fht-cpu 1.0.1

Fast Hadamard Transform with SIMD acceleration

FHT - Fast Hadamard Transform

High-performance Fast Hadamard Transform library with SIMD-optimized implementations for x86 (SSE/AVX) and ARM (NEON), and Python bindings via nanobind.

Install

pip install fht_cpu

From source:

git clone https://github.com/grigori-hpnalgs-lab/fht_cpu.git
cd fht
pip install .

Python Usage

import numpy as np
from fht import fht

# 1D transform (in-place)
x = np.random.randn(1024).astype(np.float32)
fht(x)

# Allocating mode (returns new array, original unchanged)
y = fht(x, inplace=False)

# Preallocated output
out = np.empty_like(x)
fht(x, out=out)

# 2D — each row transformed in parallel via OpenMP
X = np.random.randn(1000, 2**16).astype(np.float32)
fht(X, axis=-1)

# Complex arrays (decomposes into real/imag, transforms separately)
z = np.random.randn(512).astype(np.complex128)
fht(z)

Supported dtypes: float32, float64, complex64, complex128.

The transform axis must have a power-of-2 length. For 2D arrays, rows (or columns) are processed in parallel with OpenMP — set thread count via OMP_NUM_THREADS=N.

C/C++ Usage

Header-only. Just include and compile:

#include <fht/fht.h>

float buf[1024];
fht_float(buf, 10);  // log2(1024) = 10

C API

int fht_float(float *buf, int log_n);
int fht_double(double *buf, int log_n);
int fht_float_oop(float *in, float *out, int log_n);
int fht_double_oop(double *in, double *out, int log_n);

Returns 0 on success, 1 on invalid log_n (valid range: 0-30).

CMake Integration

# Via CPM (recommended)
CPMAddPackage("gh:grigori-hpnalgs-lab/fht@1.0.0")
target_link_libraries(myapp PRIVATE fht::fht)

# Or as subdirectory
add_subdirectory(fht)
target_link_libraries(myapp PRIVATE fht::fht)

Compile with -mavx on x86 for best performance.

Platform Support

Platform	Float	Double
x86_64 + AVX	yes	yes
x86_64 + SSE	yes	yes
ARM64 (NEON)	yes	yes

Re-optimizing the code for your CPU

cmake -B build -DFHT_OPTIMIZE_FOR_HOST=ON
cmake --build build

Limitations

Known issues:

• inplace=False / out= does copy + in-place — we will add an out of place version soon, it just requires some changes to the codegen files.
• Complex number support is not optimal — we will provide separate kernels for complex, currently we deinterleave into real and imaginary parts, apply the transform and then recombine.

Acknowledgments

The x86 AVX/SSE implementation is based on FFHT from the FALCONN project by Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, and Ludwig Schmidt. The original code was copied and integrated with minor modifications.

The ARM NEON implementation was written from scratch with auto-tuned code generation.

License

See LICENSE.