Understanding Parallel Computing in R and the `knn2nb` Library: Speeding Up Neighbor Computation with Multicore Computing

Understanding Parallel Computing in R and the `knn2nb` Library

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As a data analyst or scientist working with large datasets, it’s common to encounter challenges related to processing and analyzing these datasets. One such challenge is dealing with computationally intensive tasks, such as determining the nearest neighbors for a given dataset. In this article, we’ll explore how to use parallel computing in R to speed up such computations using the knn2nb library.

The Problem: Determining Nearest Neighbors

Given a large dataset containing n polygons, the task of determining the k closest polygons with the knn2nb library can be computationally intensive. This is especially true when performing sensitivity tests, where we need to evaluate the library for various values of k (from 1 to n). In this scenario, using R’s built-in parallel computing capabilities can significantly speed up the computation.

The Approach: Using `lapply` and Parallel Computing

To solve this problem, we’ll use R’s built-in lapply function in combination with parallel computing. This approach allows us to take advantage of multiple CPU cores, thereby reducing the overall processing time.

Serial Computation using `lapply`

# Create neighbours list    
lapply(1:n, function(k) knn2nb(knearneigh(coords, k=k), row.names=coordsID)

This code snippet demonstrates how to compute the nearest neighbors for each value of k (from 1 to n) using lapply. However, as our dataset is large, this computation can be quite slow.

Parallel Computation using `parLapply`

# Calculate the number of cores
no_cores <- detectCores() - 1

# Initiate cluster
cl <- makeCluster(no_cores)

# Create neighbours list
parLapply(cl, 1:n, function(k) knn2nb(knearneigh(coords, k=k), row.names=coordsID))

In this code snippet, we use parLapply to parallelize the computation. This approach allows us to utilize multiple CPU cores, thereby speeding up the computation.

The Issue: Missing Functions in the Cluster

However, when using parLapply, we encounter an error related to missing functions in the cluster:

Error in checkForRemoteErrors(val) :
  7 nodes produced errors; first error: impossible to find the function "knn2nb"

This error message suggests that the knn2nb library is not available in the cluster. To resolve this issue, we need to export the necessary functions from the library.

Exporting Functions from the Library

To fix this issue, we can use the clusterExport function to export the required functions from the library:

# Calculate the number of cores
no_cores <- detectCores() - 1

# Initiate cluster
cl <- makeCluster(no_cores)

# Export necessary functions from the library
clusterExport(cl, c("knn2nb", "knearneigh", "coords", "coordsID"), envir=environment())

# Create neighbours list
parLapply(cl, 1:n, function(k) knn2nb(knearneigh(coords, k=k), row.names=coordsID))

By exporting the required functions from the library, we ensure that they are available in the cluster. This resolves the issue with missing functions and allows us to parallelize the computation effectively.

Conclusion

In this article, we explored how to use parallel computing in R to speed up the determination of nearest neighbors using the knn2nb library. We discussed the challenges related to processing large datasets and demonstrated how to use lapply and parLapply to parallelize the computation.

We also highlighted the importance of exporting necessary functions from the library to resolve issues with missing functions in the cluster. By following these steps, you can efficiently compute nearest neighbors for your dataset using parallel computing in R.

Additional Considerations

When working with large datasets and parallel computing, it’s essential to consider several factors:

Data size and complexity: As our dataset is large, we need to ensure that the computation can handle such data. This may require optimizing the code for performance or using more advanced techniques like distributed computing.
Available resources: The number of CPU cores available affects the performance of parallel computations. We should consider using multiple cores if possible and adjust the code accordingly.
Memory constraints: If our dataset does not fit in memory, we may need to use techniques like chunking or data storage in disk space.

By considering these factors and following best practices for parallel computing, you can efficiently compute nearest neighbors for your dataset and achieve significant performance improvements.

Last modified on 2023-05-06