Intel Corporation

Sk Hynix Nand Product Solutions Corp.

Mark A Schmisseur

Francesc Guim Bernat

Karthik Kumar

Thomas Willhalm

Kshitij Arun Doshi

Daniel Rivas Barragan

Sivakumar Radhakrishnan

Raj K Ramanujan

Timothy Verrall

Suraj Prabhakaran

Dimitrios Ziakas

Blaise B Fanning

Robert J Royer, Jr

Lidia Warnes

Raymond Scott Tetrick

David B Minturn

Debendra Das Sharma

Mohan J Kumar

Murugasamy K Nachimuthu

Michael T Klinglesmith

Bradley A Burres

Durgesh Srivastava

Eric Dahlen

Vinodh Gopal

Wajdi K Feghali

Kapil Sood

Aaron Gorius

Samantha J Edirisooriya

Tarun Viswanathan

Brinda Ganesh

David J Harriman

Matthew James Adiletta

Wei P Chen

Andrew J Herdrich

Robert Charles Swanson

Yen-Cheng Liu

Michael T Crocker

Mahesh S Natu

Pankaj Kumar

Sin Sim Tan

Steen K Larsen

Kenneth C Haren

Thomas M Slaight

Sailesh Bissessur

Da-Ming Chiang

David R Smith

Shane Matthews

Brian M Leitner

Balint Fleischer

Geetani R Edirisooriya

Richard Paul Mackey

David C Lee

Marc Tim Jones

John Chun Kwok Leung

Matthew R Wilcox

Rasika Subramanian

Timothy J Jehl

Robert L Sheffield

Saptarshi Mondal

Ginger Gilsdorf

Ramanathan Sethuraman

Eswaramoorthi Nallusamy

Marc A Goldschmidt

Asher M Altman

Gregory W Tse

Jeff J McCoskey

Thomas C Brown

Myles Wilde

Richard Paul Mangold

Stephen R Van Doren

Narayan Ranganathan

Marcos Emanuel Carranza

Ramamurthy Krithivas

David M Lee

Larisa Novakovsky

Bassam N Coury

Paul H Dormitzer

Ishwar Agarwal

Woojong Han

Barry R Davis

Deif N Atallah

Benjamin Graniello

Richard C Beckett

Andy M Rudoff

Marc Milobinski

Harshad S Sane

Rita D Gupta

Kimberly A Malone

Filip Schmole

Anup Mohan

Eric Rasmussen

Peter Molnar

Delf Atallah

Brett W Gaines

Steen Larsen

Kimberly Malone

Brian Aspnes

Douglas E Stewart

Systems, apparatuses, and methods provide for memory management where an infrastructure processing unit bypasses a central processing unit. Such an infrastructure processing unit determines if incoming packets of memory traffic trigger memory rules stored by the infrastructure processing unit. The incoming packets are routed to the central processing unit in a default mode when the incoming packets do not trigger the memory rules. Conversely, the incoming packets are routed to the infrastructure processing unit and bypass the central processing unit in an inline mode when the incoming packets trigger the memory rules. A memory architecture communicatively coupled to the central processing unit receives a set of atomic transactions from the infrastructure processing unit that bypasses the central processing unit and performs the set of atomic transactions from the infrastructure processing unit.

Memory schemes for infrastructure processing unit architectures

An embodiment of an electronic apparatus may comprise one or more substrates, and a controller coupled to the one or more substrates, the controller to allocate a first secure portion of a pooled memory to a first instantiation of an application on a first node, and circuitry coupled to the one or more substrates and the controller, the circuitry to provide a failover interface for a second instantiation of the application on a second node to access the first secure portion of the pooled memory in the event of a failure of the first node. Other embodiments are disclosed and claimed.

Failover for pooled memory

Methods and apparatus for application aware memory patrol scrubbing techniques. The method may be performed on a computing system including one or more memory devices and running multiple applications with associated processes. The computer system may be implemented in a multi-tenant environment, where virtual instances of physical resources provided by the system are allocated to separate tenants, such as through virtualization schemes employing virtual machines or containers. Quality of Service (QoS) scrubbing logic and novel interfaces are provided to enable memory scrubbing QoS policies to be applied at the tenant, application, and/or process level. This QoS policies may include memory ranges for which specific policies are applied, as well as bandwidth allocations for performing scrubbing operations. A pattern generator is also provided for generating scrubbing patterns based on observed or predicted memory access patterns and/or predefined patterns.

Application aware memory patrol scrubbing techniques

The platform data aging for adaptive memory scaling described herein provides technical solutions for technical problems facing power management for electronic device processors. Technical solutions described herein include improved processor power management based on a memory region life-cycle (e.g., short-lived, long-lived, static). In an example, a short-term memory request is allocated to a short-term memory region, and that short-term memory region is powered down upon expiration of the lifetime of all short-term memory requests on the short-term memory region. Multiple memory regions may be scaled down (e.g., shut down) or scaled up based on demands for memory capacity and bandwidth.

Platform data aging for adaptive memory scaling

In an example, there is disclosed a host-fabric interface (HFI), including: an interconnect interface to communicatively couple the HFI to an interconnect; a network interface to communicatively couple the HFI to a network; network interface logic to provide communication between the interconnect and the network; a coprocessor configured to provide an offloaded function for the network; a memory; and a caching agent configured to: designate a region of the memory as a shared memory between the HFI and a core communicatively coupled to the HFI via the interconnect; receive a memory operation directed to the shared memory; and issue a memory instruction to the memory according to the memory operation.

Shared memory for intelligent network interface cards

There is disclosed in one example an application-specific integrated circuit (ASIC), including: an artificial intelligence (AI) circuit; and circuitry to: identify a flow, the flow including traffic diverted from a core cloud service of a network to be serviced by an edge node closer to an edge of the network than to the core of the network; receive telemetry related to the flow, the telemetry including fine-grained and flow-level network monitoring data for the flow; operate the AI circuit to predict, from the telemetry, a future service-level demand for the edge node; and cause a service parameter of the edge node to be tuned according to the prediction.

Scalable edge computing

In accordance with embodiments disclosed herein, there are provided methods, systems, mechanisms, techniques, and apparatuses for presentation of direct accessed storage under a logical drive model; for implementing a distributed architecture for cooperative NVM Data protection; data mirroring for consistent SSD latency; for boosting a controller's performance and RAS with DIF support via concurrent RAID processing; for implementing arbitration and resource schemes of a doorbell mechanism, including doorbell arbitration for fairness and prevention of attack congestion; and for implementing multiple interrupt generation using a messaging unit and NTB in a controller through use of an interrupt coalescing scheme.

Presentation of direct accessed storage under a logical drive model

There is disclosed an example of an artificial intelligence (AI) system, including: a first hardware platform; a fabric interface configured to communicatively couple the first hardware platform to a second hardware platform; a processor hosted on the first hardware platform and programmed to operate on an AI problem; and a first training accelerator, including: an accelerator hardware; a platform inter-chip link (ICL) configured to communicatively couple the first training accelerator to a second training accelerator on the first hardware platform without aid of the processor; a fabric ICL to communicatively couple the first training accelerator to a third training accelerator on a second hardware platform without aid of the processor; and a system decoder configured to operate the fabric ICL and platform ICL to share data of the accelerator hardware between the first training accelerator and second and third training accelerators without aid of the processor.

System decoder for training accelerators

An embodiment of an electronic apparatus may include one or more substrates, and logic coupled to the one or more substrates, the logic to process memory operation requests from a memory controller, and provide a front end interface to remote pooled memory hosted at a near edge device. An embodiment of another electronic apparatus may include local memory and logic communicatively coupled the local memory, the logic to allocate a range of the local memory as remote pooled memory, and provide a back end interface to the remote pooled memory for memory requests from a far edge device. Other embodiments are disclosed and claimed.

Remote pooled memory device

Systems, apparatuses and methods may provide for a memory controller to manage quality of service enforcement and migration between local and pooled memory. A memory controller may include logic to communicate with a local memory and with a pooled memory controller to track memory page usage on a per application basis, instruct the pooled memory controller to perform a quality of service enforcement in response to a determination that an application is latency bound or bandwidth bound, wherein the determination that the application is latency bound or bandwidth bound is based on a cycles per instruction determination, and instruct a Direct Memory Access engine to perform a migration from a remote memory to the local memory in response to a determination that the quality of service cannot be enforced.

Memory controller to manage quality of service enforcement and migration between local and pooled memory

An embodiment of an electronic apparatus may include one or more substrates, and logic coupled to the one or more substrates, the logic to allocate a first memory portion to a first application as a combination of a local memory and remote memory, wherein the remote memory is shared between multiple compute nodes, and manage a first memory balloon associated with the first memory portion based on two or more memory tiers associated with the local memory and the remote memory. Other embodiments are disclosed and claimed.

Ballooning for multi-tiered pooled memory

An apparatus and/or system is described including a memory device including a memory range and a temporal data management unit (TDMU) coupled to the memory device to receive from an interface, the memory range and a temporal range corresponding to validity of data in the memory range, check the temporal range against a time and/or date value provided by a timer or clock to identify the data in the memory range as expired, and invalidate the data that is expired in the memory device. In some embodiments, the TDMU includes hardware logic that resides on a memory module with the memory device and is coupled to invalidate expired data when the memory module is decoupled from the interface. Other embodiments may be disclosed and claimed.

Apparatuses, systems, and methods for invalidating expired memory

Embodiments may be generally directed to techniques to cause communication of a registration request between a first end-point and a second end-point of an end-to-end path, the registration request to establish resource load monitoring for one or more resources of the end-to-end path, receive one or more acknowledgements indicating resource loads for each of the one or more resources of the end-to-end path, at least one of the acknowledgements to indicate a resource of the one or more resources is not meeting a threshold requirement for the end-to-end path, and perform an action for communication traffic utilizing the one or more resources based on the acknowledgement.

Techniques to control quality of service for end-to-end paths in a compute environment

Data management for edge architected computing systems extends current storage and memory schemes of edge resources to expose interfaces to allow a device, such as an endpoint or client device, or another edge resource, to specify criteria for managing data originating from the device and stored in an edge resource, and extends the storage and memory controllers to manage data in accordance with the criteria, including removing stored data that no longer satisfies the criteria. The criteria includes a temporal hint to specify a time after which the data can be removed, a physical hint to specify a list of edge resources outside of which the data can be removed, an event-based hint to specify an event after which the data can be removed, and a quality of service condition to modify the time specified in the temporal hint based on a condition, such as memory and storage capacity of the edge resource in which the data is managed.

Data management for edge architectures

Growing community of inventors

Phoenix, AZ, United States of America

Mark A Schmisseur