Optimizing PyDataStructs - LLVM Backend for Adjacency List Graphs
This week’s work revolved around extending the LLVM backend in PyDataStructs to support adjacency list graphs. The focus was on bridging our existing C++ graph backend with llvmlite, allowing graph operations to be compiled into efficient machine code while preserving the same Python-facing API.
1. LLVM Backend for Graphs
The first milestone was designing an LLVM wrapper for adjacency list graphs. The core goals were:
a. Defining function signatures (add vertex, add edge, check adjacency) at the LLVM IR level
b. Exposing these functions as callable symbols from Python
c. Ensuring the graph memory layout is preserved between Python, C++, and LLVM layers
d. By integrating LLVM IR generation with our graph APIs, we can now begin compiling graph operations into optimized, platform-specific machine code. On macOS, the prototype backend compiles and runs as expected (with a minor error still to be fixed on Ubuntu).
2. Why an LLVM Graph Backend?
The motivation for this work is to push graph algorithms in PyDataStructs beyond what Python or even a C++ backend alone can achieve. An LLVM-based backend opens the door to:
a. Instruction-level parallelism for traversals and adjacency checks
b. Target-specific optimizations (AVX, AVX2, AVX-512) for graph algorithms
c. JIT compilation of graph routines tuned to the structure of the input data
d. Cross-platform performance portability without rewriting code in assembly
e. Even basic operations like adjacency lookups benefit from LLVM’s ability to optimize hash map lookups, inline small routines, and aggressively optimize branches.
What’s Next?
With the backend structure in place, the next steps are:
a. Fixing the Ubuntu build error
b. Implementing BFS and DFS directly in the LLVM backend
c. Exploring optimization passes for graph traversal and edge relaxation
d. Benchmarking LLVM graph operations against the Python and C++ backends
This week’s progress sets the stage for a powerful new dimension in PyDataStructs - LLVM accelerated graph algorithms that combine flexibility with near hand-tuned performance.