Intel Corporation

Sk Hynix Nand Product Solutions Corp.

Yipeng Wang

Ren Wang

Tsung-Yuan Charles Tai

Sameh Gobriel

Andrew J Herdrich

Jr-Shian Tsai

Edwin Verplanke

Bruce Richardson

Debra Bernstein

David Sonnier

Stephen R Van Doren

William G Burroughs

Niall D McDonnell

Hugh Wilkinson

Andrew Cunningham

James T Clee

Narender R Vangati

Jerry Pirog

Joseph R Hasting

Gage Eads

Te Khac Ma

Jonathan Kenny

Stephen Miller

Jamison D Whitesell

Xiangyang Guo

Yifan Yuan

An Yan

Bradley A Burres

Namakkal N Venkatesan

Mrittika Ganguli

Francesc Guim Bernat

Kshitij Arun Doshi

Rajesh M Sankaran

Ravishankar Iyer

Raanan Sade

Alexander Bachmutsky

Chris MacNamara

Raghu R Kondapalli

Narayan Ranganathan

Rahul R Shah

Nilesh K Jain

Brinda Ganesh

Somnath Paul

Tsung-Yuan Charlie Tai

Bin Li

Tony Hurson

Hongjun Ni

Sundar Vedantham

Priya Autee

Janet Tseng

Abhirupa Layek

Rasika Subramanian

Abhijit Davare

Sriram Kabisthalam Muthukumar

Cristian Florin Dumitrescu

Anton Alexandrovich Sorokin

Pravin Pathak

Desmond A Kirkpatrick

Antonio Fischetti

Suvadeep Banerjee

Han Ju Lee

Zhu Zhou

Jaykant B Timbadiya

Nalini Murari

Shaman Narayana

Chenmin Sun

Pablo De Lara Guarch

Simonjit Dutta

Examples described herein relate to a work scheduler that includes at least one processor and at least one queue. In some examples, the work scheduler receives a request to allocate a region of memory and based on availability of a memory segment associated with a central cache to satisfy the request to allocate a region of memory, provide a memory allocation using an available memory segment entry associated with the central cache from the at least one queue. In some examples, the work scheduler assigns a workload to a processor and controls when to pre-fetch content relevant to the workload to store in a cache or memory accessible to the processor based on a position of the workload in a work queue associated with the processor.

Workload scheduler for memory allocation

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

An apparatus and method for closed loop dynamic resource allocation. For example, one embodiment of a method comprises: collecting data related to usage of a plurality of resources by a plurality of workloads over one or more time periods, the workloads including priority workloads associated with one or more guaranteed performance levels and best effort workloads not associated with guaranteed performance levels; analyzing the data to identify resource reallocations from one or more of the priority workloads to one or more of the best effort workloads in one or more subsequent time periods while still maintaining the guaranteed performance levels; reallocating the resources from the priority workloads to the best effort workloads for the subsequent time periods; monitoring execution of the priority workloads with respect to the guaranteed performance level during the subsequent time periods; and preemptively reallocating resources from the best effort workloads to the priority workloads during the subsequent time periods to ensure compliance with the guaranteed performance level and responsive to detecting that the guaranteed performance level is in danger of being breached.

Apparatus and method for a closed-loop dynamic resource allocation control framework

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 described herein relates to a network interface apparatus that includes packet processing circuitry and a bus interface. In some examples, the packet processing circuitry to: process a received packet that includes data, a request to perform a write operation to write the data to a cache, and an indicator that the data is to be durable and based at least on the received packet including the request and the indicator, cause the data to be written to the cache and non-volatile memory. In some examples, the packet processing circuitry is to issue a command to an input output (IO) controller to cause the IO controller to write the data to the cache and the non-volatile memory. In some examples, the cache comprises one or more of: a level-0 (L0), level-1 (L1), level-2 (L2), or last level cache (LLC) and the non-volatile memory comprises one or more of: volatile memory that is part of an Asynchronous DRAM Refresh (ADR) domain, persistent memory, battery-backed memory, or memory device whose state is determinate even if power is interrupted to the memory device. In some examples, based on receipt of a second received packet that includes a request to persist data, the packet processing circuitry is to request that data stored in a memory buffer be copied to the non-volatile memory.

Data consistency and durability over distributed persistent memory systems

A central processing unit can offload table lookup or tree traversal to an offload engine. The offload engine can provide hardware accelerated operations such as instruction queueing, bit masking, hashing functions, data comparisons, a results queue, and a progress tracking. The offload engine can be associated with a last level cache. In the case of a hash table lookup, the offload engine can apply a hashing function to a key to generate a signature, apply a comparator to compare signatures against the generated signature, retrieve a key associated with the signature, and apply the comparator to compare the key against the retrieved key. Accordingly, a data pointer associated with the key can be provided in the result queue. Acceleration of operations in tree traversal and tuple search can also occur.

Offload of data lookup operations

One embodiment provides a network system. The network system includes an application layer to execute one or more networking applications to generate or receive data packets having flow identification (ID) information; and a packet processing layer having profiling circuitry to generate a sketch table indicative of packet flow count data; the sketch table having a plurality of buckets, each bucket includes a first section including a plurality of data fields, each data field of the first section to store flow ID and packet count data, each bucket also having a second section having a plurality of data fields, each data field of the second section to store packet count data.

Sketch table for traffic profiling and measurement

Techniques and apparatus for dynamic data access mode processes are described. In one embodiment, for example, an apparatus may a processor, at least one memory coupled to the processor, the at least one memory comprising an indication of a database and instructions, the instructions, when executed by the processor, to cause the processor to determine a database utilization value for a database, perform a comparison of the database utilization value to at least one utilization threshold, and set an active data access mode to one of a low-utilization data access mode or a high-utilization data access mode based on the comparison. Other embodiments are described.

Techniques for dynamic database access modes

Examples may include a method of compiling a declarative language program for a virtual switch. The method includes parsing the declarative language program, the program defining a plurality of match-action tables (MATs), translating the plurality of MATs into intermediate code, and parsing a core identifier (ID) assigned to each one of the plurality of MATs. When the core IDs of the plurality of MATs are the same, the method includes connecting intermediate code of the plurality of MATs using function calls, and translating the intermediate code of the plurality of MATs into machine code to be executed by a core identified by the core IDs.

Data plane semantics for software virtual switches

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

Apparatus and methods implementing a hardware queue management device for reducing inter-core data transfer overhead by offloading request management and data coherency tasks from the CPU cores. The apparatus include multi-core processors, a shared L3 or last-level cache ('LLC'), and a hardware queue management device to receive, store, and process inter-core data transfer requests. The hardware queue management device further comprises a resource management system to control the rate in which the cores may submit requests to reduce core stalls and dropped requests. Additionally, software instructions are introduced to optimize communication between the cores and the queue management device.

Multi-core communication acceleration using hardware queue device

Growing community of inventors

Hillsboro, OR, United States of America

Yipeng Wang