公开(公告)号：US20160072908A1

公开(公告)日：2016-03-10

申请号：US14481686

申请日：2014-09-09

申请人： James Dinan ,  Srinivas Sridharan

发明人： James Dinan ,  Srinivas Sridharan

IPC分类号： H04L29/08

摘要： Technologies for proxy-based multithreaded message passing include a number of computing nodes in communication over a network. Each computing node establishes a number of message passing interface (MPI) endpoints associated with threads executed within a host processes. The threads generate MPI operations that are forwarded to a number of proxy processes. Each proxy process performs the MPI operation using an instance of a system MPI library. The threads may communicate with the proxy processes using a shared-memory communication method. Each thread may be assigned to a particular proxy process. Each proxy process may be assigned dedicated networking resources. MPI operations may include sending or receiving a message, collective operations, and one-sided operations. Other embodiments are described and claimed.

摘要翻译： 基于代理的多线程消息传递的技术包括通过网络进行通信的多个计算节点。 每个计算节点建立与主机进程内执行的线程相关联的多个消息传递接口（MPI）端点。 线程生成转发到多个代理进程的MPI操作。 每个代理进程使用系统MPI库的实例执行MPI操作。 线程可以使用共享存储器通信方法与代理进程通信。 每个线程可能被分配给特定的代理进程。 每个代理进程可以被分配专用的网络资源。 MPI操作可能包括发送或接收消息，集体操作和单面操作。 描述和要求保护其他实施例。

公开(公告)号：US20180285733A1

公开(公告)日：2018-10-04

申请号：US15476998

申请日：2017-04-01

申请人： Naveen K. Mellempudi ,  Srinivas Sridharan ,  Dheevatsa Mudigere ,  Dipankar Das

发明人： Naveen K. Mellempudi ,  Srinivas Sridharan ,  Dheevatsa Mudigere ,  Dipankar Das

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06N3/063

摘要： Technologies for artificial neural network training include a computing node with a host fabric interface that sends a message that includes one or more artificial neural network training algorithm values to another computing node in response to receipt of a request to send the message. Prior to sending the message, the host fabric interface may receive a request to quantize the message and quantize the message based on a quantization level included in the request to generate a quantized message. The quantization message includes one or more quantized values such that each quantized value has a lower precision than a corresponding artificial neural network training algorithm value. The host fabric interface then transmits the quantized message, which includes metadata indicative of the quantization level, to another computing node in response to quantization of the message for artificial neural network training. Other embodiments are described and claimed.