Learning based Memory Interference Prediction for Co-running Applications on Multi-Cores

Saeed, Ahsan; Mueller-Gritschneder, Daniel; Rehm, Falk; Hamann, Arne; Ziegenbein, Dirk; Schlichtmann, Ulf; Gerstlauer, Andreas

doi:10.1109/mlcad52597.2021.9531245

Cited by 2 publications

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Masola,

Capodieci,

Rouxel

et al. 2023

2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

View full text Add to dashboard Cite

Nowadays, heterogeneous embedded platforms are extensively used in various low-latency applications, including the automotive industry, real-time IoT systems, and automated factories. These platforms utilize specific components, such as CPUs, GPUs, and neural network accelerators for efficient task processing and to solve specific problems with a lower power consumption compared to more traditional systems. However, since these accelerators share resources such as the global memory, it is crucial to understand how workloads behave under high computational loads to determine how parallel computational engines on modern platforms can interfere and adversely affect the system's predictability and performance. One area that remains unclear is the interference effect on shared memory resources between the CPU and GPU: more specifically, the latency degradation experienced by GPU kernels when memory-intensive CPU applications run concurrently. In this work, we first analyze the metrics that characterize the behavior of different kernels under various board conditions caused by CPU memory-intensive workloads on a Nvidia Jetson Xavier. Then, we exploit various machine learning methodologies aiming to estimate the latency degradation of kernels based on their metrics. As a result of this, we are able to identify the metrics that could potentially have the most significant impact when predicting the kernels completion latency degradation.

show abstract

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Masola,

Capodieci,

Rouxel

et al. 2023

2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

View full text Add to dashboard Cite

show abstract

Learning-based Phase-aware Multi-core CPU Workload Forecasting

Alcorta

Gerstlauer

2022

ACM Trans. Des. Autom. Electron. Syst.

View full text Add to dashboard Cite

Predicting workload behavior during workload execution is essential for dynamic resource optimization in multi-processor systems. Recent studies have proposed advanced machine learning techniques for dynamic workload prediction. Workload prediction can be cast as a time series forecasting problem. However, traditional forecasting models struggle to predict abrupt workload changes. These changes occur because workloads are known to go through phases. Prior work has investigated machine learning-based approaches for phase detection and prediction, but such approaches have not been studied in the context of dynamic workload forecasting. In this paper, we propose phase-aware CPU workload forecasting as a novel approach that applies long-term phase prediction to improve the accuracy of short-term workload forecasting. Phase-aware forecasting requires machine learning models for phase classification, phase prediction, and phase-based forecasting that have not been explored in this combination before. Furthermore, existing prediction approaches have only been studied in single-core settings. This work explores phase-aware workload forecasting with multi-threaded workloads running on multi-core systems. We propose different multi-core settings differentiated by the number of cores they access and whether they produce specialized or global outputs per core. We study various advanced machine learning models for phase classification, phase prediction, and phase-based forecasting in isolation and different combinations for each setting. We apply our approach to forecasting of multi-threaded Parsec and SPEC workloads running on an 8-core Intel Core-i9 platform. Our results show that combining GMM clustering with LSTMs for phase prediction and phase-based forecasting yields the best phase-aware forecasting results. An approach that uses specialized models per core achieves an average error of 23% with up to 22% improvement in prediction accuracy compared to a phase-unaware setup.

show abstract

Learning based Memory Interference Prediction for Co-running Applications on Multi-Cores

Cited by 2 publications

References 10 publications

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Learning-based Phase-aware Multi-core CPU Workload Forecasting

Contact Info

Product

Resources

About