Algorithms—136.Single Number

本文介绍了一种通过异或运算解决单数元素查找问题的方法,利用循环遍历数组并进行异或操作,最终得到结果。

思路:异或计算。0^a=a;a^b=b^a。

public class Solution {
    public int singleNumber(int[] nums) {
    	int answer=0;
    	for (int i = 0; i < nums.length; i++) {
			answer^=nums[i];
		}
    	return answer;
    }
}


Ring Algorithm: This is NCCL's default communication pattern for small to medium-sized clusters. In this scheme, each GPU sends data to its neighbor and receives data from another neighbor, forming a ring-like structure. CUDA facilitates this by handling the GPU-to-GPU data transfers within the ring, using high-bandwidth connections like NVLink within a node or GPUDirect RDMA across nodes. This algorithm is bandwidth-efficient because each GPU participates in both sending and receiving, distributing the workload across the ring. However, as the number of GPUs in the ring increases, the communication latency becomes a limiting factor, necessitating more complex algorithms for larger clusters.Tree and Hierarchical Algorithms: For larger clusters, NCCL employs tree-based algorithms to reduce the number of communication steps, thus minimizing latency. Tree algorithms allow the reduction and aggregation of data in a multi-level structure, where each GPU communicates with fewer nodes in each step, drastically reducing the total communication time compared to a ring.Hierarchical All-Reduce: For even greater scalability, NCCL introduces a hierarchical approach, which is a two-step process that leverages CUDA's intra- and inter-node communication capabilities. First, NCCL performs intra-node reduction using NVLink or NVSwitch, facilitated by CUDA's ability to rapidly transfer data between GPUs within a single node. During this step, CUDA manages the memory pooling, synchronization, and data transfer across the GPUs, effectively aggregating the data locally.
04-01
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