A memory comprising: media access circuitry coupled to a memory media having a cross point architecture, wherein the media access circuitry comprises a plurality of scratch pads and multiple compute logic units, and is configured to: access matrix data from the memory media, including: reading, during each time period within a set of time periods, a different subset with dimensions B_K and B_N of one matrix with dimensions K and N from a corresponding partition of N partitions (311 321 331) of the memory media, writing each different subset of the one matrix to a corresponding scratch pad (132; 314, 324, 334) associated with the corresponding partition (311, 321, 331), reading, during each time period within another set of time periods, a different subset with dimensions B_M and B_K of another matrix with dimensions M and K from a corresponding partition of N partitions (311, 321, 331) of the memory media, writing each different subset of the other matrix to a corresponding scratch pad (132; 312, 322, 332) associated with the corresponding partition (311, 312, 331), broadcasting, in each time period, the read subset of the other matrix to the scratch pads (132; 312, 322, 332) associated with the N-1 other partitions of the memory media; perform, with each of the multiple compute logic units (136; 318, 328, 338) associated with the different N partitions (311, 321, 331) of the memory media, tensor operations on the matrix data in the scratch pads (132; 312, 314, 322, 324, 332, 334) associated with the partitions (311, 321, 331) the compute logic units (136; 318, 328, 338) are associated with; and write, to the memory media, resultant data indicative of a result of the tensor operations. access matrix data from the memory media, including: reading, during each time period within a set of time periods, a different subset with dimensions B_K and B_N of one matrix with dimensions K and N from a corresponding partition of N partitions (311 321 331) of the memory media, writing each different subset of the one matrix to a corresponding scratch pad (132; 314, 324, 334) associated with the corresponding partition (311, 321, 331), reading, during each time period within another set of time periods, a different subset with dimensions B_M and B_K of another matrix with dimensions M and K from a corresponding partition of N partitions (311, 321, 331) of the memory media, writing each different subset of the other matrix to a corresponding scratch pad (132; 312, 322, 332) associated with the corresponding partition (311, 312, 331), broadcasting, in each time period, the read subset of the other matrix to the scratch pads (132; 312, 322, 332) associated with the N-1 other partitions of the memory media; reading, during each time period within a set of time periods, a different subset with dimensions B_K and B_N of one matrix with dimensions K and N from a corresponding partition of N partitions (311 321 331) of the memory media, writing each different subset of the one matrix to a corresponding scratch pad (132; 314, 324, 334) associated with the corresponding partition (311, 321, 331), reading, during each time period within another set of time periods, a different subset with dimensions B_M and B_K of another matrix with dimensions M and K from a corresponding partition of N partitions (311, 321, 331) of the memory media, writing each different subset of the other matrix to a corresponding scratch pad (132; 312, 322, 332) associated with the corresponding partition (311, 312, 331), broadcasting, in each time period, the read subset of the other matrix to the scratch pads (132; 312, 322, 332) associated with the N-1 other partitions of the memory media; perform, with each of the multiple compute logic units (136; 318, 328, 338) associated with the different N partitions (311, 321, 331) of the memory media, tensor operations on the matrix data in the scratch pads (132; 312, 314, 322, 324, 332, 334) associated with the partitions (311, 321, 331) the compute logic units (136; 318, 328, 338) are associated with; and write, to the memory media, resultant data indicative of a result of the tensor operations.