Abstract: In some examples, a client system, in response to a request to modify a first data page at a memory server in a remote access by a client over a network, sends, to the memory server, a request to update a data modification tracking structure stored by the memory server to indicate that the first data page is modified. The client system initiates an incremental data backup from the memory server to a backup storage system of data pages indicated as modified by the data modification tracking structure stored at the memory server.

Abstract: In some examples, a computer identifies a plurality of memory servers accessible by the computer to perform remote access over a network of data stored by the plurality of memory servers, sends allocation requests to allocate memory segments to place interleaved data of the computer across the plurality of memory servers, and receives, at the computer in response to the allocation requests, metadata relating to the memory segments at the plurality of memory servers, the metadata comprising addresses of the memory segments at the plurality of memory servers. The computer uses the metadata to access, by the computer, the interleaved data at the plurality of memory servers, the interleaved data comprising blocks of data distributed across the memory segments.

Abstract: In an example, a first metadata tag and a second metadata tag are added to first Personally Identifiable Information (PII) of a first user handled by a first application. The first PII is to be part of call home data captured from a hosting system. The first metadata tag may be indicative of security rules to be complied with for the first application and the second metadata tag may be indicative of security rules to be complied with for the first user. The first PII, the first metadata tag, and the second metadata tag may be protected and transmitted to a data processing center. The transmission may be in response to a determination to transmit the call home data.

Abstract: Repeating patterns are identified in a matrix. Based on the identification of the repeating patterns, instructions are generated, which are executable by processing cores of a dot product engine to allocate analog multiplication crossbars of the dot product engine to perform multiplication of the matrix with a vector.

Abstract: Repeating patterns are identified in a matrix. Based on the identification of the repeating patterns, instructions are generated, which are executable by processing cores of a dot product engine to allocate analog multiplication crossbars of the dot product engine to perform multiplication of the matrix with a vector.

Abstract: In an example, a first metadata tag and a second metadata tag are added to first Personally Identifiable Information (PII) of a first user handled by a first application. The first PII is to be part of call home data captured from a hosting system. The first metadata tag may be indicative of security rules to be complied with for the first application and the second metadata tag may be indicative of security rules to be complied with for the first user. The first PII, the first metadata tag, and the second metadata tag may be protected and transmitted to a data processing center. The transmission may be in response to a determination to transmit the call home data.

Abstract: Systems and methods are provided for sparse matrix vector multiplication with a matrix vector multiplication unit. The method includes partitioning a sparse matrix of entries into a plurality of sub-matrices; mapping each of the sub-matrices to one of a plurality of respective matrix vector multiplication engines; partitioning an input vector into a plurality of sub-vectors; computing, via each matrix vector multiplication engine, a plurality of intermediate result vectors each resulting from a multiplication of one of the sub-matrices and one of the sub-vectors; for each set of rows of the sparse matrix, adding elementwise the intermediate result vectors to produce a plurality of result sub-vectors; and concatenating the result sub-vectors to form a result vector.

Abstract: Example implementations relate to domain specific programming language (DSL) compiler for large scale sparse matrices. A method can comprise partitioning a sparse matrix into a plurality of submatrices based on a sparse matrix representation and inputting each one of the submatrices into a respective one of a plurality of matrix-vector multiplication units (MVMUs) of a crossbar-based architecture.

Abstract: Systems and methods are provided for sparse matrix vector multiplication with a matrix vector multiplication unit. The method includes partitioning a sparse matrix of entries into a plurality of sub-matrices; mapping each of the sub-matrices to one of a plurality of respective matrix vector multiplication engines; partitioning an input vector into a plurality of sub-vectors; computing, via each matrix vector multiplication engine, a plurality of intermediate result vectors each resulting from a multiplication of one of the sub-matrices and one of the sub-vectors; for each set of rows of the sparse matrix, adding elementwise the intermediate result vectors to produce a plurality of result sub-vectors; and concatenating the result sub-vectors to form a result vector.