Emergency Departments (EDs) are among the most complex areas in healthcare, requiring immediate medical attention for acute and urgent conditions. Optimizing staff configurations to reduce patient Length of Stay (LoS) and improve operational efficiency poses a significant challenge due to the combinatorial and high-dimensional nature of the problem. To identify the most effective staff configuration, we propose a heuristic optimization strategy that is based on the Montecarlo Clustering Search Algorithm (MCSA), which efficiently explores the multidimensional solution space. MCSA leverages an agent-based simulation (ABM) model that evaluates each proposed staff configuration under realistic operational conditions, providing Key Performance Indicator (KPI) feedback values related to each proposed staff configuration. Through this strategy, we explore staff configurations capable of handling patient volumes with varying acuity levels in an ED to optimize the LoS KPI. Results demonstrate that our methodology is capable to find a solution as a staff configuration that reduces LoS compared to a baseline, offering a computationally efficient and practical tool for decision-makers. We identified solutions by exploring less than 1% of the total search space, demonstrating the efficiency of the proposed approach in addressing complex optimization problems. This approach supports informed planning in healthcare environments while maintaining system feasibility and scalability.

Abstract The analysis of parallel scientific applications allows us to understand their computational and communication behavior. One way of obtaining performance information is through performance tools. One such tool is parallel application signatures for performance prediction (PAS2P), based on parallel application repeatability, focusing on performance analysis and prediction. The same resources that execute the parallel application are used to perform its analysis, creating a machine independent model of the application and identifying its common patterns. However, the analysis is costly in terms of execution time due to the high number of synchronization communications performed by PAS2P, degrading performance as the number of processes increases. To solve this problem, we propose a model that reduces data dependency between processes, reducing the number of communications performed by PAS2P in the analysis stage and taking advantage of the characteristics of single program, multiple sata applications. Our analysis proposal allows us to decrease the analysis time by 29 times when the application scales to 256 processes, while keeping error levels below 11% in the runtime prediction. It is important to mention that the analysis time is not considerably affected by increasing the number of application processes.

Compute node failures are becoming a normal event for many long-running and scalable MPI applications. Keeping within the MPI standards and applying some of the methods developed so far in terms of fault tolerance, we developed a methodology that allows applications to tolerate failures through the creation of semi-coordinated checkpoints within the RADIC architecture. To do this, we developed the ULSC2-RADIC middleware that divides the application into independent MPI worlds where each MPI world would correspond to a compute node and make use of the DMTCP checkpoint library in a semi-coordinated environment. We performed experimental results using scientific applications and the NAS Parallel Benchmarks to assess the overhead and also the functionality in case of a node failure. We evaluated the computational cost of the semi-coordinated checkpoints compared with the coordinated checkpoints.

In this paper, a new method for prediction of future performance and demand on emergency department (ED) in Spain is presented. Increased life expediency and population aging in Spain, along with their corresponding health conditions such as non-communicable diseases (NCDs), have been suggested to contribute to higher demands on ED. These lead to inferior performance of the department and cause longer ED length of stay (LoS). Prediction and quantification of behavior of ED is, however, challenging as ED is one of the most complex parts of hospitals. Using detailed computational approaches integrated with clinical data behavior of Spain's ED in future years was predicted. First, statistical models were developed to predict how the population and age distribution of patients with non-communicable diseases change in Spain in future years. Then, an agent-based modeling approach was used for simulation of the emergency department to predict impacts of the changes in population and age distribution of patients with NCDs on the performance of ED, reflected in ED LoS, between years 2019 and 2039. Results from different projection scenarios indicated that Spain would experience a continuous increase in total ED LoS from 5.7 million hours in 2019 to 6.2 million hours in 2039 if same human and physical resources, as well as same ED configuration, are used. The results from this study can provide health care provider with quantitative information on required staff and physical resources in the future and allow health care policymakers to improve modifiable factors contributing to the demand and performance of ED.

Executing message-passing parallel applications on a large number of resources in an efficient way is not a trivial task. Due to the complex interaction between the parallel applications and the HPC system, many applications may suffer performance inefficiencies when they scale. To achieve an efficient use of these large-scale systems using thousands of cores, a point to consider before executing an application is to know its behavior in the system. In this work, we propose a novel methodology called P3S (Prediction of Parallel Program Scalability), which allows us to analyze and predict the scalability of message-passing applications on a given system. The methodology strives to use a bounded analysis time, and a reduced set of resources to predict the application behavior for large-scale. The experimental validation proves that the P3S is able to predict the application scalability with an average accuracy greater than 95 percent using a reduced set of resources.

Predicting the performance of parallel scientific applications is becoming increasingly complex. Our goal was to characterize the behavior of message-passing applications on different target machines. To achieve this goal, we developed a method called parallel application signature for performance prediction (PAS2P), which strives to describe an application based on its behavior. Based on the application's message-passing activity, we identified and extracted representative phases, with which we created a parallel application signature that enabled us to predict the application's performance. We experimented with using different scientific applications on different clusters. We were able to predict execution times with an average accuracy greater than 97 percent.

In this paper we propose a methodology that allows us to predict the application scalability behavior in a specific system, providing information to select the most appropriate resources to run the application. We explain the general methodology, focusing on the presentation of a novel method to model the logical application trace for a large number of processes. This method is based on the projection of a set of executions of the application signature for a small number of processes. The generated traces are validated by comparing them with the real traces obtained with PAS2P tool. We present the experimental validation for the BT Nas Parallel Benchmark. The signatures for 16, 36, 64, 81 and 100 processes were executed and used to model and project the logical trace for 1024 processes. The results obtained show the accuracy of the method. The communication pattern was predicted without error, while the predicted error is less than 10% for the communication volume and less than 5% for the number of instructions.

Being able to accurately estimate how an application will perform in a specific computational system provides many useful benefits and can result in smarter decisions. In this work we present a novel approach to model the behavior of message passing parallel applications. Based in the concept of signatures, which are the most relevant parts of an application (phases), we are able to build a model that allows us to predict the application execution time in different systems with variable input data size. Executing these signatures with different input data sizes defines a program's behavior partial function. Using regression we can generalize this behavior function to predict an application performance in a target system with other input data size within a predefined range. We explain our methodology and in order to validate the proposal present results using a synthetic program and well known applications.

Performance prediction and application behavior modeling have been the subject of extensive research that aims to estimate applications performance with acceptable precision. In this paper we present a novel approach to model the behavior of message passing parallel applications. There are many dimensions to consider while predicting a deterministic application behavior. Two dimensions that affect an application performance are the computational resources available and the size of its input data used in the computation. Based on the concept of signatures, we are able to build a model that allows us to predict applications execution time in different systems with variable input data size within a predefined range. Our approach generates signatures, which consist of the most relevant parts of an application (phases). Executing these phases for different workloads partially defines a program's behavior function. By using regression analysis we are able to generalize this behavior function to predict an application performance in a target system with any input data size within a predefined range. We explain our methodology and in order to validate the proposal, we present results using a synthetic program and well-known applications. We were able to estimate the total execution time for a input data size range with an average error of 4 % executing, at most, three signatures that represent less than the 10 % of the total application execution time.

Predicting performance of parallel applications is becoming increasingly complex and the best performance predictor is the application itself, but the time required to run it thoroughly is a onerous requirement. We seek to characterize the behavior of message-passing applications on different systems by extracting a signature which will allow us to predict what system will allow the application to perform best. To achieve this goal, we have developed a method we called Parallel Application Signatures for Performance Prediction (PAS2P) that strives to describe an application based on its behavior. Based on the application's message-passing activity, we have been able to identify and extract representative phases, with which we created a Parallel Application Signature that has allowed us to predict the application's performance. We have experimented with different signature-extraction algorithms and found a reduction in the prediction error using different scientific applications on different clusters. We were able to predict execution times with an average accuracy of over 98%.

We seek to achieve characterization or application signature from a parallel application that will allow us, through the execution of this signature, to evaluate its performance in different computers. Sequential applications behavior can be understood by means of tools such as SimPoint. This tool can identify and select significant phases describing the applications behavior. Our proposal is to extend those concepts towards parallel applications, with the goal of modeling and predicting the parallel application. To achieve this, we developed a methodology, enabling us to identify and extract repetitive behavior to create the application signature. We have validated our proposal using scientific applications such as the NAS Parallel Benchmarks, Sweep3D. We could predict the execution time of the entire application.