Micron Technology Incorporated

Huawei Technologies Co., Limited

Splunk Inc.

Zhou Zhou

Wei Zhang

Jianfeng Li

Lin Huang

Huachen Li

Chen Huang

Chaofeng Zhang

Wei Wei

Ning He

Yang Cao

Yanhua Bi

Xiaolu Ye

Li Xin Zhao

Chong Yu

Yuwei Pu

Jie Cai

Bing Pei

Aiping Zhang

Methods, systems, and devices for improved implicit ordered command handling are described. For instance, a memory device may receive, from a host device, a first command and a second command. The memory device may determine whether a first memory operation associated with the first command and a second memory operation associated with the second command are to be performed in an order relative to each other based on a first time when the first command is received relative to a second time when the second command is received. The memory device may select whether to perform a first memory access procedure or a second memory access procedure based on whether the first memory operation and the second memory operation are a same type of memory operation and on whether the first memory operation and the second memory operation are to be performed in the order relative to each other.

Implicit ordered command handling

Methods, systems, and devices for memory system logical unit number (LUN) procedures are described. A memory system may receive an indication to convert a LUN for storing LBAs associated with an application from a first type to a second type, where the second type is associated with a higher performance defragmentation process than the first type. The memory system may perform defragmentation on data associated with the LUN based on converting the LUN to the second type. The memory system may determine whether the LBAs stored in the LUN are ordered based on the defragmentation, and the memory system may operate (e.g., execute) the application based on the LBAs being ordered.

Memory system logical unit number procedures

A dynamic power consumption estimation method includes recording a statistical value of each of n target signals of a to-be-measured target in a unit time, where the target signal is a clock signal obtained after clock gating and the statistical value is a quantity of times toggling, or the target signal is a clock gating signal and the statistical value is a quantity of enable cycles, and calculating the dynamic power consumption of the to-be-measured target in the unit time based on the statistical value of each of the n target signals in the unit time.

Dynamic power consumption estimation method, apparatus, and system

Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment. The predicted execution platform requirements can be deployed and tested to validate or tune the performance model.

Two-tier capacity planning

Growing community of inventors

Shanghai, China

Zhou Zhou