Over the years, many multiprocessor locking protocols have been designed and analyzed. However, the performance of these protocols highly depends on how the tasks are partitioned and prioritized and how the resources are shared locally and globally. This paper answers a few fundamental questions when realtime tasks share resources in multiprocessor systems. We explore the fundamental difficulty of the multiprocessor synchronization problem and show that a very simplified version of this problem is N P -hard in the strong sense regardless of the number of processors and the underlying scheduling paradigm. Therefore, the allowance of preemption or migration does not reduce the computational complexity. For the positive side, we develop a dependency-graph approach, that is specifically useful for framebased real-time tasks, in which all tasks have the same period and release their jobs always at the same time. We present a series of algorithms with speedup factors between 2 and 3 under semi-partitioned scheduling. We further explore methodologies and tradeoffs of preemptive against non-preemptive scheduling algorithms and partitioned against semi-partitioned scheduling algorithms. The approach is extended to periodic tasks under certain conditions.
The predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$
MODES
that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$
MODES
-B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$
MODES
-I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$
MODES
by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$
MODES
-B), run-time efficiency ($$\textit{MODES}$$
MODES
-I), and statistical stability for both modes, $$\textit{MODES}$$
MODES
outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.
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