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Assessing Intel OneAPI capabilities and cloud-performance for heterogeneous computing

Silvia R. Alcaraz, Ruben Laso, Oscar G. Lorenzo, David L. Vilariño, Tomás F. Pena, Francisco F. Rivera
Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Rúa de Jenaro de la Fuente Domínguez S/N, 15782 Santiago de Compostela, Galicia, Spain
The Journal of Supercomputing

@article{rodriguez2024assessing,

   title={Assessing Intel OneAPI capabilities and cloud-performance for heterogeneous computing},

   author={Rodr{‘i}guez Alcaraz, Silvia and Laso Rodr{‘i}guez, Rub{‘e}n and Garc{‘i}a Lorenzo, {‘O}scar and L{‘o}pez Vilari{~n}o, David and Fern{‘a}ndez Pena, Anselmo Tom{‘a}s and Fern{‘a}ndez Rivera, Francisco},

   year={2024},

   publisher={Springer}

}

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This work presents a performance-oriented study of a heterogeneous application developed with Intel OneAPI to solve two well-known diffusion problems: heat diffusion and image denoising. We have explored CPU+iGPU and CPU+FPGA schemes, applying dynamic load balancing and conducting experiments on Intel DevCloud. The results demonstrate that the CPU+iGPU scheme outperforms the execution times achieved by the fastest device when the problem is sufficiently computationally demanding. We also found that the performance of the CPU+FPGA scheme is heavily affected by bandwidth limitations and specific strategies to manage memory efficiently are required. Moreover, it was demonstrated that dynamic workload balancing is crucial due to possible performance fluctuations in any of the implicated devices. In conclusion, Intel OneAPI provides a helpful tool for multiplatform development using a unique high-level language, DPC++. However, developing specific code for each platform is necessary to achieve optimal performance.
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