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1. On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines

   https://doi.org/10.1109/RTSS49844.2020.00037  

   Liu, Shengzhong; Yao, Shuochao; Fu, Xinzhe; Tabish, Rohan; Yu, Simon; Bansal, Ayoosh; Yun, Heechul; Sha, Lui; Abdelzaher, Tarek (December 2020, IEEE Real-Time Systems Symposium)

   Chen, Jian-Jia (Ed.)

   The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based cyber-physical applications and develops a scheduling solution to mitigate its effect. In general, priority inversion occurs in real-time systems when computations that are of lower priority are performed together with or ahead of those that are of higher priority. 1 In current machine intelligence software, significant priority inversion occurs on the path from perception to decision-making, where the execution of underlying neural network algorithms does not differentiate between critical and less critical data. We describe a scheduling framework to resolve this problem and demonstrate that it improves the system's ability to react to critical inputs, while at the same time reducing platform cost. 

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2. Dynamic Feature Sharing for Cooperative Perception from Point Clouds

   https://doi.org/10.1109/ITSC57777.2023.10422242  

   Bai, Zhengwei; Wu, Guoyuan; Barth, Matthew J; Liu, Yongkang; Sisbot, Emrah Akin; Oguchi, Kentaro (September 2023, IEEE)

   Perceiving the surrounding environment is critical to enable cooperative driving automation, which is regarded as a transformative solution to improving our transportation system. Cooperative perception, by cooperating information from spatially separated nodes, can innately unlock the bottleneck caused by physical occlusions and has become an important research topic. Although cooperative perception aims to resolve practical problems, most of the current research work is designed based on the default assumption that the communication capacities of collaborated perception entities are identical. In this work, we introduce a fundamental approach - Dynamic Feature Sharing (DFS) - for cooperative perception from a more pragmatic context. Specifically, a DFS-based cooperative perception framework is designed to dynamically reduce the feature data required for sharing among the cooperating entities. Convolution-based Priority Filtering (CPF) is proposed to enable DFS under different communication constraints (e.g., bandwidth) by filtering the feature data according to the designed priority values. Zero-shot experiments demonstrate that the proposed CPF method can improve cooperative perception performance by approximately +22% under a dynamic communication-capacity condition and up to +130% when the communication bandwidth is reduced by 90 %. 

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3. Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations

   https://doi.org/10.1088/1361-6382/ad3279  

   Berbel, Marina; Miravet-Tenés, Miquel; Sharma_Chaudhary, Sushant; Albanesi, Simone; Cavaglià, Marco; Magaña_Zertuche, Lorena; Tseneklidou, Dimitra; Zheng, Yanyan; Coughlin, Michael_W; Toivonen, Andrew (April 2024, Classical and Quantum Gravity)

   Abstract Because of the electromagnetic (EM) radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave (GW) signal with EM telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms,K-nearest neighbors and random forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist EM telescopes in deciding whether to follow up on GW events in real time. 

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4. Responsive Parallelism with Synchronization

   https://doi.org/10.1145/3591249  

   Muller, Stefan_K; Singer, Kyle; Keeney, Devyn_Terra; Neth, Andrew; Agrawal, Kunal; Lee, I-Ting_Angelina; Acar, Umut_A (June 2023, Proceedings of the ACM on Programming Languages)

   Many concurrent programs assign priorities to threads to improve responsiveness. When used in conjunction with synchronization mechanisms such as mutexes and condition variables, however, priorities can lead to priority inversions, in which high-priority threads are delayed by low-priority ones. Priority inversions in the use of mutexes are easily handled using dynamic techniques such as priority inheritance, but priority inversions in the use of condition variables are not well-studied and dynamic techniques are not suitable. In this work, we use a combination of static and dynamic techniques to prevent priority inversion in code that uses mutexes and condition variables. A type system ensures that condition variables are used safely, even while dynamic techniques change thread priorities at runtime to eliminate priority inversions in the use of mutexes. We prove the soundness of our system, using a model of priority inversions based on cost models for parallel programs. To show that the type system is practical to implement, we encode it within the type systems of Rust and C++, and show that the restrictions are not overly burdensome by writing sizeable case studies using these encodings, including porting the Memcached object server to use our C++ implementation. 

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5. A Comparison of End-to-End Decision Forest Inference Pipelines

   https://doi.org/10.1145/3620678.3624656  

   Guan, Hong; Masood, Saif; Dwarampudi, Mahidhar; Gunda, Venkatesh; Min, Hong; Yu, Lei; Nag, Soham; Zou, Jia (October 2023, Proceedings of 2023 ACM Symposium on Cloud Computing (SoCC'23))

   Decision forest, including RandomForest, XGBoost, and Light-GBM, dominates the machine learning tasks over tabular data. Recently, several frameworks were developed for decision forest inference, such as ONNX, TreeLite from Amazon, TensorFlow Decision Forest from Google, HummingBirdfrom Microsoft, Nvidia FIL, and lleaves. While these frameworks are fully optimized for inference computations, they are all decoupled with databases and general data management frameworks, which leads to cross-system performance overheads. We first provided a DICT model to understand the performance gaps between decoupled and in-database inference. We further identified that for in-database inference, in addition to the popular UDF-centric representation that encapsulates the ML into one User Defined Function(UDF), there also exists a relation-centric representation that breaks down the decision forest inference into several fine-grained SQL operations. The relation-centric representation can achieve significantly better performance for large models. We optimized both implementations and conducted a comprehensive benchmark to compare these two implementations to the aforementioned decoupled inference pipelines and existing in-database inference pipelines such as Spark-SQL and PostgresML. The evaluation results validated the DICT model and demonstrated the superior performance of our in-database inference design compared to the baselines. 

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