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A Provenance-based Execution Strategy for Variant GPU-accelerated Scientific Workflows in Clouds

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Abstract

Several complex scientific simulations process large amounts of distributed and heterogeneous data. These simulations are commonly modeled as scientific workflows and require High Performance Computing (HPC) environments to produce results timely. Although scientists already benefit from clusters and clouds, new hardware, such as General Purpose Graphical Processing Units (GPGPUs), can be used to speedup the execution of the workflow. Clouds also provide virtual machines (VMs) with GPU capabilities that can also be used, thus becoming hybrid clouds. This way, many workflows can be modeled considering programs that execute in GPUs, CPUs or both. A problem that arises is how to schedule workflows with variant activities (that can be executed in CPU, GPU or both) in this hybrid environment. Although existing workflow systems (WfMS) can execute in GPGPUs and clouds independently, they do not provide mechanisms for scheduling workflows with variant activities in this hybrid environment. In fact, reducing the makespan and the financial cost of variant workflows in hybrid clouds may be a difficult task. In this article, we present a scheduling strategy for Variant GPU-accelerated workflows in clouds, named PROFOUND, which schedules activations (atomic tasks) to a set of CPU and GPU/CPU VMs based on provenance data (historical data). PROFOUND is based on a combination of a mathematical formulation and a heuristic, and aims at minimizing not only the makespan, but also the financial cost involved in the execution. To evaluate PROFOUND, we used a set of benchmark instances based on synthetic and real scenarios gathered from different workflows traces. The experiments show that PROFOUND is able to solve the referred scheduling problem.

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Data Availability

The Workflow traces (input data), source code (the scheduling heuristic) and results (output data) data that support the findings of the study presented in this article are available in GitHub, http://github.com/UFFeScience/Wf-GPU.

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Authors and Affiliations

  1. Instituto de Computação, Universidade Federal Fluminense, Niterói, RJ, Brazil

    Murilo B. Stockinger, Filipe Santiago, Yuri Frota, Isabel Rosseti, Alexandre Plastino & Daniel de Oliveira

  2. Instituto Federal de Educação, Ciência e Tecnologia do Rio de Janeiro, Arraial do Cabo, RJ, Brazil

    Marcos A. Guerine

  3. Departamento de Ciência da Computação, Universidade Federal Rural do Rio de Janeiro, Nova Iguaçu, RJ, Brazil

    Ubiratam de Paula

Authors
  1. Murilo B. Stockinger
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  2. Marcos A. Guerine
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  3. Ubiratam de Paula
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  4. Filipe Santiago
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  5. Yuri Frota
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  6. Isabel Rosseti
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  7. Alexandre Plastino
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  8. Daniel de Oliveira
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Correspondence to Daniel de Oliveira.

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Authors would like to thank CNPq and FAPERJ. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Stockinger, M.B., Guerine, M.A., de Paula, U. et al. A Provenance-based Execution Strategy for Variant GPU-accelerated Scientific Workflows in Clouds. J Grid Computing 20, 36 (2022). https://doi.org/10.1007/s10723-022-09625-y

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