Programming efficiently heterogeneous systems is a major challenge, due to the complexity of their architectures. Intel oneAPI, a new and powerful standards-based unified programming model, built on top of SYCL, addresses these issues. In this paper, oneAPI is provided with co-execution strategies to run the same kernel between different devices, enabling the exploitation of static and dynamic policies. On top of that, static and dynamic load-balancing algorithms are integrated and analyzed. This work evaluates the performance and energy efficiency for a well-known set of regular and irregular HPC benchmarks, using an integrated GPU and CPU. Experimental results show that co-execution is worthwhile when using dynamic algorithms, improving efficiency even more when using unified shared memory.

中文翻译：

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利用与 oneAPI 的共同执行：现代视角的异构性

由于其体系结构的复杂性，对异构系统进行高效编程是一项重大挑战。英特尔 oneAPI 是一种基于标准的全新且强大的统一编程模型，构建在 SYCL 之上，可以解决这些问题。在本文中，oneAPI 提供了共同执行策略以在不同设备之间运行相同的内核，从而能够利用静态和动态策略。最重要的是，对静态和动态负载平衡算法进行了集成和分析。这项工作使用集成的 GPU 和 CPU，评估一组众所周知的常规和不规则 HPC 基准的性能和能效。实验结果表明，在使用动态算法时，共同执行是值得的，在使用统一共享内存时更能提高效率。