FAQ: What Tools Honor the Parallel Processing Factor Environment Setting in ArcGIS Pro?

Answer

The Parallel Processing Factor environment setting uses the computer’s multi-core CPUs by dividing and performing operations across multiple processes to speed up the performance of geoprocessing tools. Only certain tools honor this environment setting. This article compiles the tools that honor the Parallel Processing Factor environment setting in ArcGIS Pro.

Note:
To view a tool's environment settings, navigate to:
• The Environments section of the tool reference page.
• The Environments tab in the tool's geoprocessing pane in ArcGIS Pro.

The table below lists the tools by toolbox that honor the Parallel Processing Factor environment setting:


Toolbox	Tool
3D Analyst	Classify LAS Building Contour Contour List Geodesic Viewshed Intersect 3D Lines Intervisibility Prepare Point Cloud Training Data Reclassify Regularize Building Footprint
Analysis	Apportion Polygon Buffer Clip Erase Graphic Buffer Identity Intersect Pairwise Buffer Pairwise Clip Pairwise Dissolve Pairwise Erase Pairwise Integrate Pairwise Intersect Split Symmetrical Difference Union Update
Business Analyst	Enrich Layer
Conversion	Multipatch to Raster
Data Management	Add Rasters To Mosaic Dataset Analyze Mosaic Dataset Build Boundary Build Footprints Build Mosaic Dataset Item Cache Build Overviews Build Pyramids Build Pyramids And Statistics Build Seamlines Calculate Cell Size Ranges Check Geometry Clip Raster Color Balance Mosaic Dataset Composite Bands Compute Camera Model Compute Control Points Compute Fiducials Compute Mosaic Candidates Compute Tie Points Consolidate Layer Consolidate Map Consolidate Project Copy Raster Create Building Scene Layer Content Create Color Composite Create Map Tile Package Create Mobile Map Package Create Mobile Scene Package Create Ortho Corrected Raster Dataset Create Pansharpened Raster Dataset Define Overviews Export Mosaic Dataset Items Export Tile Cache Extract Subdataset Flip Generate Point Cloud Generate Raster Collection Generate Raster From Raster Function Generate Table From Raster Function Import Tile Cache Interpolate From Point Cloud Manage Tile Cache Match Control Points Mirror Mosaic Mosaic To New Raster Package Layer Package Map Package Project Project Raster Register Raster Repair Geometry Repair Mosaic Dataset Paths Resample Rescale Rotate Shift Split Raster Subdivide Polygon Synchronize Mosaic Dataset Transfer Files Warp Warp From File
GeoAnalytics Desktop	Aggregate Points Calculate Density Calculate Field Clip Layer Copy Dataset From Multifile Feature Connection Create Buffers Describe Dataset Detect Incidents Dissolve Boundaries Find Dwell Locations Find Hot Spots Find Point Clusters Find Similar Locations Forest-based Classification and Regression Generalized Linear Regression Join Features Overlay Layers Reconstruct Tracks Snap Tracks Summarize Attributes Summarize Center And Dispersion Summarize Within
Geostatistical Analyst	Areal Interpolation Layer To Polygons Compare Geostatistical Layers Create Geostatistical Layer Cross Validation Densify Sampling Network Diffusion Interpolation With Barriers EBK Regression Prediction Empirical Bayesian Kriging Empirical Bayesian Kriging 3D Exploratory Interpolation GA Layer 3D To Multidimensional Raster GA Layer 3D To NetCDF GA Layer To Contour GA Layer To Grid GA Layer To Points GA Layer To Rasters Gaussian Geostatistical Simulations Generate Subset Polygons Global Polynomial Interpolation IDW Kernel Interpolation With Barriers Local Polynomial Interpolation Moving Window Kriging
Image Analyst	Aggregate Multidimensional Raster Analyze Changes Using CCDC Analyze Changes Using LandTrendr Apply Geometric Terrain Correction Apply Radiometric Calibration Classify Objects Using Deep Learning Classify Pixels Using Deep Learning Classify Raster Compute Change Raster Compute Segment Attributes Convert SAR Units Create Color Composite Despeckle Detect Change Using Change Analysis Raster Detect Change Using Deep Learning Detect Objects Using Deep Learning Dimensional Moving Statistics Export Training Data For Deep Learning Find Argument Statistics Focal Statistics Generate Multidimensional Anomaly Generate Trend Raster Inspect Training Samples Linear Spectral Unmixing Multidimensional Principal Components Predict Using Regression Model Predict Using Trend Raster Remove Raster Segment Tiling Artifacts Remove Thermal Noise Sample Segment Mean Shift Train Deep Learning Model Train ISO Cluster Classifier Train Support Vector Machine Classifier Weighted Sum Zonal Statistics Zonal Statistics as Table
Intelligence	Classify Movement Events Compare Areas Find Cotravelers Find Frequented Locations Find Meeting Locations
Location Referencing	Generate Calibration Points Generate Routes Remove Overlapping Centerlines
Multidimension	Build Multidimensional Transpose Make Multidimensional Raster Layer Manage Multidimensional Raster Merge Multidimensional Rasters Subset Multidimensional Raster
Raster Analysis	Classify Objects Using Deep Learning Classify Pixels Using Deep Learning Detect Objects Using Deep Learning
Space Time Pattern Mining	Change Point Detection Create Space Time Cube By Aggregating Points Create Space Time Cube From Defined Locations Create Space Time Cube From Multidimensional Raster Layer Forest-based Forecast Local Outlier Analysis Time Series Clustering
Spatial Analyst	Aggregate Boundary Clean Calculate Kernel Density Ratio Classify Raster Compute Segment Attributes Contour Contour List Distance Accumulation Distance Allocation Expand Export Training Data For Deep Learning Fill Flow Accumulation Flow Direction Flow Distance Focal Statistics Generate Training Samples From Seed Points Geodesic Viewshed Inspect Training Samples Kernel Density Linear Spectral Unmixing Nibble Reclassify Remove Raster Segment Tiling Artifacts Sample Segment Mean Shift Shrink Sink Storage Capacity Stream Link Train ISO Cluster Classifier Train Support Vector Machine Classifier Watershed Weighted Overlay Weighted Sum Zonal Statistics Zonal Statistics as Table
Spatial Statistics	Build Balanced Zones Colocation Analysis Density-based Clustering Forest-based Classification and Regression Local Bivariate Relationships Multiscale Geographically Weighted Regression (MGWR) Presence-only Prediction (MaxEnt) Spatial Outlier Detection Spatially Constrained Multivariate Clustering