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1. Regular Composite Resource Partitioning and Reconfiguration in Open Systems

   https://doi.org/10.1145/3609424  

   Chen, Wei-Ju; Wu, Peng; Huang, Pei-Chi; Mok, Aloysius K; Han, Song (September 2023, ACM Transactions on Embedded Computing Systems)

   We consider the problem of resource provisioning for real-time cyber-physical applications in an open system environment where there does not exist a global resource scheduler that has complete knowledge of the real-time performance requirements of each individual application that shares the resources with the other applications. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically partition and assign various resource slices among such applications. However, previous work on RRP model only discusses uniform resource environment, where resources are implicitly assumed to be synchronized and clocked at the same frequency. The challenge is that a task utilizing multiple resources may experience unexpected delays in non-uniform environments, where resources are clocked at different frequencies. This paper extends the RRP model to non-uniform multi-resource open system environments to tackle this problem. It first introduces a novel composite resource partition abstraction and then proposes algorithms to construct and reconfigure the composite resource partitions. Specifically, theAcyclic Regular Composite Resource Partition Scheduling (ARCRP-S)algorithm constructs regular composite resource partitions and theAcyclic Regular Composite Resource Partition Dynamic Reconfiguration (ARCRP-DR)algorithm reconfigures the composite resource partitions in the run time upon requests of partition configuration changes. Our experimental results show that compared with state-of-the-art methods, ARCRP-S can prevent unexpected resource supply shortfall and improve the schedulability up to 50%. On the other hand, ARCRP-DR can guarantee the resource supply during the reconfiguration with moderate computational overhead. 

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2. Ferroelectric FET-based context-switching FPGA enabling dynamic reconfiguration for adaptive deep learning machines

   https://doi.org/10.1126/sciadv.adk1525  

   Xu, Yixin; Zhao, Zijian; Xiao, Yi; Yu, Tongguang; Mulaosmanovic, Halid; Kleimaier, Dominik; Duenkel, Stefan; Beyer, Sven; Gong, Xiao; Joshi, Rajiv; et al (January 2024, Science Advances)

   Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)–based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively. 

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3. Performance Implication of Tensor Irregularity and Optimization for Distributed Tensor Decomposition

   https://doi.org/10.1145/3580315  

   Miao, Zheng; Calhoun, Jon C; Ge, Rong; Li, Jiajia (June 2023, ACM Transactions on Parallel Computing)

   Tensors are used by a wide variety of applications to represent multi-dimensional data; tensor decompositions are a class of methods for latent data analytics, data compression, and so on. Many of these applications generate large tensors with irregular dimension sizes and nonzero distribution. CANDECOMP/PARAFAC decomposition (Cpd) is a popular low-rank tensor decomposition for discovering latent features. The increasing overhead on memory and execution time ofCpdfor large tensors requires distributed memory implementations as the only feasible solution. The sparsity and irregularity of tensors hinder the improvement of performance and scalability of distributed memory implementations. While previous works have been proved successful inCpdfor tensors with relatively regular dimension sizes and nonzero distribution, they either deliver unsatisfactory performance and scalability for irregular tensors or require significant time overhead in preprocessing. In this work, we focus on medium-grained tensor distribution to address their limitation for irregular tensors. We first thoroughly investigate through theoretical and experimental analysis. We disclose that the main cause of poorCpdperformance and scalability is the imbalance of multiple types of computations and communications and their tradeoffs; and sparsity and irregularity make it challenging to achieve their balances and tradeoffs. Irregularity of a sparse tensor is categorized based on two aspects: very different dimension sizes and a non-uniform nonzero distribution. Typically, focusing on optimizing one type of load imbalance causes other ones more severe for irregular tensors. To address such challenges, we propose irregularity-aware distributedCpdthat leverages the sparsity and irregularity information to identify the best tradeoff between different imbalances with low time overhead. We materialize the idea with two optimization methods: the prediction-based grid configuration and matrix-oriented distribution policy, where the former forms the global balance among computations and communications, and the latter further adjusts the balances among computations. The experimental results show that our proposed irregularity-aware distributedCpdis more scalable and outperforms the medium- and fine-grained distributed implementations by up to 4.4 × and 11.4 × on 1,536 processors, respectively. Our optimizations support different sparse tensor formats, such as compressed sparse fiber (CSF), coordinate (COO), and Hierarchical Coordinate (HiCOO), and gain good scalability for all of them. 

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4. Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

   https://doi.org/10.1145/3570616  

   Kadota, Igor; Jacoby, Dror; Messer, Hagit; Zussman, Gil; Ostrometzky, Jonatan (December 2022, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances. 

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5. Performance of the HAWC Observatory and TeV Gamma-Ray Measurements of the Crab Nebula with Improved Extensive Air Shower Reconstruction Algorithms

   https://doi.org/10.3847/1538-4357/ad5f2d  

   Albert, A; Alfaro, R; Alvarez, C; Andrés, A; Arteaga-Velázquez, J C; Avila_Rojas, D; Ayala_Solares, H A; Babu, R; Belmont-Moreno, E; Bernal, A; et al (September 2024, The Astrophysical Journal)

   Abstract The High-Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory, located on the side of the Sierra Negra volcano in Mexico, has been fully operational since 2015. The HAWC collaboration has recently significantly improved their extensive air shower reconstruction algorithms, which has notably advanced the observatory performance. The energy resolution for primary gamma rays with energies below 1 TeV was improved by including a noise-suppression algorithm. Corrections have also been made to systematic errors in direction fitting related to the detector and shower plane inclinations, $\mathcal{O}(0.°1)$biases in highly inclined showers, and enhancements to the core reconstruction. The angular resolution for gamma rays approaching the HAWC array from large zenith angles (>37°) has improved by a factor of 4 at the highest energies (>70 TeV) as compared to previous reconstructions. The inclusion of a lateral distribution function fit to the extensive air shower footprint on the array to separate gamma-ray primaries from cosmic-ray ones based on the resultingχ²values improved the background rejection performance at all inclinations. At large zenith angles, the improvement in significance is a factor of 4 compared to previous HAWC publications. These enhancements have been verified by observing the Crab Nebula, which is an overhead source for the HAWC Observatory. We show that the sensitivity to Crab-like point sources (E^−2.63) with locations overhead to 30° zenith is comparable to or less than 10% of the Crab Nebula’s flux between 2 and 50 TeV. Thanks to these improvements, HAWC can now detect more sources, including the Galactic center. 

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