Optimizing PyDataStructs - LLVM Backend for Bubble Sort
This week’s progress on PyDataStructs focused on a major milestone: implementing an LLVM backend for Bubble Sort using llvmlite, alongside fixing critical stability issues discovered in previous backends.
1. LLVM Backend Integration with llvmlite
Building on our earlier C++ backends, I added LLVM as a new compilation target, starting with Bubble Sort. The LLVM approach brings several benefits:
JIT Compilation for runtime-optimized code generation
Cross-platform support via LLVM’s intermediate representation
Advanced optimizations like vectorization and loop unrolling
Using llvmlite, we can now generate optimized machine code directly from Python while staying tightly integrated with PyDataStructs.
2. Why Bubble Sort?
Although Bubble Sort has O(n²) complexity, it serves important purposes for this integration:
Establishes a baseline for LLVM performance gains
Validates the LLVM pipeline on a simple, well understood algorithm
Helps analyze memory access patterns for optimization effectiveness
LLVM-generated assembly code can take advantage of modern CPU features like SIMD and branch prediction, already showing clear benefits over interpreted Python.
3. Bug Fixes and Stability Improvements
In parallel, I fixed critical issues affecting backend reliability:
Corrected memory management and reference counting in the BFS backend.
Improved memory management in the C++ backend for OneDimensionalArray.
These fixes strengthen both the new LLVM backend and existing C++ implementations.
What’s Next?
The LLVM Bubble Sort implementation lays the groundwork for broader enhancements. Upcoming steps include:
a. Extending LLVM support to Quick Sort and Merge Sort
b. Exploring SIMD vectorization for numerical workloads
c. Developing adaptive compilation between Python, C++, and LLVM backends
d. Using LLVM passes for memory layout and cache optimization
The PR for the LLVM Bubble Sort implementation is available here. Benchmarking results will follow as testing continues.