Emc IP Holding Company LLC

Dell Products Inc.

Emc Corporation

Gabriel Hershkovitz

Arieh Don

Peng Yin

Jerome Cartmell

Ningdong Li

Roii Raz

Robert Lucey

Michael R Barber

James McGillis

Ghil Iancovici

Li Lang

Thomas Rogers

Ashutosh Lakhani

Backend processes in a storage system are implemented using a plurality of worker threads that are divided into two thread groups: thread group A and thread group B. Threads of thread group A are able to be used to process both Random Read Miss (RRM) workload items and Random Write Miss (RWM) workload items. Threads of thread group B are divided into two thread sub-groups: thread sub-group B1 and thread sub-group B2. Threads of thread sub-group B1 are able to be used to process both RRM and RWM workload items, while threads of thread sub-group B2 are reserved to be used to process only RRM workload items. The size of thread sub-group B2 relative to the size of thread sub-group B1 changes over time (from 0 threads to all threads of thread group B), depending on the duty cycle of the threads of group A.

Optimizing backend workload processing in a storage system

Improved mechanisms and techniques for recording and aggregating trace information from multiple computing modules of a storage system may be provided. On a storage system having multiple computing modules, where each computing module has multiple processing cores, processing cores may record trace information for I/O operations in dedicated local memory&#x2014;i.e., memory in the same computing module as the processing core that is dedicated to the computing module. One of the processing cores may be configured to aggregate trace information from across multiple computing modules into its dedicated local memory by accessing trace information from the dedicated local memories of the other computing modules in addition to its own. The aggregated information in one dedicated local memory then may be analyzed for functionality and/or performance and additional action taken based on the analysis.

Multi-core I/O trace analysis

Techniques for memory management may include: allocating, from an allocation pool, buffers for logs used by processing cores; recording messages in the logs for the processing cores; responsive to filling a first buffer included in a first log used to record messages for a first of the plurality of processing cores, allocating a second buffer of the allocation pool for the first log; adding the second buffer to the first list of buffers for the first log; and adding the first buffer, that is included in the first list for the first log, to the allocation pool, wherein after adding the first buffer to the allocation pool, the first buffer is included in the first list of buffers for the first log and also included in the allocation pool. The buffers may be included in a distributed global memory portion of the same computing module as the processing cores.

Shared memory management

Described are techniques for processing a request to update a globally shared data item. The request is received at a first processor designated as the master processor. Other processors are designated as non-master processors. The first processor sends a synchronization request message to the other processors. The first processor waits a predetermined amount of time for the other processors to signal to the first processor regarding commitment in executing the request. Responsive to the first processor receiving the signal from the other processors first processing is performed that includes the first processor notifying the other processors regarding completion of a first synchronization point, updating the globally shared data item by said first processor, and waiting, by the other processors, for notification from the first processor regarding completion of a second synchronization point thereby indicating that updating of the globally shared data item is completed.

Framework for performing updates of globally shared data in a multiprocessor environment

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Newton, MA, United States of America

Gabriel Hershkovitz