Cloud Warehouses have been exploiting CPU-FPGA environments to accelerate multi-tenant applications to achieve scalability and maximize resource utilization. In this scenario, kernels are sent to CPU and FPGA concurrently, considering available resources and workload characteristics, which are highly variant. Therefore, we propose a multi-objective optimization strategy to improve resource provisioning in CPU-FPGA environments. It is based on the Genetic Multidimensional Knapsack solution and can be tuned to minimize makespan or energy. Our strategy provides similar results as the optimal Exhaustive Search, but with feasible execution time, while presenting 77% energy savings with 39% lower makespan than the commonly-used First-Fit strategy.

中文翻译：

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CPU-FPGA云环境的资源感知协作分配

云仓库一直在利用CPU-FPGA环境来加速多租户应用程序，以实现可扩展性和最大程度地利用资源。在这种情况下，考虑到可用资源和工作负载的特性，内核会同时发送到CPU和FPGA，这是非常不同的。因此，我们提出了一种多目标优化策略来改善CPU-FPGA环境中的资源供应。它基于遗传多维背包解决方案，可以进行调整以最大程度地减少制造时间或精力。我们的策略可提供与最佳穷举搜索相似的结果，但执行时间可行，同时与传统的First-Fit策略相比，可节省77％的能源，并降低39％的制造时间。