Dynamic Work Load Balancing for Compute Intensive Application Using Parallel and Hybrid Programming Models on CPU-GPU Cluster

Chandrashekhar, B. N.; Sanjay, H. A.

doi:10.1166/jctn.2018.7464

Cited by 3 publications

(1 citation statement)

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“…The model considers workload, processing capabilities, memory bandwidth, and the number of CPU and GPU cores on the cluster to distribute the workload effectively. In a heterogeneous CPU+GPUs cluster, efficient utilization of resources is a challenging issue since the application is executing in different hardware environments [3]. The efficient computing on a multi-core CPU depends on the parameters such as how we parallelize the code.…”

Section: Introductionmentioning

confidence: 99%

Performance Driven Analytical Workload Division Model for the HPC Applications on CPU-GPU Heterogeneous Cluster

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A²,

Murthy

et al. 2022

Preprint

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High-Performance Computing is the cornerstone for many scientific and industrial innovations. The demand for high-performance computing power is one of the driving factors for the innovations of computer hardware. In the hybrid system, CPUs and GPUs are combined to produce better performance while executing HPC applications. The critical challenge to achieving better performance in a heterogeneous cluster is the efficient distribution of the workload among the CPUs/GPUs in the nodes. In this work, to address the distribution workload issue, an optimized analytical workload division model for the heterogeneous cluster is developed to efficiently distribute the workload among the nodes of a heterogeneous cluster. The analytical model considers workload, processing capabilities, and the number of CPUs and GPUs on the cluster to effectively distribute the workload. HPL and merge sort benchmark applications are used to test the proposed strategy. Workload division strategy is tested by conducting extensive experiments. To address the inter-node and intra-node communication challenge, pinned memory technique is used along with a single MPI process per node technique and CUDA IPC. The proposed workload division strategy is validated with the HPL application and Merge sort. Experiments show that the proposed workload division strategy performs much better than the existing works.

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Section: Introductionmentioning

confidence: 99%