We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core (MIC)) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core operations of the application. We correlate the observed performance with the characteristics of computing devices and data access patterns, computation complexities, and parallelization forms of the operations. The results show a significant variability in the performance of operations with respect to the device used. The performances of operations with regular data access are comparable or sometimes better on a MIC than that on a GPU. GPUs are more efficient than MICs for operations that access data irregularly, because of the lower bandwidth of the MIC for random data accesses. We propose new performance-aware scheduling strategies that consider variabilities in operation speedups. Our scheduling strategies significantly improve application performance compared with classic strategies in hybrid configurations.

中文翻译：

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在具有多核 CPU、GPU 和 MIC 的系统上进行应用程序性能分析和高效执行：显微镜图像分析案例研究

我们使用显微镜图像分析应用程序对多核 CPU、GPU 和英特尔至强融核（多核集成 (MIC)）进行了性能比较研究。我们通过实验评估计算设备对应​​用程序核心操作的性能。我们将观察到的性能与计算设备的特性和数据访问模式、计算复杂性和操作的并行化形式相关联。结果表明，与所使用的设备相关的操作性能存在显着差异。具有常规数据访问的操作的性能在 MIC 上与在 GPU 上的性能相当或有时更好。对于不规则访问数据的操作，GPU 比 MIC 更有效，因为 MIC 用于随机数据访问的带宽较低。我们提出了新的性能感知调度策略，该策略考虑了操作加速的可变性。与混合配置中的经典策略相比，我们的调度策略显着提高了应用程序性能。