FLEX-MPI: An MPI Extension for Supporting Dynamic Load Balancing on Heterogeneous Non-dedicated Systems

Martín, Gonzalo; Marinescu, Maria-Cristina; Singh, David E.; Carretero, Jesús

doi:10.1007/978-3-642-40047-6_16

Cited by 36 publications

(23 citation statements)

References 11 publications

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“…Another similar approach is found in [14], where a performance-aware framework based on the Flex-MPI library [15] is presented. That work leverages job reconfiguration in order to expand/shrink a job targeting execution performance.…”

Section: Related Workmentioning

confidence: 99%

Efficient Scalable Computing through Flexible Applications and Adaptive Workloads

Iserte

Mayo

Quintana‐Ortí

et al. 2017

2017 46th International Conference on Parallel Processing Workshops (ICPPW)

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Abstract-In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming model runtime in collaboration with the global resource manager. We improve the system throughput by exploiting malleability techniques (in terms of number of MPI ranks) through the reallocation of resources assigned to a job during its execution. In our proposal, the OmpSs runtime reconfigures the number of MPI ranks during the execution of an application in cooperation with the Slurm workload manager. In addition, we take advantage of OmpSs offload semantics to allow application developers deal with data redistribution. By combining these elements a job is able to expand itself in order to exploit idle nodes or be shrunk if other queued jobs could be initiated. This novel approach adapts the system workload in order to increase the throughput as well as make a smarter use of the underlying resources. Our experiments demonstrate that this approach can reduce the total execution time of a practical workload by more than 40% while reducing the amount of resources by 30%.

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Section: Related Workmentioning

confidence: 99%

Efficient Scalable Computing through Flexible Applications and Adaptive Workloads

Iserte

Mayo

Quintana‐Ortí

et al. 2017

2017 46th International Conference on Parallel Processing Workshops (ICPPW)

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“…The monitoring can be selectively activated in order to obtain application-related performance metrics like execution time as well as hardware-counter information such as FLOPS and cache misses. Dynamic load balance permits the distribution of the application workload based on the performance of each compute-node and takes into account whether the CPU resources are exclusive or shared between different applications [7]. By means of malleability, FlexMPI controller can dynamically send commands to create or destroy the applications processes and, in the case of creation, allocate them to certain compute nodes.…”

Section: Software and Management Toolsmentioning

confidence: 99%

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

2019

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This work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform’s compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.

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“…Flex-MPI integrates a dynamic load balancing technique [34] for SPMD applications that uses the performance metrics collected by the monitoring functionality to make work-load distribution decisions. One of the main advantages of this approach is that it does not require prior knowledge about the underlying architecture.…”

Section: Load Balancingmentioning

confidence: 99%

“…In a non-dedicated system [38] the computing resources are shared between different user applications which may come and go, and can have irregular execution patterns. Our previous work [34] provides more details and a practical evaluation of the dynamic load balancing algorithm.…”

Section: Load Balancingmentioning

confidence: 99%

Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration

et al. 2015

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The work in this paper focuses on providing malleability to MPI applications by using a novel performance-aware dynamic reconfiguration technique. This paper describes the design and implementation of Flex-MPI, an MPI library extension which can automatically monitor and predict the performance of applications, balance and redistribute the workload, and reconfigure the application at runtime by changing the number of processes. Unlike existent approaches, our reconfiguring policy is guided by user-defined performance criteria. We focus on iterative SPMD programs, a class of applications with critical mass within the scientific community. Extensive experiments show that Flex-MPI can improve the performance, parallel efficiency, and cost-efficiency of MPI programs with a minimal effort from the programmer.

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FLEX-MPI: An MPI Extension for Supporting Dynamic Load Balancing on Heterogeneous Non-dedicated Systems

Cited by 36 publications

References 11 publications

Efficient Scalable Computing through Flexible Applications and Adaptive Workloads

Efficient Scalable Computing through Flexible Applications and Adaptive Workloads

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration

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