International Business Machines Corporation

Jason Szecheong Ma

Timothy J Fisher

Aaron Daniel Fry

Michael C Hollinger

Thomas W Aarts

Gundam Raghuswamyreddy

Jose Luis Ortiz

Ched D Hays

Roman Alexander Pletka

Nikolas Ioannou

Sasa Tomic

Lincoln T Simmons

Adalberto Guillermo Yanes

Kevin E Sallese

Charles A Keller

Van Huynh

Nikolaos Papandreou

Ioannis Koltsidas

Razik S Ahmed

Matthew R Orr

Charles John Camp

A computer-implemented method, according to one embodiment, includes: receiving a stream of data, and selecting more than one block of memory to write the stream of data to. The selected blocks of memory are in a memory that includes a plurality of blocks. Moreover, the data is written across the selected blocks of memory in parallel. The blocks of memory are also selected such that no two or more of the selected blocks of memory have an effect on a read apparent voltage of a same one of the plurality of blocks in the memory. Other systems, methods, and computer program products are described in additional embodiments.

Reducing effects of read array operations of read apparent voltage

A technique for garbage collection in a storage system includes generating regrouping metadata for one or more pages of at least two logical erase blocks (LEB). The regrouping metadata indicates an associated stream for each of the pages. Multiple of the LEBs that include valid pages associated with a first stream are selected, based on the regrouping metadata, for regrouping. The valid pages associated with the first stream from the selected LEBs are regrouped into a new LEB.

Regrouping data during relocation to facilitate write amplification reduction

Read Apparent Voltage (RAV) is an anomality in which an apparent threshold voltage of a storage cell transistor does not equal the actual threshold voltage of that same transistor by a large enough magnitude that the binary state of transistor is not read correctly. An infector page may cause the RAV anomality within a different infected page. To determine whether any page is an infector, each page is programmed, a page within each block is read, an acting infector page within an acting infector block is set, a possible infected page within a possible infected block is set, the acting infector page is read a predetermined plurality of instances, the possible infected page is read, a raw bit error rate (RBER) of the read of the possible infected page is determined, and the acting infector page is set as an actual infector page based upon the determined RBER.

Determining a read apparent voltage infector page and infected page

A data storage system includes a non-volatile memory array controlled by a controller that records a number of a plurality of like operations targeting a first block among a plurality of blocks in the non-volatile memory array. In response to the number of the plurality of like operations satisfying a threshold, the controller initiates a mitigation read request by recording an identifier of a second block in a high priority request in a mitigation data structure. The controller initiates other mitigation read requests by recording identifiers of other blocks of the non-volatile memory in low priority requests in the mitigation data structure. The controller preferentially services the high priority request from the mitigation data structure over the low priority requests, where servicing the high priority request includes performing a mitigation read to the second block.

Reducing read errors by performing mitigation reads to blocks of non-volatile memory

In at least one embodiment, a controller of a non-volatile memory array including a plurality of subdivisions stores write data within the non-volatile memory array utilizing a plurality of block stripes of differing numbers of blocks, where all of the blocks within each block stripe are drawn from different ones of the plurality of subdivisions. The controller builds new block stripes for storing write data from blocks selected based on estimated remaining endurances of blocks in each of the plurality of subdivisions.

Wear leveling of a memory array

A technique for garbage collection in a data storage system includes determining a dirty physical byte count for each of a plurality of candidate garbage collection units. The dirty physical byte count provides a total amount of dirty bytes. At least one of a dirty physical codeword container count and a dirty physical page count is determined for each of the candidate garbage collection units. The dirty physical codeword container count provides an amount of physical codeword containers that are completely dirty and the dirty physical page count provides an amount of physical pages that are completely dirty. A garbage collection unit, included in the candidate garbage collection units, is selected for garbage collection based on the dirty physical byte count and at least one of the dirty physical codeword container count and the dirty physical page count.

Garbage collection techniques for a data storage system

Mechanisms are provided for controlling a fidelity of a simulation of a computer system. A model of the system is received that has a plurality of components. A representation of the plurality of individual components of the system is generated. A component is assigned to be a fidelity center having a highest possible associated fidelity value. Fidelity values are assigned to each other component in the plurality of individual components based on an affinity of the other component to the fidelity center. The system is simulated based on assigned fidelity values to the components in the plurality of individual components.

Determining simulation fidelity in a self-optimized simulation of a complex system

An approach is provided by detecting password entries by a user of a system with each of the password entries corresponding to a password entered when invoking a password enabled application on the system. A password strength is calculated corresponding to each of the detected password entries. A strongest password is identified based on the password strength calculations. A device lock request is received from the user of the system, with the device lock request being received at a user interface, such as a GUI control. The system is set into a locked state. The locked state prevents data stored on the information handling system from being accessed while the information handling system is in the locked state. The system is subsequently unlocked by entry of the identified strongest password by the user.

Dynamic password strength dependent on system state

Mechanisms are provided for controlling a fidelity of a simulation of a system. A model of the system is received, the model of the system having a plurality of individual components of the system. Fidelity values are assigned to models of the individual components of the system. A simulation of the system is executed using the model of the system and the models of the individual components of the system. For each component in the plurality of individual components of the system, an activity level of the component during execution of the simulation is determined. The fidelity values of one or more of the models of the individual components of the system are dynamically adjusted during the execution of the simulation based on changes in individual activity levels of the individual components.

Dynamically adjusting simulation fidelity based on changes in activity levels of components

Mechanisms are provided for controlling a fidelity of a simulation of a system. A model of the system is received, the model of the system having a plurality of individual components of the system. Fidelity values are assigned to models of the individual components of the system. A simulation of the system is executed using the model of the system and the models of the individual components of the system. The fidelity values of one or more of the models of the individual components of the system are dynamically adjusted during the execution of the simulation by creating a checkpoint of a state of the simulation and modifying one or more fidelity values of one or more of the models of the individual components after generating the checkpoint, thereby generating a modified fidelity value state.

Dynamically adjusting simulation fidelity based on checkpointed fidelity state

Mechanisms are provided for controlling a fidelity of a simulation of a system. A model of the system is received, the model of the system having a plurality of individual components of the system. Fidelity values are assigned to models of the individual components of the system. A required fidelity value is assigned to transactions between components in the plurality of individual components of the system. A simulation of the system is executed using the model of the system and the models of the individual components of the system. The fidelity values of one or more of the models of the individual components of the system are dynamically adjusted during the execution of the simulation based on the required fidelity values assigned to the transactions.

Dynamically adjusting simulation fidelity in a self-optimized simulation of a complex system

Mechanisms are provided for controlling a fidelity of a simulation of a system. A model of the system is received, where the model has a plurality of individual components of the system. Fidelity values are assigned to models of the individual components of the system and a simulation of the system is executed using the model of the system and the models of the individual components of the system. The simulation comprises a plurality of transactions targeting a receiver component. A history of mismatches between a fidelity value associated with the receiver component and required fidelity values of the plurality of transactions targeting the receiver component is maintained. A prediction of a fidelity value to be assigned to the receiver component based on the history of mismatches is performed. A fidelity value of the receiver component is adjusted based on results of predicting the fidelity value to be assigned to the receiver component.

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Austin, TX, United States of America

Jason Szecheong Ma