Ngd Systems, Inc.

Vincent Lazo

Joao Alcantara

Ricardo Cassia

Kamyar Souri

Vladimir Alves

A system and method for combining the execution of a query with other operations, such as a data retention scan, in a storage device, when the execution of the query is not time-sensitive. The storage device may be connected to a host, and may operate during intervals of time in a power save mode. When a query is received by the host that is not time-sensitive, the query may be stored in the host or in the storage device until such time as the device would otherwise return to a normal operating mode, and then the query may be executed. Such delayed execution may enable the sharing of read operations for the query with read operations used, for example, for the execution of other queries or for a data retention scan.

Power efficient method and system for executing host data processing tasks during data retention operations in a storage device

A method of writing multi-stream host data to a storage device comprising a CPU, a multi-stream fast release buffer (FRB), and a non-volatile memory (NVM), includes: receiving a command to write the multi-stream host data to the NVM, the multi-stream host data being associated with a logical block number (LBN) and a new stream ID, recording a status of the active stream ID and retrieving a status of the new stream ID to determine a physical address in the NVM for storing one or more codewords (CWs) corresponding to the multi-stream host data, allocating space in a buffer of the FRB for storage of the multi-stream host data, organizing the multi-stream host data into the one or more CWs, and storing the one or more CWs into the allocated space in the buffer, transferring the one or more CWs from the buffer to the physical address in the NVM.

System and method for multi-stream data write

A system and method of providing enhanced data processing and analysis in an infrastructure for distributed computing and large-scale data processing. This infrastructure uses the Apache Spark framework to divide an application into a large number of small fragments of work, each of which may be performed on one of a large number of compute nodes. The work may involve Spark transformations, operations, and actions, which may be used to categorize and analyze large amounts of data in distributed systems. This infrastructure includes a cluster with a driver node and a plurality of worker nodes. The worker nodes may be, or may include, intelligent solid state drives capable of executing data processing functions under the Apache Spark framework. The use of intelligent solid state drives reduces the need to exchange data with a central processing unit (CPU) in a server.

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San Diego, CA, United States of America

Vincent Lazo