Maintaining a high rate of productivity, in terms of completed jobs per unit of time, in High-Performance Computing (HPC) facilities is a cornerstone in the next generation of exascale supercomputers. Process malleability is presented as a straightforward mechanism to address that issue. Nowadays, the vast majority of HPC facilities are intended for distributed-memory applications based on the Message Passing (MP) paradigm. For this reason, many efforts are based on the Message Passing Interface (MPI), the de facto standard programming model. Malleability aims to rescale executions on-the-fly, in other words, reconfigure the number and layout of processes in running applications. Process malleability involves resources reallocation within the HPC system, handling processes of the application, and redistributing data among those processes to resume the execution. This manuscript compiles how different frameworks address process malleability, their main features, their integration in resource management systems, and how they may be used in user codes. This paper is a detailed state-of-the-art devised as an entry point for researchers who are interested in process malleability.

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Malleability allows computing facilities to adapt their workloads through resource management systems to maximize the throughput of the facility and the efficiency of the executed jobs. This technique is based on reconfiguring a job to a different resource amount during execution and then continuing with it. One of the stages of malleability is the dynamic spawning of processes in execution time, where different decisions in this stage will affect how the next stage of data redistribution is performed, which is the most time-consuming stage. This paper describes different methods and strategies, defining eight different alternatives to spawn processes dynamically and indicates which one should be used depending on whether a strong or weak scaling application is being used. In addition, it is described for both types of applications which strategies benefit most the application performance or the system productivity. The results show that reducing the number of spawning processes by reusing the older ones can reduce reconfiguration time compared to the classical method by up to 2.6 times for expanding and up to 36 times for shrinking. Furthermore, the asynchronous strategy requires analysing the impact of oversubscription on application performance.

Nowadays, the throughput improvement in large clusters of computers recommends the development of malleable applications. Thus, during the execution of these applications in a job, the resource management system (RMS) can modify its resource allocation, in order to increase the global throughput. There are different alternatives to complete the different steps in which the reallocation of resources is decomposed. To find the best alternatives, this paper introduces a configurable synthetic iterative MPI malleable application capable of modifying, in execution time, the number of MPI processes according to several parameters. The application includes a performance module to measure stages time within steps, from processes management to data redistribution. In this way, the analysis of different scenarios will allow to conclude how the reconfiguration of application has to be made in different circumstances. At the same time, this tool can be used to create workloads that will allow to analyse the impact of malleability on a system and the work in progress.