cimes-isi / fft-ct

FFT Corner Turn Benchmarks

FFT Corner Turn Benchmarks

This project contains benchmarks related to FFT Corner Turns.

Prerequisites

For full functionality, this project requires the following.

A C compiler that supports AVX-512 instructions, e.g., one of:

• GCC >= 5.0.0 - tested with version 9.1.0.
• Clang >= 3.9.1 - tested with version 10.0.1.
• Intel C Compiler >= 15.0.1 - tested with version 19.0.

Build tools:

• CMake >= 2.8.12
• pkg-config

Libraries:

• POSIX Threads - pthreads-compatible library (usually included with compiler).
• FFTW3 - tested with version 3.3.8. Both single and double precision libraries are used (libfftw3f, libfftw3).
• Intel MKL - tested with version 2019 Update 4.

Building

The project uses CMake:

mkdir build
cd build
cmake ..
make

If dependencies are installed to a non-standard location ${PREFIX}, you must first configure PKG_CONFIG_PATH. E.g., if FFTW3 is configured with ./configure --prefix=${PREFIX}:

export PKG_CONFIG_PATH=${PREFIX}/lib/pkgconfig/

To use a different C compiler than your environment's cc, configure CMake's CMAKE_C_COMPILER variable, e.g.:

cmake .. -DCMAKE_C_COMPILER=/path/to/cc

Benchmarks

There are a handful benchmark program templates:

• transp: Populate a matrix and perform a transpose.
• fft-2d: Populate a matrix and perform a 2-D FFT. Whether a transpose is actually performed depends on the FFT implementation.
• fft-ct: Populate a matrix and perform 1-D FFTs -> transpose -> 1-D FFTs. In this benchmark, a transpose is always performed.

Data Types

C99 types:

• float (flt)
• double (dbl)
• float complex (fcmplx)
• double complex (dcmplx)

FFTW types:

• fftwf_complex (fftwf) - redefined as float complex
• fftw_complex (fftw) - redefined as double complex

MKL types:

• MKL_Complex8 (cmplx8) - redefined as float complex
• MKL_Complex16 (cmplx16) - redefined as double complex

Usage

Benchmark templates are used to generate benchmarks supporting a variety of data types and transpose implementations using different algorithms and library APIs. Benchmark names are generally in the form: ${prog}-${datatype}-${algo}[-${lib}].

All benchmarks support the -h parameter to print a usage/help message.

Benchmarks require specifying, at a minimum, the matrix row and column count. E.g., to perform a naive transpose of a 2048x4096 matrix with double data:

./transp-dbl-naive -r 2048 -c 4096

Some implementations have constraints on parameters:

• Blocked transposes must use block dimensions that are divisors of their corresponding matrix dimensions. I.e., partial blocks are not supported.
• Transposes using AVX-512 instructions require matrix sizes to be multiples of 8x8 blocks. This constraint extends to threaded AVX-512 implementations -- each thread's partition of a matrix must be a multiple of 8x8, e.g., while a single thread (or even three threads) may transpose a 24x24 matrix, two threads cannot because data is partitioned evenly between threads (12x24 or 24x12, for two threads).