Plan: Implement Pearce-Kelly algorithm for incremental topological ordering ✅ COMPLETE

The goal is to optimize transitiveDirtyListBottomUpDiff by using the Pearce-Kelly algorithm to maintain a running topological order of the dependency graph.

Implementation Plan

• Add Pearce-Kelly topological order maintenance to Database
  • Add a TVar for the topological order in SchedulerState
  • Add helper functions for Pearce-Kelly algorithm (reorder, shift operations)
• Update dependency tracking to maintain topological order incrementally
  • Update order when edges are added (in updateReverseDeps)
  • Update order when edges are removed
• Optimize transitiveDirtyListBottomUpDiff to use the maintained order
  • Use the topological order to compute affected keys in linear time
  • Filter and sort affected keys using the pre-computed order
• Add documentation
• Verify tests pass
• Address review feedback
  • Use StmContainers.Map instead of TVar (Map.HashMap ...) for better STM performance
  • Remove unused getOrderedList function with O(n log n) complexity

Recent Changes (Review Feedback)

Changed TopoOrder to use StmContainers.Map:

• Replaced TVar (Map.HashMap Key Int) with SMap.Map Key Int
• Consistent with the rest of the codebase's use of STM containers
• Better performance for concurrent STM operations
• Simplified implementation without repeated readTVar/writeTVar operations

Removed getOrderedList function:

• Was not used anywhere in the codebase
• Had O(n log n) complexity due to sorting
• Eliminated unnecessary overhead

Implementation Details

New Module: TopoSort.hs

Created a new module Development.IDE.Graph.Internal.TopoSort implementing the Pearce-Kelly algorithm:

• Maintains a mapping of keys to integer order values using StmContainers.Map
• Implements addEdge to incrementally update order when new dependencies are added
• Provides getAffectedKeysInOrder to efficiently sort keys by topological order

Updated Types.hs

Added TopoOrder data structure to SchedulerState:

• Stores the topological order map using SMap.Map Key Int
• Integrated into the scheduler state for global access

Updated Database.hs

1. Initialize TopoOrder: Added initialization in newDatabase
2. Maintain Order: Updated updateReverseDeps to call addEdge when dependencies change
3. Use Order: Modified transitiveDirtyListBottomUpDiff to use getAffectedKeysInOrder for efficient sorting
4. Documentation: Added detailed comments explaining the optimization

Performance Benefits

• Before: O(V + E) DFS traversal + O(V log V) sorting on each invocation
• After: O(V + E) DFS traversal + O(V) lookup and filter using pre-maintained order
• Incremental Updates: Order is maintained incrementally during dependency changes (O(affected nodes))

The Pearce-Kelly algorithm ensures that the topological order is maintained with minimal overhead during dependency updates, making transitiveDirtyListBottomUpDiff faster especially for large dependency graphs.

Testing

• All existing tests pass
• Fixed test warnings by properly handling return values
• The implementation is backward compatible and doesn't change any external APIs

Files Changed

• hls-graph/hls-graph.cabal - Added new module to exposed modules
• hls-graph/src/Development/IDE/Graph/Internal/Types.hs - Added TopoOrder data structure using StmContainers.Map
• hls-graph/src/Development/IDE/Graph/Internal/TopoSort.hs - New module with Pearce-Kelly implementation, removed unused getOrderedList
• hls-graph/src/Development/IDE/Graph/Internal/Database.hs - Integration of topological ordering
• hls-graph/README.md - Documentation of the optimization
• hls-graph/test/DatabaseSpec.hs - Fixed test warnings

Original prompt

transitiveDirtyListBottomUpDiff is slow, can you use Pearce–Kelly algorithm to keep a running topo of dependency graph so we can speed up transitiveDirtyListBottomUpDiff
transitiveDirtyListBottomUpDiff is slow, can you use Pearce–Kelly algorithm to keep a running topo of dependency graph so we can speed up transitiveDirtyListBottomUpDiff

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