Parallel and chunk-wise processing of Spectra

Many operations on Spectra objects, specifically those working with the actual MS data (peaks data), allow a chunk-wise processing in which the Spectra is splitted into smaller parts (chunks) that are iteratively processed. This enables parallel processing of the data (by data chunk) and also reduces the memory demand since only the MS data of the currently processed subset is loaded into memory and processed. This chunk-wise processing, which is by default disabled, can be enabled by setting the processing chunk size of a Spectra with the processingChunkSize() function to a value which is smaller than the length of the Spectra object. Setting processingChunkSize(sps) <- 1000 will cause any data manipulation operation on the sps, such as filterIntensity() or bin(), to be performed eventually in parallel for sets of 1000 spectra in each iteration.

Such chunk-wise processing is specifically useful for Spectra objects using an on-disk backend or for very large experiments. For small data sets or Spectra using an in-memory backend, a direct processing might however be more efficient. Setting the chunk size to Inf will disable the chunk-wise processing.

For some backends a certain type of splitting and chunk-wise processing might be preferable. The MsBackendMzR backend for example needs to load the MS data from the original (mzML) files, hence chunk-wise processing on a per-file basis would be ideal. The backendParallelFactor() function for MsBackend allows backends to suggest a preferred splitting of the data by returning a factor defining the respective data chunks. The MsBackendMzR returns for example a factor based on the dataStorage spectra variable. A factor of length 0 is returned if no particular preferred splitting should be performed. The suggested chunk definition will be used if no finite processingChunkSize() is defined. Setting the processingChunkSize overrides backendParallelFactor.

See the Large-scale data handling and processing with Spectra for more information and examples.

Functions to configure parallel or chunk-wise processing:

• processingChunkSize(): allows to get or set the size of the chunks for parallel processing or chunk-wise processing of a Spectra in general. With a value of Inf (the default) no chunk-wise processing will be performed.

• processingChunkFactor(): returns a factor defining the chunks into which a Spectra will be split for chunk-wise (parallel) processing. A factor of length 0 indicates that no chunk-wise processing will be performed.

Usage

processingChunkSize(x)

processingChunkSize(x) <- value

processingChunkFactor(x)

# S4 method for class 'Spectra'
backendBpparam(object, BPPARAM = bpparam())

Arguments

x

Spectra.

value

integer(1) defining the chunk size.

object

Spectra object.

BPPARAM

Parallel setup configuration. See bpparam() for more information.

Value

processingChunkSize() returns the currently defined processing chunk size (or Inf if it is not defined). processingChunkFactor() returns a factor defining the chunks into which x will be split for (parallel) chunk-wise processing or a factor of length 0 if no splitting is defined.

Note

Some backends might not support parallel processing at all. For these, the backendBpparam() function will always return a SerialParam() independently on how parallel processing was defined.

Author

Johannes Rainer