Abstract: 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.

Abstract: Technologies for communication with direct data placement include a number of computing nodes in communication over a network. Each computing node includes a many-core processor having an integrated host fabric interface (HFI) that maintains an association table (AT). In response to receiving a message from a remote device, the HFI determines whether the AT includes an entry associating one or more parameters of the message to a destination processor core. If so, the HFI causes a data transfer agent (DTA) of the destination core to receive the message data. The DTA may place the message data in a private cache of the destination core. Message parameters may include a destination process identifier or other network address and a virtual memory address range. The HFI may automatically update the AT based on communication operations generated by software executed by the processor cores. Other embodiments are described and claimed.

Abstract: 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.