Chelsio Communications, Inc.

Fungible, Inc.

Microsoft Technology Licensing, LLC

Wael Noureddine

Asgeir Thor Eiriksson

Pradeep S Sindhu

Jean-Marc Frailong

Deepak Goel

Felix A Marti

Bertrand Serlet

Srihari Raju Vegesna

Ayaskant Pani

Paul Kim

Rajan Goyal

Philip A Thomas

Dimitrios Michailidis

Narendra Jayawant Gathoo

Gopesh Goyal

Robert William Bowdidge

Charles Edward Gray

Chris Yuhong Mao

Sunil Mekad

Aibing Zhou

Phillip A Thomas

Satish Deo

Balekudru A Krishna

Chandrasekhar Srinivasaiah

Anamaya Sullerey

Dmitriy Leonidovich Budko

Gaurav Gupte

Hoai Vu Thanh Tran

Aravind Vidhyasagar Lappasi

Leith Alan Leedom

Satish Dattatray Deo

Yixing Ruan

Rajesh Nair

Pratapa Reddy Vaka

Rajesh G Nair

A new processing architecture is described that utilizes a data processing unit (DPU). Unlike conventional compute models that are centered around a central processing unit (CPU), the DPU that is designed for a data-centric computing model in which the data processing tasks are centered around the DPU. The DPU may be viewed as a highly programmable, high-performance I/O and data-processing hub designed to aggregate and process network and storage I/O to and from other devices. The DPU comprises a network interface to connect to a network, one or more host interfaces to connect to one or more application processors or storage devices, and a multi-core processor with two or more processing cores executing a run-to-completion data plane operating system and one or more processing cores executing a multi-tasking control plane operating system. The data plane operating system is configured to support software functions for performing the data processing tasks.

Data processing unit for stream processing

Techniques are described for providing a scaled-out transport supported by interconnected data processing units (DPUs) that operates as a single system bus connection proxy for device-to-device communications within a data center. As one example, this disclosure describes techniques for providing a Peripheral Component Interconnect Express (PCIe) proxy for device-to-device communications employing the PCIe standard. The disclosed techniques include adding PCIe proxy logic on top of a host unit of a DPU to expose a PCIe proxy model to application processors, storage devices, network interface controllers, field programmable gate arrays, or other PCIe endpoint devices. The PCIe proxy model may be implemented as a physically distributed Ethernet-based switch fabric with PCIe proxy logic at the edge and fronting the PCIe endpoint devices. The interconnected DPUs and the distributed Ethernet-based switch fabric together provide a reliable, low-latency, and scaled-out transport that operates as a PCIe proxy.

Scaled-out transport as connection proxy for device-to-device communications

A fabric control protocol (FCP) and packet forwarding mechanisms are described that maximize utilization of bandwidth within massive, large-scale data centers having multi-stage data center switch fabric topologies, such as topologies that include a third switching layer formed by super spine switches. Automatic generation of data plane forwarding information referred to as FCP path information enumerates, for each data processing unit (DPU), the available FCP paths. The FCP path information may be based on unique combinations of peak points of the switch fabric for a given DPU with FCP colors assigned to network links that are used to multi-home the DPU to the switch fabric.

Fabric control protocol for large-scale multi-stage data center networks

A fabric control protocol is described for use within a data center in which a switch fabric provides full mesh interconnectivity such that any of the servers may communicate packet data for a given packet flow to any other of the servers using any of a number of parallel data paths within the data center switch fabric. The fabric control protocol enables spraying of individual packets for a given packet flow across some or all of the multiple parallel data paths in the data center switch fabric and, optionally, reordering of the packets for delivery to the destination. The fabric control protocol may provide end-to-end bandwidth scaling and flow fairness within a single tunnel based on endpoint-controlled requests and grants for flows. In some examples, the fabric control protocol packet structure is carried over an underlying protocol, such as the User Datagram Protocol (UDP).

Fabric control protocol for data center networks with packet spraying over multiple alternate data paths

A network system for a data center is described in which an access node sprays a data flow of packets over a logical tunnel to another access node. In one example, a method comprises establishing, by a plurality of access nodes, a logical tunnel over a plurality of data paths across a switch fabric between a source access node and a destination access node included within the plurality of access nodes, wherein the source access node is coupled to a source network device; and spraying, by the source access node, a data flow of packets over the logical tunnel to the destination access node, wherein the source access node receives the data flow of packets from the source network device, and wherein spraying the data flow of packets includes directing each of the packets within the data flow to a least loaded data path.

Data center network with packet spraying

A network system for a data center. In one example, a method comprises establishing, by a plurality of access nodes, a logical tunnel over a plurality of data paths across a switch fabric between a source access node and a destination access node included within the plurality of access nodes, wherein the source access node is coupled to a source network device; and spraying, by the source access node, a data flow of packets over the logical tunnel to the destination access node, wherein the source access node receives the data flow of packets from the source network device, and wherein spraying the data flow of packets includes directing each of the packets within the data flow to one of the data paths based on an amount of data previously transmitted on each of the plurality of data paths.

Techniques are described in which a system having multiple processing units processes a series of work units in a processing pipeline, where some or all of the work units access or manipulate data stored in non-coherent memory. In one example, this disclosure describes a method that includes identifying, prior to completing processing of a first work unit with a processing unit of a processor having multiple processing units, a second work unit that is expected to be processed by the processing unit after the first work unit. The method also includes processing the first work unit, and prefetching, from non-coherent memory, data associated with the second work unit into a second cache segment of the buffer cache, wherein prefetching the data associated with the second work unit occurs concurrently with at least a portion of the processing of the first work unit by the processing unit.

Efficient work unit processing in a multicore system

A new processing architecture is described in which a data processing unit (DPU) is utilized within a device. Unlike conventional compute models that are centered around a central processing unit (CPU), example implementations described herein leverage a DPU that is specially designed and optimized for a data-centric computing model in which the data processing tasks are centered around, and the primary responsibility of, the DPU. For example, various data processing tasks, such as networking, security, and storage, as well as related work acceleration, distribution and scheduling, and other such tasks are the domain of the DPU. The DPU may be viewed as a highly programmable, high-performance input/output (I/O) and data-processing hub designed to aggregate and process network and storage I/O to and from multiple other components and/or devices. This frees resources of the CPU, if present, for computing-intensive tasks.

Data processing unit for compute nodes and storage nodes

Methods and apparatus for memory management are described. In one example, this disclosure describes a method that includes executing, by a first processing unit, first work unit operations specified by a first work unit message, wherein execution of the first work unit operations includes accessing data from shared memory included within the computing system, modifying the data, and storing the modified data in a first cache associated with the first processing unit; identifying, by the computing system, a second work unit message that specifies second work unit operations that access the shared memory; updating, by the computing system, the shared memory by storing the modified data in the shared memory; receiving, by the computing system, an indication that updating the shared memory with the modified data is complete; and enabling the second processing unit to execute the second work unit operations.

Memory management in a multiple processor system

An access node that can be configured and optimized to perform input and output (I/O) tasks, such as storage and retrieval of data to and from network devices (such as solid state drives), networking, data processing, and the like. For example, the access node may be configured to receive data to be processed, wherein the access node includes a plurality of processing cores, a data network fabric, and a control network fabric; receive, over the control network fabric, a work unit message indicating a processing task to be performed a processing core; and process the work unit message, wherein processing the work unit message includes retrieving data associated with the work unit message over the data network fabric.

Access node for data centers

Growing community of inventors

Santa Clara, CA, United States of America

Wael Noureddine