Roadmap: Future Planning and Extensions

[j143]

Now, we have the core logic for an eager backend, a lazy engine, and an optimizer, it's the perfect time to structure this into a proper framework. This involves organizing the code into logical files and planning for future growth.

With this structure, you can now plan for new features. Here is a roadmap:

A. More Operations

• Linear Algebra:

  • Matrix Multiplication (@): Fully implement the MultiplyOp to call multiply_eager.
  • Transpose (.T): Add a TransposeOp. An interesting optimization is that a transpose can often be "free" by just changing how tiles are read, without writing a new matrix.
  • Element-wise Functions: Add exp, log, sqrt, etc. These are simple to fuse.

• Reductions:

  • sum(), mean(): These are fundamental. They are also interesting because they change the output shape from a matrix to a vector or scalar.
  • max(), min(): Implement these along different axes.

B. A Smarter Optimizer ✨

• Rule-Based Optimizer: Instead of hard-coding the if isinstance(...) check inside one operation, create a list of optimization rules. Your optimizer would have a registry like:

  • Rule 1: If the plan matches Multiply(Add(A, B), Scalar), execute with fused_add_multiply_kernel.
  • Rule 2: If the plan matches Transpose(Transpose(A)), rewrite the plan to just A.

• Cost-Based Optimizer: For truly advanced systems, you would implement a cost model. For an operation like A @ B @ C, the optimizer would calculate the estimated I/O cost of both (A @ B) @ C and A @ (B @ C) and choose the cheaper execution path based on the matrix shapes.

• Recognize and fuse the (A + B) * 2 pattern #7

C. An Enhanced Execution Engine

• Parallelism: Use Python's concurrent.futures.ThreadPoolExecutor to process tiles in parallel and saturate a fast NVMe drive, or ProcessPoolExecutor to use multiple CPU cores for computation-heavy tiles.

• Memory Management: Implement a simple LRU (Least Recently Used) cache to keep a few frequently accessed data tiles in memory, avoiding re-reading them from disk. #13

• Add option to visualize the cache access #19

This roadmap transforms the project from a simple script into a conceptual blueprint for a real computational framework. The process of thinking about this architecture is just as valuable as your work on SystemDS, as it deals with the same fundamental design patterns of separating the logical plan from the physical execution.

[j143]

Selected improvements

1. NumPy-compatible API Layer (Score: 8.20/10)
   Rationale: This is the foundation for user adoption. Without NumPy compatibility, users won't migrate from existing solutions. It leverages your deep understanding of matrix operations from SystemDS while providing the familiar interface developers expect.​

2. Compressed Block Storage (Score: 7.40/10)
   Rationale: This directly complements your buffer manager work and provides a unique differentiator. On-the-fly compression with zstd/lz4 can reduce I/O by 2-4x with minimal CPU overhead, as shown in GPU out-of-core research.​

3. Hierarchical Memory Management (Score: 7.20/10)
   Rationale: Multi-tier caching (RAM → SSD → Network) is critical for scaling beyond single-machine limits. This builds on your buffer pool architecture and aligns with modern cloud-native deployments.​

4. GPU Acceleration Support (Score: 7.20/10)
   Rationale: CuPy integration can provide 10-100x speedups for large matrices, as shown in benchmarks. This positions your framework for deep learning workloads where GPU acceleration is essential.​

5. Smart Prefetching with Access Pattern Learning (Score: 7.20/10)
   Rationale: This directly builds on your Belady/prescient eviction work in SystemDS. Predictive prefetching using learned access patterns is cutting-edge research with strong academic publication potential and practical impact.​