Hi all. I need to work with 4 large matrix variables repeatedly in sequence. But only one of them can fit in RAM at a time. (70 GB each, 128 GB of RAM). The operations are time-sensitive, so I need to be able to load one block of data, do some computations, release the memory, load the next block…and so on, as fast as possible. I have been looking at data store & tall arrays — I could concatenate the 4 into one tall array — but that seems intended for operations that have to span more rows than memory can hold, like taking a max across all of them. Here I just need access to one contiguous block that -will- all fit in memory, but to cycle through multiple of them. What is the preferred approach? I am using the parallel processing toolbox, but am not sure how to apply parallelization to this issue in a way that doesn’t just add more overhead. Currently I save data into uncompressed v7.3 .mat files, but I can convert to whatever helps. Thanks in advance for your insights!Hi all. I need to work with 4 large matrix variables repeatedly in sequence. But only one of them can fit in RAM at a time. (70 GB each, 128 GB of RAM). The operations are time-sensitive, so I need to be able to load one block of data, do some computations, release the memory, load the next block…and so on, as fast as possible. I have been looking at data store & tall arrays — I could concatenate the 4 into one tall array — but that seems intended for operations that have to span more rows than memory can hold, like taking a max across all of them. Here I just need access to one contiguous block that -will- all fit in memory, but to cycle through multiple of them. What is the preferred approach? I am using the parallel processing toolbox, but am not sure how to apply parallelization to this issue in a way that doesn’t just add more overhead. Currently I save data into uncompressed v7.3 .mat files, but I can convert to whatever helps. Thanks in advance for your insights! Hi all. I need to work with 4 large matrix variables repeatedly in sequence. But only one of them can fit in RAM at a time. (70 GB each, 128 GB of RAM). The operations are time-sensitive, so I need to be able to load one block of data, do some computations, release the memory, load the next block…and so on, as fast as possible. I have been looking at data store & tall arrays — I could concatenate the 4 into one tall array — but that seems intended for operations that have to span more rows than memory can hold, like taking a max across all of them. Here I just need access to one contiguous block that -will- all fit in memory, but to cycle through multiple of them. What is the preferred approach? I am using the parallel processing toolbox, but am not sure how to apply parallelization to this issue in a way that doesn’t just add more overhead. Currently I save data into uncompressed v7.3 .mat files, but I can convert to whatever helps. Thanks in advance for your insights! memory management, tall arrays MATLAB Answers — New Questions

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