Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective per-flow credit-based flow control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective endpoint congestion detection and control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective per-flow credit-based flow control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective flow control of individual applications and traffic flows in conjunction with an end host. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, an ingress edge switch can perform fine grain flow control of individual sources of the flows residing on an end host.

Abstract: Systems and methods are provided for “on the fly” routing of data transmissions in the presence of errors. Switches can establish flow channels corresponding to flows in the network. In response to encountering a critical error on a network link along a transmission path, a switch can generate an error acknowledgement. The switch can transmit the error acknowledgements to ingress ports upstream from the network link via the plurality of flow channels. By transmitting the error acknowledgement, it indicates that the network link where the critical error was encountered is a failed link to ingress ports upstream from the failed link. Subsequently, each ingress port upstream from the failed link can dynamically update the path of the plurality of flows that are upstream from the failed link such that the plurality of flows that are upstream from the failed link are routed in a manner that avoids the failed link.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective congestion control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: Methods and systems are provided for performing lossy dropping and ECN marking in a flow-based network. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow are acknowledged after reaching the egress point of the network, and the acknowledgement packets are sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform per-flow packet dropping and ECN marking.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective flow control of individual applications and traffic flows in conjunction with an end host. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, an ingress edge switch can perform fine grain flow control of individual sources of the flows residing on an end host.

Abstract: Systems and methods are provided for “on the fly” routing of data transmissions in the presence of errors. Switches can establish flow channels corresponding to flows in the network. In response to encountering a critical error on a network link along a transmission path, a switch can generate an error acknowledgement. The switch can transmit the error acknowledgements to ingress ports upstream from the network link via the plurality of flow channels. By transmitting the error acknowledgement, it indicates that the network link where the critical error was encountered is a failed link to ingress ports upstream from the failed link. Subsequently, each ingress port upstream from the failed link can dynamically update the path of the plurality of flows that are upstream from the failed link such that the plurality of flows that are upstream from the failed link are routed in a manner that avoids the failed link.

Abstract: A switch equipped with a self-managing reduction engine is provided. During operation, the reduction engine can use a timeout mechanism to manage itself in different latency-induced or error scenarios. As a result, the network can facilitate an efficient and scalable environment for high performance computing.

Abstract: Methods and systems are provided for performing lossy dropping and ECN marking in a flow-based network. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow are acknowledged after reaching the egress point of the network, and the acknowledgement packets are sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform per-flow packet dropping and ECN marking.

Abstract: Systems and methods are described for providing per traffic class routing of data within a network. A network switch has the capability to classify traffic data based on High Performance Computing (HPC) related characteristics. Traffic classes are defined based on aspects of HPC, such as routing, ordering, redirection, quiesce, HPC protocol configuration, and telemetry. A switch can receive packets at an ingress port of a switch fabric, and determine traffic classifications for the packets. The traffic classification is selected from a group of defined traffic classes. Then, the switch can generate a fabric specific flag for the at least one packet that indicates the determined traffic classification, where the fabric specific flag is used for routing packets based on their assigned traffic classification. Examples of traffic classes include: low latency class; dedicated access class; bulk data class; best efforts class; and scavenger class.

Abstract: A switch equipped with a self-managing reduction engine is provided. During operation, the reduction engine can use a timeout mechanism to manage itself in different latency-induced or error scenarios. As a result, the network can facilitate an efficient and scalable environment for high performance computing.

Abstract: A data-driven intelligent networking system that can facilitate global fairness is provided. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow are acknowledged after reaching the egress point of the network, and the acknowledgement packets are sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and enforce global fairness on a per-flow basis.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective congestion control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: A switch architecture for a data-driven intelligent networking system is provided. The system can accommodate dynamic traffic with fast, effective congestion control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow are acknowledged after reaching the egress point of the network, and the acknowledgement packets are sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic with fast, effective congestion control. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform flow control on a per-flow basis.

Abstract: Methods and systems are provided for performing lossy dropping and ECN marking in a flow-based network. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow are acknowledged after reaching the egress point of the network, and the acknowledgement packets are sent back to the ingress point of the flow along the same data path. As a result, each switch can obtain state information of each flow and perform per-flow packet dropping and ECN marking.

Abstract: Data-driven intelligent networking systems and methods are provided. The system can accommodate dynamic traffic while applying injection limits to different traffic classes at an ingress edge port. The system can maintain state information of individual packet flows, which can be set up or released dynamically based on injected data. Each flow can be provided with a flow-specific input queue upon arriving at a switch. Packets of a respective flow can be acknowledged after reaching the egress point of the network, and the acknowledgement packets can be sent back to the ingress point of the flow along the same data path. Furthermore, an edge switch can dynamically allocate the ingress port bandwidth among the traffic classes that are active at a given moment.

Abstract: Systems and methods are described for providing per traffic class routing of data within a network. A network switch has the capability to classify traffic data based on High Performance Computing (HPC) related characteristics. Traffic classes are defined based on aspects of HPC, such as routing, ordering, redirection, quiesce, HPC protocol configuration, and telemetry. A switch can receive packets at an ingress port of a switch fabric, and determine traffic classifications for the packets. The traffic classification is selected from a group of defined traffic classes. Then, the switch can generate a fabric specific flag for the at least one packet that indicates the determined traffic classification, where the fabric specific flag is used for routing packets based on their assigned traffic classification. Examples of traffic classes include: low latency class; dedicated access class; bulk data class; best efforts class; and scavenger class.