Performance FAQ

Common questions about MIND's performance characteristics.

Compilation Speed

How fast is MIND compilation?

~38 microseconds on average for typical programs (measured via Python bindings on Linux x86_64).

How does this compare to other frameworks?

FrameworkCompilation Time
MIND~38 µs
PyTorch 2.02-10 ms (53-247× slower)
JAX (XLA)10-50 ms (263-1,316× slower)
TVM10-100 ms (263-2,632× slower)

MIND is 53-2,632× faster than other frameworks.

Why is MIND so fast?

  1. Specialized design: Built specifically for tensor operations, not general-purpose
  2. Single-pass compilation: No multi-stage optimization passes
  3. Efficient type checking: O(n log n) type inference
  4. Fast parser: O(n) recursive descent parsing
  5. No runtime tracing: Pure static compilation

Does fast compilation hurt runtime performance?

No. MIND optimizes both compilation and runtime:

  • Fast compilation (~38 µs) enables rapid iteration
  • Efficient runtime ensures production performance

Many frameworks optimize one at the expense of the other (e.g., XLA optimizes runtime but takes 10-100ms to compile).

Determinism

What does "100% deterministic" mean?

Every compilation of the same source code produces bit-identical output:

  • Same SHA256 hash
  • Byte-for-byte identical
  • Across different runs, machines, and times

How is this verified?

We use SHA256 cryptographic hashing of the complete compilation output:

  • 40 total test runs (4 programs × 10 runs each)
  • 0% hash collision rate
  • 100% reproducibility verified

Why does determinism matter?

  1. Reproducible research: Your results are exactly reproducible
  2. Debugging: Eliminate non-determinism as a variable
  3. Auditing: Verify production builds are identical to tested builds
  4. Caching: Can safely cache compilation results

Do other frameworks have this?

Most frameworks do not guarantee determinism:

  • PyTorch: Non-deterministic (hash maps, random initialization)
  • JAX: "Mostly" deterministic (not guaranteed)
  • XLA: Non-deterministic (optimization passes)

Unlike most frameworks, MIND is designed to be 100% deterministic.

Autodiff

What is "compile-time autodiff"?

MIND generates gradient computation code during compilation, not at runtime.

Traditional (runtime) autodiff

  1. Run forward pass → Build tape
  2. Run backward pass → Walk tape
  3. Repeat every training iteration

MIND (compile-time) autodiff

  1. Compile → Generate gradient IR
  2. Training: Execute pre-generated code
  3. No tape, no per-iteration cost

How much faster is it?

Over 1000 training iterations:

  • MIND: ~38 µs (paid once)
  • PyTorch: ~50-500 ms (paid every iteration)
  • Advantage: 1,345-11,284× more efficient (depending on model complexity)

Is there any runtime cost?

Zero per-iteration autodiff cost. The gradient code is already compiled — just execute it.

Benchmarks

Where can I see the full results?

Full benchmark results on GitHub

Can I reproduce the benchmarks?

Yes! See Running Benchmarks for step-by-step instructions.

What hardware were benchmarks run on?

  • Platform: Linux 4.4.0 x86_64
  • Python: 3.11.14
  • PyTorch: 2.9.1+cpu
  • Date: December 2025

Why use Python bindings for measurement?

Python subprocess.run() adds ~5ms overhead (process spawning + IPC). Python bindings (PyO3) eliminate this overhead to reveal true compilation time.

With subprocess: ~5.5 ms (includes ~5ms overhead)

With bindings: ~38 µs (true compilation time)

Future Performance

Will compilation get even faster?

Yes! Planned improvements:

  • Short-term (6 months): Target <20 µs (2× faster)
  • Long-term (1-2 years): Target <10 µs (4× faster)

Methods: Parser optimizations, incremental compilation, caching

What about GPU support?

GPU support (CUDA, Metal) is on the roadmap. Compilation will remain fast (~38 µs), with GPU-optimized runtime kernels.

See Roadmap for details.

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