A little planning ahead can save a lot of time. Any external memory algorithm that is not “inherently sequential” can be parallelized; results for one chunk of data cannot depend upon prior results. Data.table is a venerable and powerful package written primarily by Matt Dowle. The crucial point is that these chunks of computation are unrelated and do not need to communicate in any way. 25% if you assign your tasks to 4 cores). The RevoScaleR functions rxRoc, and rxLorenz are other examples of ‘big data’ alternatives to functions that traditionally rely on sorting. Even though a data set may have many thousands of variables, typically not all of them are being analyzed at one time. Due to the overhead associated with communicating between the nodes, you won’t see much performance improvement on basic dplyr verbs with fewer than ~10 million observations. This abstraction is at a lower level than other abstractions such as caret’s, but it does allow us a significant amount of power to decide exactly how code is distributed across multiple cores. Dependence on data from a prior chunk is OK, but must be handled specially. Some additional background info. some packages use multicore processing implicitly (e.g. First of all, the parallel package (included in R 2.8.0+, no need to install) provides functions based on the snow package - these functions are extensions of lapply() . So, if the number of rows of your data set doubles, you can still perform the same data analyses—it will just take longer, typically scaling linearly with the number of rows. (Save the 64-bit doubles for computations). OpenBLAS). I got an i7 7760U. If the original data falls into some other range (for example, 0 to 1), scaling to a larger range (for example, 0 to 1,000) can accomplish the same thing. When data is processed in chunks, basic data transformations for a single row of data should in general not be dependent on values in other rows of data. There are two differences practitioners should be aware of: The main takeaway is that accessing global variables and dealing with global state will necessarily be different than when all code is executing in a single process. In this sample code, we leverage parallel to read a directory of 100 csv files from a folder in parallel. On Torque, you could count the node names listed in $PBS_NODEFILE using something like table(readLines(Sys.getenv('PBS_NODEFILE'))). Analysis functions are threaded to use multiple cores, and computations can be distributed across multiple computers (nodes) on a cluster or in the cloud. Massive hardware such as the AWS X1 instances, with 128 cores and 2 terabytes of RAM, have pushed the boundaries of what can be done without requiring complex, hard-to-manage, unfamiliar distributed systems. researching brings up mostly topics from around ~2010,which suggest using parallel or other packages to achieve multicore usage. These functions combine the advantages of external memory algorithms (see Process Data in Chunks preceding) with the advantages of High-Performance Computing. The analysis modeling functions in RevoScaleR use special handling of categorical data to minimize the use of memory when processing them; they do not generally need to explicitly create dummy variable to represent factors. But occasionally, output has the same number of rows as your data, for example, when computing predictions and residuals from a model. These days my favorite is doMC package, which depends on foreach and multicore packages. This is because not all of the factor levels may be represented in a single chunk of data. gettingstartedParallel.pdf It’s important to note that often optimal parallelism does not mean using all cores available, as it may saturate other resources and cause thrashing, so remember to experiment and benchmark. thanks for your reply, this supports parts of my asumptions! Besides the explicit use of parallel computing there are also a few places where processing might use multiple cores implicitly: R itself uses OpenMP for parallel execution in some places many matrix operations are handled by a BLAS/LAPCK library, which might use multiple cores (e.g. Leveraging larger datasets and more processing power allows data scientists to do more experiments, be more confident about solutions, and build more accurate models for the business. Indeed, much of the code in the base and recommended packages in R is written in this way—the bulk of the code is in R but a few core pieces of functionality are written in C, C++, or FORTRAN. It does by exploiting the parallel computing functionality that is now built into R. This is possible because the imputation (and also the analysis) of the different bootstrapped datasets can be performed completely …

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