The MIG Framework: Enabling Transparent Process Migration in Open MPI

This paper introduces the mig framework: an Open MPI extension to transparently support the migration of application processes, over different nodes of a distributed High-Performance Computing (HPC) system. The framework provides mechanism on top of which suitable resource managers can implement policies to react to hardware faults, address performance variability, improve resource utilization, perform a fine-grained load balancing and power thermal management. Compared to other state-of-the-art approaches, the mig framework does not require changes in the application code. Moreover, it is highly maintainable, since it is mainly a self-contained solution that has required a very few changes in other already existing Open MPI frameworks. Experimental results have shown that the proposed extension does not introduce significant overhead in the application execution, while the penalty due to performing a migration can be properly taken into account by a resource manager

Runtime resource management for embedded and HPC systems

Resource management is a well known problem in almost every computing system ranging from embedded to High Performance Computing (HPC) and is useful to optimize multiple orthogonal system metrics such as power consumption, performance and reliability. To achieve such an optimization a resource manager must suitably allocate the available system resources - e.g. processing elements, memories and interconnect - to the running applications. This kind of process incurs in two main problems: a) system resources are usually shared between multiple applications and this induces resource contention; and b) each application requires a different Quality of Service, making it harder for the re- source manager to work in an application-agnostic mode. In this scenario, resource management represents a critical and essential component in a computing system and should act at different levels to optimize the whole system while keeping it exible and versatile. In this paper we describe a multi-layer resource management strategy that operates at application, operating system and hardware level and tries to optimize resource allocation on embedded, desktop multi-core and HPC systems

Resource-Aware Application Execution Exploiting the BarbequeRTRM

Energy efficiency and thermal management have become ma- jor concerns in both embedded and HPC systems. The progress of silicon technology and the subsequent growth of the dark silicon phenomena are negatively a ecting the reliability of computing systems. As a result, in the next future we expect run-time variability to increase in terms of both performance and computing resources availability. To address these is- sues, systems and applications must be able to adapt to such scenarios. This work provides a brief overview of the Barbeque Run-Time Resource Manager (BarbequeRTRM) and the application execution model that it exploits, in order to deal with run-time performance and available re- sources variability