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1. Simulating 3-D Stellar Hydrodynamics using PPM and PPB Multifluid Gas Dynamics on CPU and CPU+GPU Nodes

   https://doi.org/https://doi.org/10.1088/1742-6596/1225/1/012020  

   Woodward, Paul R. ; Lin, Pei-Hung ; Mao, Huaqing ; Andrassy, Robert ; Herwig, Falk ( June 2019 , Journal of physics. Conference series)

   The special computational challenges of simulating 3-D hydrodynamics in deep stellar interiors are discussed, and numerical algorithmic responses described. Results of recent simulations carried out at scale on the NSF's Blue Waters machine at the University of Illinois are presented, with a special focus on the computational challenges they address. Prospects for future work using GPU-accelerated nodes such as those on the DoE's new Summit machine at Oak Ridge National Laboratory are described, with a focus on numerical algorithmic accommodations that we believe will be necessary. 

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2. Learning-Based CPU Power Modeling

   https://doi.org/10.1109/MLCAD48534.2019.9142100  

   Kumar, Ajay Krishna ; Gerstlauer, Andreas ( September 2019 , ACM/IEEE Workshop on Machine Learning for CAD (MLCAD))
3. Phase-Aware CPU Workload Forecasting

   Alcorta, Erika S. ; Rama, Pranav ; Aswin, Ramachandran ; Gerstlauer, Andreas ( January 2021 , International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS))

   null (Ed.)

   Predicting workload behavior during execution is essential for dynamic resource optimization of processor systems. Early studies used simple prediction algorithms such as a history tables. More recently, researchers have applied advanced machine learning regression techniques. Workload prediction can be cast as a time series forecasting problem. Time series forecasting is an active research area with recent advances that have not been studied in the context of workload prediction. In this paper, we first perform a comparative study of representative time series forecasting techniques to predict the dynamic workload of applications running on a CPU. We adapt state-of-the-art matrix profile and dynamic linear models (DLMs) not previously applied to workload prediction and compare them against traditional SVM and LSTM models that have been popular for handling non-stationary data. We find that all time series forecasting models struggle to predict abrupt workload changes. These changes occur because workloads go through phases, where prior work has studied workload phase detection, classification and prediction. We propose a novel approach that combines time series forecasting with phase prediction. We process each phase as a separate time series and train one forecasting model per phase. At runtime, forecasts from phase-specific models are selected and combined based on the predicted phase behavior. We apply our approach to forecasting of SPEC workloads running on a state-of-the-art Intel machine. Our results show that an LSTM-based phase-aware predictor can forecast workload CPI with less than 8% mean absolute error while reducing CPI error by more than 12% on average compared to a non-phase-aware approach. 

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4. The Case of Unsustainable CPU Affinity

   https://doi.org/10.1145/3604930.3605706  

   Jiechen Zhao ; Katie Lim ; Thomas Anderson ; Natalie Enright Jerger ( July 2023 , Proc. 2nd ACM Workshop on Hot Topics in Sustainable Computing Systems (HotCarbon’23)

   CPU affinity reduces data copies and improves data locality and has become a prevalent technique for high-performance programs in datacenters. This paper explores the tension between CPU affinity and sustainability. In particular, affinity settings can lead to significant uneven aging of cores on a CPU. We observe that infrastructure threads, used in a wide spectrum of network, storage, and virtualization sub-systems, exercise their affinitized cores up to 23× more when compared to typical 𝜇s-scale application threads. In addition, we observe that the affinitized infrastructure threads generate regional heat hot spots and preclude CPUs from being used with the expected lifetime. Finally, we discuss design options to tackle the unbalanced core-aging problem to improve the overall sustainability of CPUs and call for more attention to sustainabilityaware affinity and mitigation of such problems. 

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5. WHISTLE: CPU Abstractions for Hardware and Software Memory Safety Invariants

   https://doi.org/10.1109/TC.2022.3180990  

   Kim, Sungkeun ; Mahmud, Farabi ; Huang, Jiayi ; Majumder, Pritam ; Tsai, Chia-Che ; Muzahid, Abdullah ; Kim, Eun Jung ( June 2022 , IEEE Transactions on Computers)

   Memory safety invariants extracted from a program can help defend and detect against both software and hardware memory violations. For instance, by allowing only specific instructions to access certain memory locations, system can detect out-of-bound or illegal pointer dereferences that lead to correctness and security issues. In this paper, we propose CPU abstractions, called, to specify and check program invariants to provide defense mechanism against both software and hardware memory violations at runtime. ensures that the invariants must be satisfied at every memory accesses. We present a fast invariant address translation and retrieval scheme using a specialized cache. It stores and checks invariants related to global, stack and heap objects. The invariant checks can be performed synchronously or asynchronously. uses synchronous checking for high security-critical programs, while others are protected by asynchronous checking. A fast exception is proposed to alert any violations as soon as possible in order to close the gap for transient attacks. Our evaluation shows that can detect both software and hardware, spatial and temporal memory violations. incurs 53% overhead when checking synchronously, or 15% overhead when checking asynchronously. 

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