Intel Corporation

Sameh Gobriel

Tsung-Yuan Charles Tai

Ren Wang

Christian Maciocco

Tsung-Yuan Charlie Tai

Kristoffer Fleming

Yipeng Wang

Gideon Prat

Jr-Shian Tsai

Namakkal N Venkatesan

Byron Marohn

Ben-Zion Friedman

Hang T Nguyen

Sanjeev Jain

Edwin Verplanke

Shrikant M Shah

Mrittika Ganguli

Wei Shen

Michael A O'Hanlon

Rajesh M Sankaran

Raanan Sade

Sanjay Bakshi

Narayan Ranganathan

Rahul R Shah

Nilesh K Jain

Aamer Jaleel

Brinda Ganesh

Somnath Paul

Eric K Mann

Bruce Richardson

Ajay G Gupta

Ahmad A Samih

Ashok Sunder Rajan

Hongjun Ni

Abhijit Davare

Sriram Kabisthalam Muthukumar

Priya Autee

Abhirupa Layek

Kannan Babu Ramia

Saikrishna Edupuganti

Anton Alexandrovich Sorokin

Desmond A Kirkpatrick

Antonio Fischetti

Suvadeep Banerjee

Jong Han Park

Dong Zhou

George W Kennedy

Jr-Shian (James) Tsai

Chenmin Sun

Shaman Narayana

Jaykant B Timbadiya

Nalini Murari

Ken Schumm

Asif H Haswarey

Tsung-Yuang Charles Tai

Examples described herein include a device interface; a first set of one or more processing units; and a second set of one or more processing units. In some examples, the first set of one or more processing units are to perform heavy flow detection for packets of a flow and the second set of one or more processing units are to perform processing of packets of a heavy flow. In some examples, the first set of one or more processing units and second set of one or more processing units are different. In some examples, the first set of one or more processing units is to allocate pointers to packets associated with the heavy flow to a first set of one or more queues of a load balancer and the load balancer is to allocate the packets associated with the heavy flow to one or more processing units of the second set of one or more processing units based, at least in part on a packet receive rate of the packets associated with the heavy flow.

Packet processing load balancer

Examples described herein relate to offload circuitry comprising one or more compute engines that are configurable to perform a workload offloaded from a process executed by a processor based on a descriptor particular to the workload. In some examples, the offload circuitry is configurable to perform the workload, among multiple different workloads. In some examples, the multiple different workloads include one or more of: data transformation (DT) for data format conversion, Locality Sensitive Hashing (LSH) for neural network (NN), similarity search, sparse general matrix-matrix multiplication (SpGEMM) acceleration of hash based sparse matrix multiplication, data encode, data decode, or embedding lookup.

Hardware offload circuitry

Apparatus, methods, and systems for tuple space search-based flow classification using cuckoo hash tables and unmasked packet headers are described herein. A device can communicate with one or more hardware switches. The device can include memory to store hash table entries of a hash table. The device can include processing circuitry to perform a hash lookup in the hash table. The lookup can be based on an unmasked key include in a packet header corresponding to a received data packet. The processing circuitry can retrieve an index pointing to a sub-table, the sub-table including a set of rules for handling the data packet. Other embodiments are also described.

Flow classification apparatus, methods, and systems

Examples may include techniques to control an insertion ratio or rate for a cache. Examples include comparing cache miss ratios for different time intervals or windows for a cache to determine whether to adjust a cache insertion ratio that is based on a ratio of cache misses to cache insertions.

Techniques to control an insertion ratio for a cache

Technologies for flow rule aware exact match cache compression include multiple computing devices in communication over a network. A computing device reads a network packet from a network port and extracts one or more key fields from the packet to generate a lookup key. The key fields are identified by a key field specification of an exact match flow cache. The computing device may dynamically configure the key field specification based on an active flow rule set. The computing device may compress the key field specification to match a union of non-wildcard fields of the active flow rule set. The computing device may expand the key field specification in response to insertion of a new flow rule. The computing device looks up the lookup key in the exact match flow cache and, if a match is found, applies the corresponding action. Other embodiments are described and claimed.

Technologies for flow rule aware exact match cache compression

A computing apparatus for providing a node within a distributed network function, including: a hardware platform; a network interface to communicatively couple to at least one other peer node of the distributed network function; a distributor function including logic to operate on the hardware platform, including a hashing module configured to receive an incoming network packet via the network interface and perform on the incoming network packet a first-level hash of a two-level hash, the first level hash being a lightweight hash with respect to a second-level hash, the first level hash to deterministically direct a packet to one of the nodes of the distributed network function as a directed packet; and a denial of service (DoS) mitigation engine to receive notification of a DoS attack, identify a DoS packet via the first-level hash, and prevent the DoS packet from reaching the second-level hash.

Denial of service mitigation with two-tier hash

Apparatus and method to facilitate networked compute node cluster routing are disclosed herein. In some embodiments, a compute node for cluster compute may include one or more input ports to receive data packets from first selected ones of a cluster of compute nodes; one or more output ports to route data packets to second selected ones of the cluster of computer nodes; and one or more processors, wherein the one or more processors includes logic to determine a particular output port, of the one or more output ports, to which a data packet received at the one or more input ports is to be routed, and wherein the logic is to exclude output ports associated with links indicated in fault status information as having a fault status to be the particular output port to which the data packet is to be routed.

Compute node cluster based routing method and apparatus

An apparatus and method for prioritizing transactional memory regions. For example, one embodiment of a processor comprises: a plurality of cores to execute threads comprising sequences of instructions, at least some of the instructions specifying a transactional memory region; a cache of each core to store a plurality of cache lines; transactional memory circuitry of each core to manage execution of the transactional memory (TM) regions based on priorities associated with each of the TM regions; and wherein the transactional memory circuitry, upon detecting a conflict between a first TM region having a first priority value and a second TM region having a second priority value, is to determine which of the first TM region or the second TM region is permitted to continue executing and which is to be aborted based, at least in part, on the first and second priority values.

Apparatus and method for prioritized quality of service processing for transactional memory

Technologies for distributed table lookup via a distributed router includes an ingress computing node, an intermediate computing node, and an egress computing node. Each computing node of the distributed router includes a forwarding table to store a different set of network routing entries obtained from a routing table of the distributed router. The ingress computing node generates a hash key based on the destination address included in a received network packet. The hash key identifies the intermediate computing node of the distributed router that stores the forwarding table that includes a network routing entry corresponding to the destination address. The ingress computing node forwards the received network packet to the intermediate computing node for routing. The intermediate computing node receives the forwarded network packet, determines a destination address of the network packet, and determines the egress computing node for transmission of the network packet from the distributed router.

Technologies for distributed routing table lookup

The present disclosure describes a process and apparatus for improving insertions of entries into a hash table. A large number of smaller virtual buckets may be combined together and associated with buckets used for hash table entry lookups and/or entry insertion. On insertion of an entry, hash table entries associated with a hashed-to virtual bucket may be moved between groups of buckets associated with the virtual bucket, to better distribute entries across the available buckets to reduce the number of entries in the largest buckets and the standard deviation of the bucket sizes across the entire hash table.

Hash table entries insertion method and apparatus using virtual buckets

Technologies for managing lookup tables are described. The lookup tables may be used for a two-level lookup scheme for packet processing. When the tables need to be updated with a new key for packet processing, information about the new key may be added to a first-level lookup table and a second-level lookup table. The first-level lookup table may be used to identify a handling node for an obtained packet, and the handling node may perform a second-level table lookup to obtain information for further packet processing. The first lookup table may be replicated on all the nodes in a cluster, and the second-level lookup table may be unique to each node in the cluster. Other embodiments are described herein and claimed.

Technologies for management of lookup tables

Technologies for efficient network flow classification include a computing device that receives a network packet that includes a header. The computing device generates a vector Bloom filter (VBF) key as a function of the header and searches multiple VBFs for a VBF that matches the VBF key. Each VBF is associated with a flow sub-table that includes one or more flow rules. Each flow sub-table is associated with a mask length. If a matching VBF is found, the computing device searches the corresponding flow sub-table for a flow rule that matches a masked header of the network packet. If no matching VBF is found or if no matching flow rule is found, the computing device searches all of the flow sub-tables for a flow rule that matches the header. The computing device applies a flow action of a matching flow rule. Other embodiments are described and claimed.

Technologies for efficient network flow classification with vector bloom filters

Technologies for managing network flow lookups of a network device include a network controller and a target device, each communicatively coupled to the network device. The network device includes a cache for a processor of the network device and a main memory. The network device additionally includes a multi-level hash table having a first-level hash table stored in the cache of the network device and a second-level hash table stored in the main memory of the network device. The network device is configured to determine whether to store a network flow hash corresponding to a network flow indicating the target device in the first-level or second-level hash table based on a priority of the network flow provided to the network device by the network controller.

Technologies for network device flow lookup management

Growing community of inventors

Hillsboro, OR, United States of America

Sameh Gobriel