$k$ -移植与$k$ -通道广播、分散和Alltoall算法
$k$-ported vs. $k$-lane Broadcast, Scatter, and Alltoall Algorithms
摘要：
在$k$端口的消息传递系统中，处理器可以同时从$k$其它处理器接收$k$不同的消息，并且向$k$其它处理器发送$k$不同的消息，所述其它处理器可以与从其接收消息的处理器不同或者可以不不同。现代集群系统可能不具备这种能力。相反，通过让节点上的$k$处理器同时发送和接收至多一个消息，由$n$处理器组成的计算节点可以同时发送和接收来自其他节点的$k$消息。我们提出的问题，如何设计好的算法，这$k$通道模型，可能是通过适应算法设计的传统$k$移植模型。我们讨论并比较了用于广播、分散和all-to-all集体操作的许多（非最优）$k$通道算法（如在例如，MPI），并在具有双OmniPath网络（对应于$k=2$）的小型$36\times 32$节点集群上进行实验性评估。结果是初步的。
英文摘要：
In $k$-ported message-passing systems, a processor can simultaneously receive $k$ different messages from $k$ other processors, and send $k$ different messages to $k$ other processors that may or may not be different from the processors from which messages are received. Modern clustered systems may not have such capabilities. Instead, compute nodes consisting of $n$ processors can simultaneously send and receive $k$ messages from other nodes, by letting $k$ processors on the nodes concurrently send and receive at most one message. We pose the question of how to design good algorithms for this $k$-lane model, possibly by adapting algorithms devised for the traditional $k$-ported model. We discuss and compare a number of (non-optimal) $k$-lane algorithms for the broadcast, scatter and alltoall collective operations (as found in, e.g., MPI), and experimentally evaluate these on a small $36\times 32$-node cluster with a dual OmniPath network (corresponding to $k=2$). Results are preliminary.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
