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With the widespread using of GPU hardware facilities, more and more distributed machine learning applications have begun to use CPU-GPU hybrid cluster resources to improve the efficiency of algorithms. However, the existing distributed machine learning scheduling framework either only considers task scheduling on CPU resources or only considers task scheduling on GPU resources. Even considering the difference between CPU and GPU resources, it is difficult to improve the resource usage of the entire system. In other words, the key challenge in using CPU-GPU clusters for distributed machine learning jobs is how to efficiently schedule tasks in the job. In the full paper, we propose a CPU-GPU hybrid cluster schedule framework in detail. First, according to the different characteristics of the computing power of the CPU and the computing power of the GPU, the data is divided into data fragments of different sizes to adapt to CPU and GPU computing resources. Second, the paper introduces the task scheduling method under the CPU-GPU hybrid. Finally, the proposed method is verified at the end of the paper. After our verification for K-Means, using the CPU-GPU hybrid computing framework can increase the performance of K-Means by about 1.5 times. As the number of GPUs increases, the performance of K-Means can be significantly improved.

The traditional underwater sonar system usually achieve high angle resolution by increasing array aperture and the number of array elements, but this method will inevitably lead to complex system and high cost. Given that big brown bats have obtained surprisingly high resolution using a simple system, this paper proposes a bionic target localization method. First, a range-azimuth joint dictionary was constructed based on the bionic system of multi-harmonic emission and double random array reception. Then, the coherence characteristic of the dictionary was analyzed and the range and azimuth of the target were estimated, and at last the experimental verification was completed. The results show that the bionic range-azimuth joint estimation based on sparse signal representation can achieve high-precision target localization under the condition of echo high aliasing.