Metadata-Version: 2.1
Name: nanogemm
Version: 0.3.6
Summary: Minimalist, bare-metal SIMD & Assembly GEMM engine for Python. Sub-microsecond CPU matrix multiplication for AI & scientific computing.
Author-email: eminsk <M_N_Nik@yahoo.com>
License: MIT
Project-URL: Homepage, https://github.com/eminsk/nanogemm
Project-URL: Repository, https://github.com/eminsk/nanogemm
Project-URL: Issues, https://github.com/eminsk/nanogemm/issues
Keywords: gemm,matrix-multiplication,simd,assembly,fasm,avx2,cpu-inference,deep-learning,high-performance-computing,free-threaded,no-gil,pep-703,pypy,pypy3
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Programming Language :: C
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy <2.3.0,>=1.20.0 ; implementation_name != "pypy" and python_version <= "3.13"
Requires-Dist: numpy >=2.3.0 ; implementation_name != "pypy" and python_version >= "3.14"
Requires-Dist: numpy <2,>=1.20.0 ; implementation_name == "pypy" and python_version < "3.10"
Requires-Dist: numpy >=1.20.0 ; implementation_name == "pypy" and python_version >= "3.10"
Requires-Dist: typing-extensions >=4.0 ; python_version < "3.10"
Provides-Extra: dev
Requires-Dist: pytest >=7.0.0 ; extra == 'dev'

# NanoGEMM ⚡

[![PyPI](https://img.shields.io/pypi/v/nanogemm?color=blue)](https://pypi.org/project/nanogemm/)
[![Conda-Forge](https://img.shields.io/conda/vn/conda-forge/nanogemm.svg?style=flat)](https://anaconda.org/conda-forge/nanogemm)
[![MSYS2](https://img.shields.io/badge/MSYS2-pacman-orange.svg?style=flat)](https://packages.msys2.org/package/mingw-w64-x86_64-python-nanogemm)
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[![Arch Linux AUR](https://img.shields.io/badge/Arch_Linux-AUR-1793D1.svg?style=flat)](https://aur.archlinux.org/packages/python-nanogemm)
[![The Daily Diff](https://img.shields.io/badge/The_Daily_Diff-Featured_Story_(9%2F10)-crimson?logo=hackernews)](https://tdd.cat/2026-09-07/)
[![GitHub Trending](https://img.shields.io/badge/GitHub_Trending-Deep_Learning-success?logo=github)](https://github.com/eminsk/nanogemm)
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[![Python](https://img.shields.io/badge/Python-3.8%20--%203.15-blue)](https://pypi.org/project/nanogemm/)
[![PyPy](https://img.shields.io/badge/PyPy-3.8%20--%203.11-orange.svg)](https://www.pypy.org/)
[![No-GIL](https://img.shields.io/badge/No--GIL-3.13t%20--%203.15t-purple.svg)](https://peps.python.org/pep-0703/)
[![SIMD](https://img.shields.io/badge/SIMD-AVX2%20%2B%20FMA%20%7C%20ARM%20NEON-brightgreen)](https://github.com/eminsk/nanogemm)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eminsk/nanogemm/blob/main/notebooks/benchmark.ipynb)
[![Footprint](https://img.shields.io/badge/Binary-~100_KB-orange)](https://github.com/eminsk/nanogemm)
[![Dev.to](https://img.shields.io/badge/Dev.to-Read%20Article-0a0a0a?logo=devdotto)](https://dev.to/eminsk/how-i-beat-numpy-matrix-multiplication-by-28x-with-a-100kb-c-microkernel-82k)

> 🏆 **Featured in [The Daily Diff](https://tdd.cat/2026-09-07/):** Selected as a top AI & HPC story (*Interest: 9/10, Depth: 9/10, Utility: 9/10*). Ranked in **GitHub Trending** under Deep Learning topics.

### 📦 Multi-Platform Installation

| Platform / Manager | Installation Command |
|---|---|
| **PyPI (Standard)** | `pip install nanogemm` |
| **Conda-Forge** | `conda install -c conda-forge nanogemm` |
| **MSYS2 (MinGW-w64)** | `pacman -S mingw-w64-x86_64-python-nanogemm` |
| **Ubuntu / Debian (.deb)** | `sudo dpkg -i python3-nanogemm_0.3.6-1_amd64.deb` |
| **Arch Linux (AUR)** | `yay -S python-nanogemm` |

**NanoGEMM** is a minimalist, bare-metal General Matrix Multiplication (GEMM) engine designed for sub-microsecond CPU inference and high-performance computing in Python.

Built with direct **AVX2 / FMA (256-bit SIMD)** and **ARM NEON (128-bit SIMD)** assembly-level register tiling and cache blocking, NanoGEMM eliminates the heavy function-call dispatch, thread-pool barriers, and memory-packing overhead of heavyweight BLAS libraries (OpenBLAS, MKL) for small-to-medium tensors.

---

## 🧩 Universal Compatibility Matrix

| Runtime / Implementation | Supported Versions | Execution Mode | Status |
|:---|:---|:---|:---:|
| **CPython (Standard)** | 3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14, 3.15 | Native C AVX2/FMA + Buffer Protocol | ✅ Fully Supported |
| **CPython (Free-Threaded)** | 3.13t, 3.14t, 3.15t | Multi-core No-GIL (`Py_BEGIN_ALLOW_THREADS`) | ✅ Fully Supported |
| **PyPy (JIT Accelerated)** | 3.8, 3.9, 3.10, 3.11 | High-speed JIT tracing / Pure ctypes FASM fallback | ✅ Fully Supported |
| **Operating Systems** | Windows (7, 8, 10, 11), Linux, macOS (Intel & Apple Silicon) | x86_64, ARM64 | ✅ Fully Supported |

---

## 🚀 Performance Benchmarks

Measured on **Intel/AMD x86-64 CPU (AVX2 + FMA)** against **NumPy 2.2.3** (single-precision `float32`):

| Matrix Dimension | NumPy Latency | NanoGEMM Latency | Speedup Factor | NanoGEMM Throughput |
| :--- | :---: | :---: | :---: | :---: |
| **`16 x 16`** | `4.05 µs` | **`1.71 µs`** (C: `0.68 µs`) | 🚀 **2.36x – 5.50x FASTER** | `4.79 – 12.14 GFLOPS` |
| **`24 x 24`** | `19.39 µs` | **`2.67 µs`** (C: `1.27 µs`) | 🚀 **7.26x FASTER** | `10.36 – 21.73 GFLOPS` |
| **`32 x 32`** | `7.14 µs` | **`4.21 µs`** (C: `2.18 µs`) | 🚀 **1.70x – 2.95x FASTER** | `15.58 – 30.06 GFLOPS` |
| **`48 x 48`** | `12.65 µs` | **`11.15 µs`** (C: `6.27 µs`) | 🚀 **1.13x – 1.73x FASTER** | `19.83 – 35.29 GFLOPS` |
| **`64 x 64`** | `19.69 µs` | **`13.89 µs`** | 🚀 **1.42x FASTER** | `37.74 GFLOPS` |
| **`128 x 128`** | `89.44 µs` | `115.90 µs` | `0.77x` | `36.19 GFLOPS` |

### ⚡ Quantized INT8 SIMD GEMM Performance (`matmul_int8`)
Measured live on **Google Colab CPU (AVX2)** against **NumPy** (`int8 x int8 -> int32`):

| Matrix Dimension | NumPy Latency | NanoGEMM INT8 | Speedup Factor | NanoGEMM Throughput |
| :--- | :---: | :---: | :---: | :---: |
| **`16 x 16`** | `6.71 µs` | **`4.57 µs`** | 🚀 **1.47x FASTER** | `1.79 GOP/s` |
| **`32 x 32`** | `40.02 µs` | **`4.99 µs`** | 🚀 **8.03x FASTER** | `13.14 GOP/s` |
| **`64 x 64`** | `303.97 µs` | **`28.88 µs`** | 🚀 **10.53x FASTER** | `18.16 GOP/s` |
| **`128 x 128`** | `2524.86 µs` | **`221.79 µs`** | 🚀 **11.38x FASTER** | `18.91 GOP/s` |

### 🧠 Batched Matrix Multiplication (BMM) for Transformer Attention (`bmm`)
Measured live on **Google Colab CPU** (Multi-Head Attention $Q @ K^T$ shapes):

| Attention Shape (Heads, Seq, Dim) | NumPy Latency | NanoGEMM BMM | Speedup Factor |
| :--- | :---: | :---: | :---: |
| **`Heads=8, Seq=32, Dim=32`** | `25.48 µs` | **`14.96 µs`** | 🚀 **1.70x FASTER** |
| **`Heads=16, Seq=32, Dim=64`** | `74.84 µs` | **`50.56 µs`** | 🚀 **1.48x FASTER** |
| **`Heads=32, Seq=64, Dim=64`** | `450.56 µs` | **`388.69 µs`** | 🚀 **1.16x FASTER** |
| **`Heads=64, Seq=32, Dim=32`** | `148.62 µs` | **`106.96 µs`** | 🚀 **1.39x FASTER** |

> 💡 **Why is NanoGEMM faster on small/medium matrices?**  
> Traditional BLAS engines incur 3–10 µs of fixed overhead per invocation due to dynamic runtime dispatch, argument sanitization, thread synchronization, and packing buffers. NanoGEMM utilizes a zero-allocation, direct register-tiled microkernel that executes in **sub-microsecond time** immediately upon invocation.

### ⚙️ Benchmark Environment & Test Configuration

| Parameter | Specification |
| :--- | :--- |
| **CPU Architecture** | x86-64 with AVX2 (256-bit SIMD) + FMA3 support |
| **Execution Model** | **Single-Core / Single-Threaded (1 Thread)** for NanoGEMM (zero thread-pool overhead) |
| **NumPy Baseline** | NumPy 2.2+ linked against OpenBLAS (standard runtime) |
| **C Compiler** | GCC (`-O3 -mavx2 -mfma`) / MSVC (`/O2 /arch:AVX2`) |
| **Methodology** | Median latency across 10,000 iterations per size with cache pre-warming |
| **Data Type** | IEEE-754 Single-Precision (`float32`), contiguous C-order layout |

> 📌 **Single-Thread Design Note:** NanoGEMM runs strictly on a single CPU core without thread pools, pthreads, or mutex barriers. For small matrices ($16 \times 16$ to $64 \times 64$), thread synchronization in OpenMP costs more cycles than the matrix multiply itself. NumPy/OpenBLAS incurs dispatch latency, whereas NanoGEMM enters CPU registers directly.

---

## ⚖️ Architectural Comparison: NanoGEMM vs OpenBLAS vs LIBXSMM

When evaluating CPU matrix multiplication engines, understanding the design trade-offs between **compilation models, binary footprint, dispatch latency, and target matrix shapes** is critical:

| Feature / Metric | NanoGEMM | LIBXSMM | OpenBLAS / MKL |
| :--- | :---: | :---: | :---: |
| **Core Paradigm** | Static AOT Register Tiling ($6\times 16$, $4\times 16$, $2\times 16$) | Runtime JIT Machine-Code Generation | Multi-threaded Dynamic BLAS with Buffer Packing |
| **Binary Footprint** | ⚡ **~100 KB** (ultra-lightweight single binary) | ~15–30 MB | ~30–50 MB |
| **Code Generation** | **Ahead-of-Time (Zero-JIT)** | Runtime JIT code emission into executable pages | Ahead-of-Time |
| **Security / Hardened OS (W^X)** | 🛡️ **100% Compliant** (No executable heap/stack required) | Requires writable & executable memory (`PROT_EXEC`) | 🛡️ 100% Compliant |
| **External Dependencies** | **None** (pure C & FASM assembly, zero runtime deps) | C++ runtime, pthreads | OpenMP / pthreads, Fortran runtime |
| **Python Invocation Latency** | **Sub-microsecond (< 1.5 µs)** via Buffer Protocol & GIL release | Requires custom wrappers or ctypes bindings | 3–5 µs (NumPy ufunc dispatch, C-API type checking) |
| **Optimal Sweet Spot** | **Small tensors ($16 \times 16$ – $64 \times 64$) in Python / Edge AI** | Small-to-medium matrices in pure C/C++ | Large matrices ($512 \times 512$ – $4096 \times 4096+$) |
| **Multi-Threading Model** | Single-threaded per call (use `ThreadPoolExecutor` without lock contention) | Single/Multi-threaded | Multi-threaded OpenMP thread pool |

### 💡 Key Design Rationale:
1. **NanoGEMM vs OpenBLAS / MKL on Small Tensors:**  
   Traditional BLAS libraries are optimized for massive matrices ($1000 \times 1000+$). On small matrices ($16 \times 16$ to $64 \times 64$), the fixed overhead of thread synchronization, buffer packing, and NumPy ufunc argument sanitization takes 3–5 µs before arithmetic even begins. NanoGEMM eliminates this glue overhead completely, achieving sub-microsecond latency in Python.
2. **NanoGEMM vs LIBXSMM:**  
   LIBXSMM (Intel Labs) is the gold standard for small-matrix GEMM in pure C/C++ when runtime JIT code emission is permissible. NanoGEMM adopts a different engineering philosophy: **Zero-JIT, zero dynamic allocations, zero external dependencies, and a ~100 KB footprint**. NanoGEMM deploys seamlessly in hardened environments (where W^X / DEP security policies block runtime JIT emission) and installs instantly via standard Python wheels.
3. **v0.3.4 Boundary Microkernel Acceleration & Colab Validation:**  
   In NanoGEMM v0.3.4, dedicated $4 \times 16$ and $2 \times 16$ boundary register tiles (plus $8$-wide column tiles) are introduced. Standard square power-of-two matrices ($8 \times 8$, $16 \times 16$, $32 \times 32$, $64 \times 64$) now compute **100% inside AVX2+FMA vector registers with zero scalar tail fallback**, verified live in Google Colab.

---

## 🛠 Architectural Design

### 1. Register Tiling ($6 \times 16$ Microkernel)
* **Register allocation:** Utilizes 12 `ymm` registers (`ymm0` – `ymm11`) as 256-bit floating-point accumulators storing a $6 \times 16$ tile of matrix $C$.
* **Vector broadcast & FMA:** Two `ymm` registers load vectors from $B$, while individual scalar elements of $A$ are broadcast across `ymm` using `_mm256_set1_ps` and accumulated via fused multiply-add (`_mm256_fmadd_ps`).
* **Zero Spilling:** Fits completely inside the 16 available x86-64 YMM registers without stack eviction.

### 2. Multi-Level Cache Blocking
* **$L_1$ / $L_2$ Cache Tiling:** Matrices are processed in cache blocks ($M_c = 64, N_c = 128, K_c = 128$) to maintain maximum L1/L2 data cache hit ratios and eliminate memory bus thrashing.
* **Vectorized Edge Handling:** Arbitrary matrix dimensions (non-multiples of 6 or 16) are processed using boundary SIMD edge loops without padding or buffer allocations.

```
       Matrix A (M x K)              Matrix B (K x N)
     [ . . . . . . . . ]           [ . . . ymm0 . . . ]
     [ . . . . . . . . ]           [ . . . ymm1 . . . ]
     [ a0 a1 a2 a3 . . ]     x     [ . . . . .  . . . ]
     [ . . . . . . . . ]           [ . . . . .  . . . ]
     [ . . . . . . . . ]
             │                             │
             └──────────────┬──────────────┘
                            ▼
                Matrix C (6 x 16 Tile)
             [ ymm0  ymm1  ] -> Row 0
             [ ymm2  ymm3  ] -> Row 1
             [ ymm4  ymm5  ] -> Row 2
             [ ymm6  ymm7  ] -> Row 3
             [ ymm8  ymm9  ] -> Row 4
             [ ymm10 ymm11 ] -> Row 5
```

### 3. ARM NEON ($4 \times 8$ Microkernel)
* **Target Architecture:** Apple Silicon (M1/M2/M3/M4) and Linux AArch64 (AWS Graviton, Ampere Altra, Neoverse).
* **Register Tiling:** Operates on 8 128-bit `float32x4_t` accumulator registers (`c00`–`c31`) computing a $4 \times 8$ tile of matrix $C$.
* **Vector FMA:** Inner loop uses `vld1q_f32` vector loads and `vmlaq_n_f32` fused multiply-accumulates. Standard power-of-two matrix sizes ($16\times 16$, $32\times 32$, $64\times 64$) tile evenly with zero scalar tail fallback.

---

## 📦 Installation & Quickstart

### Installation via PyPI (Recommended)
```bash
pip install nanogemm
# or with uv
uv add nanogemm
```

> **Python Compatibility:** Fully tested and verified across **Python 3.9 through 3.15 (including 3.15.0rc2)**.
>
> ⚡ **No-GIL & Free-Threaded Ready (PEP 703):**
> * **Free-Threaded CPython:** Explicitly declares `Py_MOD_GIL_NOT_USED` to run safely without re-enabling the GIL under `python3.13t`, `3.14t`, and `3.15t`.
> * **GIL-Releasing Operations:** Automatically releases the GIL (`Py_BEGIN_ALLOW_THREADS`) during hardware SIMD execution, allowing Python threads (`threading.Thread`, `ThreadPoolExecutor`) to run matrix multiplications concurrently across multiple CPU cores without lock contention.

### Build from Source
```bash
git clone https://github.com/eminsk/nanogemm.git
cd nanogemm
pip install -e .
```

### Python Usage
```python
import nanogemm as ng
import numpy as np

# Verify SIMD hardware acceleration
print("Active ISA:", ng.get_simd_isa())
# Output: Active ISA: AVX2+FMA (256-bit SIMD, 6x16 register tiling)

# Allocate input matrices
A = np.random.randn(32, 64).astype(np.float32)
B = np.random.randn(64, 128).astype(np.float32)

# Direct hardware-accelerated MatMul: C = A @ B (supports 2D, 3D, and 4D tensors)
C = ng.matmul(A, B)

# Pre-allocated zero-copy output buffer for maximum throughput:
out = np.empty((32, 128), dtype=np.float32)
ng.matmul(A, B, out=out)

# Batched Multi-Head Attention GEMM for Transformers (B x H x S x D):
Q = np.random.randn(8, 16, 32, 64).astype(np.float32)
K_t = np.random.randn(8, 16, 64, 32).astype(np.float32)
scores = ng.bmm(Q, K_t)  # Output: (8, 16, 32, 32) with zero Python loop overhead

# Quantized INT8 GEMM (int8 x int8 -> int32, up to 5.0x faster than NumPy):
A_i8 = np.random.randint(-128, 128, size=(32, 64), dtype=np.int8)
B_i8 = np.random.randint(-128, 128, size=(64, 128), dtype=np.int8)
C_i32 = ng.matmul_int8(A_i8, B_i8)

# End-to-end Quantized Linear Layer forward pass:
Y_float = ng.quantized_matmul(A_i8, B_i8, scale_a=0.05, scale_b=0.02)

# Standard BLAS SGEMM interface: C = alpha * (A @ B) + beta * C
res = ng.sgemm(A, B, alpha=2.0, beta=0.5, c=out)
```

---

## 🚀 Interactive Google Colab Demo

Run NanoGEMM benchmarks interactively in your browser with zero local setup:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eminsk/nanogemm/blob/main/notebooks/benchmark.ipynb)

The [Interactive Benchmark Notebook](https://colab.research.google.com/github/eminsk/nanogemm/blob/main/notebooks/benchmark.ipynb) demonstrates:
- **Zero-Setup Installation & Hardware ISA Detection:** Compiles native C/AVX2 on Google Colab CPU in seconds.
- **100% Numerical Accuracy Verification:** Tests square, non-square, prime dimensions, and BLAS SGEMM against OpenBLAS.
- **Microsecond Latency & GFLOPS Benchmark:** Measures speedup vs NumPy across $16\times 16 \dots 128\times 128$ matrices (up to **7.26x faster** on $24\times 24$).
- **Interactive Performance Plotting:** Side-by-side latency & speedup bar/line charts using Matplotlib with Edge AI sweet spot highlights.
- **Real-Time Edge AI Loop:** Demonstrates 100,000 iterations of zero-allocation in-place multiplication (`out=C`) for $24\times 24$ and $32\times 32$.
- **Batched Matrix Multiplication (BMM):** Multi-Head Attention benchmarks ($Q @ K^T$) across 8, 16, 32, and 64 heads.
- **Quantized INT8 SIMD GEMM:** Measures speedup vs NumPy integer multiplication across $16\times 16 \dots 128\times 128$ (up to **5.0x faster**).

---

## 🧪 Testing & Verification

Run the comprehensive correctness test suite comparing NanoGEMM with NumPy reference outputs across random uniforms, normals, non-square dimensions, and prime shapes:

```bash
python tests/test_correctness.py
```

Run the official benchmark against your installed NumPy BLAS:

```bash
python benchmarks/bench_vs_numpy.py
```

---

## ⚡ Standalone Flat Assembler (FASM) 32-bit & 64-bit Engines

NanoGEMM includes native standalone assembly implementations written in **Flat Assembler (FASM)** for both 64-bit and 32-bit architectures in the [`asm/`](file:///C:/proekts/nanogemm/asm) directory:

* **x86-64 Engine (`asm/nanogemm64.dll`, `asm/test_nanogemm64.exe`)**:
  * Microkernel with $4 \times 16$ and $4 \times 8$ register-tiled AVX2+FMA instructions (`vfmadd231ps`, `vbroadcastss`, `vmovups`).
  * Complies strictly with the Microsoft x64 ABI (preserving non-volatile registers `RBX`, `RSI`, `RDI`, `R12`–`R15`, `XMM6`–`XMM15`).
  * Peak throughput exceeding **38–40 GFLOPS** on a single CPU core.
* **x86 32-bit Engine (`asm/nanogemm32.dll`, `asm/test_nanogemm32.exe`)**:
  * Vectorized $4 \times 4$ SSE2 microkernel (`movups`, `shufps`, `mulps`, `addps`) using `cdecl` calling convention.
  * Compatible with all 32-bit x86 environments and 64-bit Windows via WoW64 with zero external dependencies.
  * Delivers **13–15 GFLOPS** in pure 32-bit mode.

### Building & Running FASM Tests
```cmd
:: Build all 4 binaries and run native executable suites
cd asm
build.bat

:: Run Python verification and NumPy comparison suite
python tests/test_fasm.py
```

---

## 🌐 High-Performance Systems Ecosystem

NanoGEMM is developed by [**@eminsk**](https://github.com/eminsk) as part of an engineering ecosystem focused on low-level hardware performance, assembly programming, and native desktop computing:

* ⚡ [**NanoVector**](https://github.com/eminsk/nanovector) — Minimalist, bare-metal vector search & episodic memory engine for AI agents in ~120KB with AVX2+FMA, ARM NEON, and FASM x64 (`pip install nanovector`).
* 🎥 [**screenvideo**](https://github.com/eminsk/screenvideo) — Lightweight desktop screen recorder featuring WASAPI loopback audio and a standalone pure x64 Flat Assembler (FASM) native edition.
* 📊 [**xlsx_vievers**](https://github.com/eminsk/xlsx_vievers) — Desktop spreadsheet processor with 80+ formula functions, Chart Wizard, and hardware-accelerated SIMD SSE2 math engine.
* 📈 [**yfinance-ta-patterns**](https://github.com/eminsk/yfinance-ta-patterns) — Candlestick pattern scanner and AI ranking suite powered by TA-Lib and quantitative backtesting.
* 🔍 [**StackOverflowAPI**](https://github.com/eminsk/StackOverflowAPI) — Desktop client for Stack Overflow built with CustomTkinter and native FASM x64 search client.

---

## 📄 License

MIT License — Copyright (c) 2026 [eminsk](https://github.com/eminsk).
