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1. Lightweight Function Monitors for Fine-Grained Management in Large Scale Python Applications

   https://doi.org/10.1109/IPDPS49936.2021.00088  

   Shaffer, Tim; Li, Zhuozhao; Tovar, Ben; Babuji, Yadu; Dasso, TJ; Surma, Zoe; Chard, Kyle; Foster, Ian; Thain, Douglas (May 2021, IEEE International Parallel and Distributed Processing Symposium)

   null (Ed.)

   Python has become a widely used programming language for research, not only for small one-off analyses, but also for complex application pipelines running at supercomputer- scale. Modern parallel programming frameworks for Python present users with a more granular unit of management than traditional Unix processes and batch submissions: the Python function. We review the challenges involved in running native Python functions at scale, and present techniques for dynamically determining a minimal set of dependencies and for assembling a lightweight function monitor (LFM) that captures the software environment and manages resources at the granularity of single functions. We evaluate these techniques in a range of environ- ments, from campus cluster to supercomputer, and show that our advanced dependency management planning and dynamic re- source management methods provide superior performance and utilization relative to coarser-grained management approaches, achieving several-fold decrease in execution time for several large Python applications. 

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2. PyQMC : An all-Python real-space quantum Monte Carlo module in PySCF

   https://doi.org/10.1063/5.0139024  

   Wheeler, William A.; Pathak, Shivesh; Kleiner, Kevin G.; Yuan, Shunyue; Rodrigues, João N. B.; Lorsung, Cooper; Krongchon, Kittithat; Chang, Yueqing; Zhou, Yiqing; Busemeyer, Brian; et al (March 2023, The Journal of Chemical Physics)

   We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions. 

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3. glaciome1D: python code for modeling ice mélange flow, 2024

   https://doi.org/10.18739/A21R6N28Z  

   Amundson, Jason; Robel, Alexander; Burton, Justin; Nissanka, Kavinda (January 2024, NSF Arctic Data Center)

   glaciome1D is a quasi-one-dimensional continuum model for modeling the flow of dense packs of icebergs (ice mélange) found in some fjords. In many respects the model is similar to one-dimensional models of ice streams and ice shelves, except that it uses the nonlocal granular fluidity rheology of Henann and Kamrin (2013). The model was created with the intention of developing coupled glacier-ocean-melange models. This is reflected in the modeling framework, which mimics that used for ice streams and ice shelves (Schoof, 2007). Using the model involves creating an instance of the glaciome class, which contains information on the glacier velocity, viscosity (granular fluidity), and geometry as well as model parameters and various external forcings. The glaciome class includes several basic and easy to use functions, such as: self.diagnostic(), self.prognostic(), self.steadystate(), self.save(). The model physics and numerics are described in detail in Amundson et al. (in press). This data set includes a single python module that includes all of the functions for setting up and running the model, an example script that runs the model, and a conda environment list that contains python modules that the code has been tested on. 

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4. Battery-free MakeCode: Accessible Programming for Intermittent Computing

   https://doi.org/10.1145/3517236  

   Kraemer, Christopher; Guo, Amy; Ahmed, Saad; Hester, Josiah (March 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Hands-on computing has emerged as an exciting and accessible way to learn about computing and engineering in the physical world for students and makers of all ages. Current end-to-end approaches like Microsoft MakeCode require tethered or battery-powered devices like a micro:bit, limiting usefulness and applicability, as well as abdicating responsibility for teaching sustainable practices. Unfortunately, energy harvesting computing devices are usually only programmable by experts and require significant supporting toolchains and knowledge across multiple engineering and computing disciplines to work effectively. This paper bridges the gap between sustainable computing efforts, the maker movement, and novice-focused programming environments with MakeCode-Iceberg, a set of compiler extensions to Microsoft's open-source MakeCode project. The extensions automatically and invisibly transform user code in any language supported (Blocks, JavaScript, Python)into a version that can safely and correctly execute across intermittent power failures caused by unreliable energy harvesting. Determining where, when, and what to save in a checkpoint on limited energy, time, and hardware budget is challenging. We leverage the unique intermediate representation of the MakeCode source-to-source compiler to design and deploy various checkpointing techniques. Our approach allows us to provide, for the first time, a fully web-based and toolchain-free environment to program intermittent computing devices, making battery-free operation accessible to all. We demonstrate new use cases with multiple energy harvesters, peripherals, and application domains: including a Smart Terrarium, Step Counter, and Combination Lock. MakeCode-Iceberg provides sustainable hands-on computing opportunities to a broad audience of makers and learners, democratizing access to energy harvesting and battery-free embedded systems. 

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5. Data Offloading in UAV-assisted Multi-access Edge Computing Systems under Resource Uncertainty

   https://doi.org/10.1109/TMC.2021.3069911  

   Apostolopoulos, Pavlos Athanasios; Fragkos, Georgios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (January 2021, IEEE Transactions on Mobile Computing)

   null (Ed.)

   In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user's satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework's consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users' experienced performance compared to alternative approaches. 

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