calculation. cabinetry also provides a range of utilities to study and disseminate fit results. These include visualizations of the fit model and data, visualizations of template histograms and fit results, ranking of nuisance parameters by their impact, a goodness-of-fit calculation, and likelihood scans. The library takes a modular approach, allowing users to include some or all of its functionality in their workflow. 

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2. Hierarchical computing for hierarchical models in ecology

   https://doi.org/10.1111/2041-210X.13513  

   McCaslin, Hanna M.; Feuka, Abigail B.; Hooten, Mevin B.; O'Hara, ed., Robert B. (November 2020, Methods in Ecology and Evolution)

   Abstract Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade‐offs between capturing ecological complexity in statistical models and implementing these models.We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation‐assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual‐ and population‐level migratory characteristics.Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm.For complex ecological statistical models, like those for animal movement, multi‐species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation‐assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena. 

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3. Swift machine learning model serving scheduling: a region based reinforcement learning approach

   https://doi.org/10.1145/3295500.3356164  

   Qin, Heyang; Zawad, Syed; Zhou, Yanqi; Yang, Lei; Zhao, Dongfang; Yan, Feng (November 2019, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'19))

   The success of machine learning has prospered Machine-Learning-as-a-Service (MLaaS) - deploying trained machine learning (ML) models in cloud to provide low latency inference services at scale. To meet latency Service-Level-Objective (SLO), judicious parallelization at both request and operation levels is utterly important. However, existing ML systems (e.g., Tensorflow) and cloud ML serving platforms (e.g., SageMaker) are SLO-agnostic and rely on users to manually configure the parallelism. To provide low latency ML serving, this paper proposes a swift machine learning serving scheduling framework with a novel Region-based Reinforcement Learning (RRL) approach. RRL can efficiently identify the optimal parallelism configuration under different workloads by estimating performance of similar configurations with that of the known ones. We both theoretically and experimentally show that the RRL approach can outperform state-of-the-art approaches by finding near optimal solutions over 8 times faster while reducing inference latency up to 79.0% and reducing SLO violation up to 49.9%. 

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4. PERSEUS: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models

   LeMay, Matthew; Li, Shijian; Guo, Tian (May 2020, 2020 IEEE International Conference on Cloud Engineering (IC2E))

   Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accommodate high inference throughput, it is common to host a single pre-trained Convolutional Neural Network (CNN) in dedicated cloud-based servers with hardware accelerators such as Graphics Processing Units (GPUs). However, GPUs can be orders of magnitude more expensive than traditional Central Processing Unit (CPU) servers. These resources could also be under-utilized facing dynamic workloads, which may result in inflated serving costs. One potential way to alleviate this problem is by allowing hosted models to share the underlying resources, which we refer to as multi-tenant inference serving. One of the key challenges is maximizing the resource efficiency for multi-tenant serving given hardware with diverse characteristics, models with unique response time Service Level Agreement (SLA), and dynamic inference workloads. In this paper, we present PERSEUS, a measurement framework that provides the basis for understanding the performance and cost trade-offs of multi-tenant model serving. We implemented PERSEUS in Python atop a popular cloud inference server called Nvidia TensorRT Inference Server. Leveraging PERSEUS, we evaluated the inference throughput and cost for serving various models and demonstrated that multi-tenant model serving led to up to 12% cost reduction. 

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5. CyberGIS-compute for enabling computationally intensive geospatial research

   https://doi.org/10.1145/3486189.3490017  

   Padmanabhan, Anand; Ziao, Ximo; Vandewalle, Rebecca C.; Baig, Furqan; Michel, Alexander; Li, Zhiyu; Wang, Shaowen (November 2021, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science (SpatialAPI’21))

   Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGISJupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework. 

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