In the design of future HPC systems, research in resource management is showing an increasing interest in a more dynamic control of the available resources. It has been proven that enabling the jobs to change the number of computing resources at run time, i.e. their malleability, can significantly improve HPC system performance. However, job schedulers and applications typically do not support malleability due to the common belief that it introduces additional programming complexity and performance impact. This paper presents DROM, an interface that provides efficient malleability with no effort for program developers. The running application is enabled to adapt the number of threads to the number of assigned computing resources in a comple...
In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming m...
Several studies have proved the benefits of job malleability, that is, the capacity of an applicatio...
This work focuses on scheduling of MPI jobs when executing in shared-memory multiprocessors (SMPs). ...
In the design of future HPC systems, research in resource management is showing an increasing intere...
In job scheduling, the concept of malleability has been explored since many years ago. Research show...
Process malleability has proved to have a highly positive impact on the resource utilization and glo...
Maintaining a high rate of productivity, in terms of completed jobs per unit of time, in High-Perfor...
Process malleability has proved to have a highly positive impact on the resource utilization and glo...
In recent years, high-performance computing research became essential in pushing the boundaries of w...
Adaptive workloads can change on–the–fly the configuration of their jobs, in terms of number of pro...
Efficiency is a must in the HPC world. Supercomputers are extensively used in public research instit...
The OpenMP programming model provides parallel applications a very important feature: job malleabili...
Adaptive workloads can change on–the–fly the configuration of their jobs, in terms of number of proc...
As HPC hardware continues to evolve and diversify and workloads become more dynamic and complex, app...
High Performance Computing (HPC) is nowadays a strategic asset required to sustain the surging deman...
In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming m...
Several studies have proved the benefits of job malleability, that is, the capacity of an applicatio...
This work focuses on scheduling of MPI jobs when executing in shared-memory multiprocessors (SMPs). ...
In the design of future HPC systems, research in resource management is showing an increasing intere...
In job scheduling, the concept of malleability has been explored since many years ago. Research show...
Process malleability has proved to have a highly positive impact on the resource utilization and glo...
Maintaining a high rate of productivity, in terms of completed jobs per unit of time, in High-Perfor...
Process malleability has proved to have a highly positive impact on the resource utilization and glo...
In recent years, high-performance computing research became essential in pushing the boundaries of w...
Adaptive workloads can change on–the–fly the configuration of their jobs, in terms of number of pro...
Efficiency is a must in the HPC world. Supercomputers are extensively used in public research instit...
The OpenMP programming model provides parallel applications a very important feature: job malleabili...
Adaptive workloads can change on–the–fly the configuration of their jobs, in terms of number of proc...
As HPC hardware continues to evolve and diversify and workloads become more dynamic and complex, app...
High Performance Computing (HPC) is nowadays a strategic asset required to sustain the surging deman...
In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming m...
Several studies have proved the benefits of job malleability, that is, the capacity of an applicatio...
This work focuses on scheduling of MPI jobs when executing in shared-memory multiprocessors (SMPs). ...