Dynamic Load-Balancing with Variable Number of Processors based on Graph Repartitioning

Dynamic load balancing is an important step conditioning the performance of parallel adaptive codes whose load evolution is difficult to predict. Most of the works which answer this problem perform well, but are limited to an initially fixed number of processors which is not modified at runtime. These approaches can be very inefficient, especially in terms of resource consumption. In this paper, we present a new graph repartitioning algorithm which accepts a variable number of processors, assuming the load is already balanced. Our algorithm minimizes both data communication and data migration overheads, while maintaining the computational load balanced. This algorithm is based on a theoretical result, that constructs optimal communication matrices with both a minimum migration volume and a minimum number of communications. An experimental study which compares our work against state-of-the-art approaches is presented

Graph Repartitioning with both Dynamic Load and Dynamic Processor Allocation

International audienceDynamic load balancing is an important step conditioning the performance of parallel programs, like adaptive mesh refinement codes. If the global workload varies drastically over time (such that memory is exceeded), it can be relevant to adjust the number of processors while maintaining the load balanced. We propose two different solutions, that extend classic graph repartitioning approaches to accept a variable number of processors: one based on biased partitioning method and one based on a diffusive method. We call this problem: the MxN graph repartitioning problem. Finally, an experimental study on real-life graphs validates our algorithms against state-of-the-art methods

Invasive compute balancing for applications with shared and hybrid parallelization

This is the author manuscript. The final version is available from the publisher via the DOI in this record.Achieving high scalability with dynamically adaptive algorithms in high-performance computing (HPC) is a non-trivial task. The invasive paradigm using compute migration represents an efficient alternative to classical data migration approaches for such algorithms in HPC. We present a core-distribution scheduler which realizes the migration of computational power by distributing the cores depending on the requirements specified by one or more parallel program instances. We validate our approach with different benchmark suites for simulations with artificial workload as well as applications based on dynamically adaptive shallow water simulations, and investigate concurrently executed adaptivity parameter studies on realistic Tsunami simulations. The invasive approach results in significantly faster overall execution times and higher hardware utilization than alternative approaches. A dynamic resource management is therefore mandatory for a more efficient execution of scenarios similar to our simulations, e.g. several Tsunami simulations in urgent computing, to overcome strong scalability challenges in the area of HPC. The optimizations obtained by invasive migration of cores can be generalized to similar classes of algorithms with dynamic resource requirements.This work was supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Centre ”Invasive Computing” (SFB/TR 89)