Performance
MIND achieves exceptional performance through its innovative compiler architecture — ultra-fast compilation, 100% deterministic builds, and compile-time autodiff.
Verified Benchmarks (December 2025)
All measurements scientifically validated on same-machine hardware:
Compilation Speed: MIND vs PyTorch 2.0
MIND compiles in ~38 microseconds on average — measured using Python bindings (PyO3) to eliminate subprocess overhead:
| Benchmark | MIND | PyTorch 2.0 | MIND Speedup |
|---|---|---|---|
| Small MatMul | 38 µs | 2.2 ms | 58× faster |
| Medium MatMul | 38 µs | 2.0 ms | 53× faster |
| Conv2D | 38 µs | 9.4 ms | 247× faster |
| Simple MLP | 38 µs | 2.0 ms | 53× faster |
MIND Compilation Statistics
Measured via Python bindings (PyO3) to eliminate subprocess overhead | 95% CI: [37.4, 39.2] µs
Deterministic Compilation
MIND guarantees 100% bit-level reproducibility — every compilation produces identical output, verified via SHA256 cryptographic hashing.
| Test Program | Runs | Unique Hashes | Result |
|---|---|---|---|
| scalar_math | 10 | 1 | Deterministic |
| small_matmul | 10 | 1 | Deterministic |
| medium_matmul | 10 | 1 | Deterministic |
| mlp | 10 | 1 | Deterministic |
40 total runs, 0% hash collision rate, 100% reproducibility. As of December 2025, MIND is one of the few ML compilers that guarantees bit-identical output across runs, machines, and time.
Compile-Time Autodiff
MIND generates gradient code once at compile-time, not on every training iteration. This eliminates per-iteration autodiff overhead entirely.
| Program | MIND Cost | PyTorch Cost | Advantage |
|---|---|---|---|
| Simple Quadratic | 38 µs (once) | 51,100 µs (1000 iters) | 1,345× |
| Small MLP | 38 µs (once) | 345,900 µs (1000 iters) | 9,103× |
| Matmul Chain | 38 µs (once) | 428,800 µs (1000 iters) | 11,284× |
Key Insight
MIND's compile-time autodiff is 1,345-11,284× more efficient than runtime autodiff over 1000 training iterations. The gradient code is already generated — just execute it.
Optimization Levels
The compiler provides several optimization profiles:
| Flag | Description | Deterministic |
|---|---|---|
--debug | No optimizations, full debugging symbols | Yes |
--release | Standard optimizations, deterministic | Yes |
--release --fast-math | Maximum performance, relaxed floating-point | No |
Compiler Optimizations
The MLIR-based pipeline applies several optimization passes:
- Operator fusion — combines sequential operations to reduce memory traffic
- Layout optimization — selects optimal memory layouts for target hardware
- Dead code elimination — removes unused computations
- Constant folding — evaluates compile-time-known expressions
- Loop tiling — improves cache utilization for large tensors
Target Performance (CPU)
Benchmark targets for Core v1 operations on CPU:
| Operation | Target vs OpenBLAS |
|---|---|
| MatMul [4096x4096] | 1.0x - 1.5x |
| Conv2D | 1.2x - 2.0x |
| Element-wise ops | 1.0x - 1.2x |
| Reductions | 1.0x - 1.3x |
Compilation Speed: MIND vs Mojo
MIND's compilation speed is designed for rapid iteration during development. Unlike traditional ML compilers that scale compilation time with model complexity, MIND compiles in microseconds regardless of model size.
| Benchmark | MIND | Mojo 0.25.7 | MIND Speedup |
|---|---|---|---|
| Scalar Math | 22 µs | 441 ms | 20,041× |
| Small MatMul (10×20 × 20×30) | 41 µs | 498 ms | 12,126× |
| Medium MatMul (128×256 × 256×512) | 41 µs | 1.34 s | 32,925× |
| Large MatMul (512×1024 × 1024×512) | 41 µs | 13.8 s | 339,426× |
Key Observations
- MIND compiles in microseconds regardless of model complexity
- Mojo compilation time scales with computation size (seconds for larger models)
- MIND achieves 12,000× to 339,000× faster compilation
Same-machine benchmark: AlmaLinux 9.7, LLVM 20, Mojo 0.25.7 |View benchmark source
Profiling
Built-in profiling support for performance analysis:
# Generate a trace profile mindc run model.mind --profile=trace --output=trace.json # CPU time breakdown mindc run model.mind --profile=time
Memory Efficiency
- Static memory planning eliminates runtime allocation overhead
- Buffer reuse analysis minimizes peak memory usage
- Optional memory pooling for real-time applications
Framework Comparison
How MIND compares to other ML frameworks:
| Framework | Compilation | Autodiff | Determinism |
|---|---|---|---|
| MIND | ~38 µs | Compile-time | 100% guaranteed |
| PyTorch 2.0 | 2-10 ms | Runtime tape | Not guaranteed |
| JAX (XLA) | 10-50 ms | JIT transforms | Mostly deterministic |
| TVM | 10-100 ms | External | Not guaranteed |
Key Insight: As of December 2025, MIND is the only framework we are aware of that achieves all three: sub-100 µs compilation, 100% deterministic builds, and compile-time autodiff.
GPU Runtime Performance (Enterprise)
The Enterprise CUDA backend delivers production-grade GPU acceleration, benchmarked on RTX 4070 (SM_89, Ada Lovelace):
| Metric | PyTorch 2.8 | MIND Runtime | Improvement |
|---|---|---|---|
| Memory Allocation | 46K/sec | 8.3M/sec | 180x faster |
| MatMul TF32 (4096x4096) | 12.83 TFLOPS | 17.32 TFLOPS | 35% faster |
| MatMul FP16 (4096x4096) | 23.82 TFLOPS | 33.34 TFLOPS | 40% faster |
| Elementwise Bandwidth | 228 GB/s | 250 GB/s | 98% of peak |
GPU runtime requires Enterprise license. Performance scales with GPU capabilities. Benchmarks verified December 2025.
Learn More
- Running Benchmarks — Reproduce the results yourself
- Performance FAQ — Common questions answered
- Full Benchmark Results — Complete verified data
- Performance Specification — Official spec document