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fast_matrix_market

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Fast and full-featured Matrix Market I/O library for C++, Python, and R

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Fast and full-featured Matrix Market I/O.

**C++**: 
Ready-to-use bindings for
[GraphBLAS](README.GraphBLAS.md),
[Eigen](README.Eigen.md),
[CXSparse](README.CXSparse.md),
[Blaze](README.Blaze.md),
[Armadillo](README.Armadillo.md),
and `std::vector`-like types.
Easy to integrate with any datastructure.  
**Python**:
[Python bindings](python). Included as `scipy.io.mmread` and `mmwrite` in SciPy 1.12.  
**R**:
Third party bindings for R in [fastMatMR](https://github.com/HaoZeke/fastMatMR).

[Matrix Market](https://math.nist.gov/MatrixMarket/formats.html) is a simple, human-readable, and widely used sparse matrix file format that looks like this:
```
%%MatrixMarket matrix coordinate real general
% 3-by-3 identity matrix
3 3 3
1 1 1
2 2 1
3 3 1
```

# Why use fast_matrix_market
Most sparse matrix libraries include Matrix Market read and write routines.
However, these are typically written using `scanf`, IOStreams, or equivalent in their language. These routines work,
but are too slow to saturate even a low-end mechanical hard drive, nevermind today's fast storage devices.
This means working with large multi-gigabyte datasets is far more painful than it needs to be.

Additionally, many loaders only support a subset of the Matrix Market format. Typically only coordinate matrices and often just a subset of that.
This means loading data from external sources like the [SuiteSparse Matrix Collection](https://sparse.tamu.edu/) becomes
hit or miss as some valid matrices cannot be loaded.

Use this library to fix these shortcomings.

# Fast

`fast_matrix_market` takes the fastest available sequential methods and parallelizes them.

Reach **>1GB/s** read and write speeds on a laptop (about 25x improvement over IOStreams).

![read](https://raw.githubusercontent.com/alugowski/fast_matrix_market/main/benchmark_plots/parallel-scaling-cpp-read.svg)
![write](https://raw.githubusercontent.com/alugowski/fast_matrix_market/main/benchmark_plots/parallel-scaling-cpp-write.svg)

Note: IOStreams benchmark is sequential because IOStreams get *slower* with additional parallelism due to internal locking on the locale.
Loaders using IOStreams or `fscanf` are both slow and do not parallelize.
See [parse_bench](https://github.com/alugowski/parse-bench) for more.

The majority of `fast_matrix_market`'s sequential improvement comes from using C++17's `<charconv>`.
If floating-point versions are not available then fall back on [fast_float](https://github.com/fastfloat/fast_float) 
and [Dragonbox](https://github.com/jk-jeon/dragonbox). These methods are then parallelized by chunking the input stream and parsing each chunk in parallel.

Use the `run_benchmarks.sh` script to see for yourself on your own system. The script builds, runs, and saves benchmark data.
Then simply run all the cells in the [benchmark_plots/plot.ipynb](benchmark_plots/plot.ipynb) Jupyter notebook.

# Full Featured

* `coordinate` and `array`, each readable into both sparse and dense structures.

* All `field` types supported: `integer`, `real`, `complex`, `pattern`.

* Support all C++ types.
    * `float`, `double`, `long double`, `std::complex<>`, integer types, `bool`.
    * Arbitrary types. `std::string` comes bundled. See [implementation](include/fast_matrix_market/app/user_type_string.hpp), [example usage](tests/user_type_test.cpp)
    * C++23 fixed width floating point types like `std::float32_t`.

* Automatic `std::complex` up-cast. For example, `real` files can be read into `std::complex<double>` arrays.

* Read and write `pattern` files. Read just the indices or supply a default value.

* Both `matrix` and `vector` files. Most loaders crash on `vector` files.
  * A vector data structure (sparse doublet or dense) will accept either a `vector` file, an M-by-1 `matrix` file or a 1-by-N `matrix` file.
  * A matrix data structure will read a `vector` file as if it was a M-by-1 column matrix.

* Ability to read just the header (useful for metadata collection).

* Read and write Matrix Market header comments.

* Read and write all symmetries: `general`, `symmetric`, `skew-symmetric`, `hermitian`.

* Optional (on by default) automatic symmetry generalization. If your code cannot make use of the symmetry but the file specifies one, the loader can emit the symmetric entries for you.

# Usage

`fast_matrix_market` provides both ready-to-use methods for common data structures and building blocks for your own. See [examples/](examples) for complete code.

In general, the `read_matrix_market_*` methods accept a `std::istream` and a datastructure to read into. This datastructure can be sparse or dense, and will accept any Matrix Market file.

The `write_matrix_market_*` methods accept a `std::ostream` and a datastructure. If the datastructure is sparse then a `coordinate` Matrix Market file is written, else an `array` file is written.

The methods also accept an optional `header` argument that can be used to read and write file metadata, such as the comment or whether the matrix is a `pattern`.

**Important: Open output file streams in binary mode.** Text mode on Windows will naturally emit files with CRLF line endings. FMM can read such files on any platform, but that is not always true of other MatrixMarket loaders.

## Coordinate / Triplets

Matrix composed of row and column index vectors and a value vector. Any vector class that can be resized and iterated like `std::vector` will work.

```c++
#include <fast_matrix_market/fast_matrix_market.hpp>

struct triplet_matrix {
    int64_t nrows = 0, ncols = 0;
    std::vector<int64_t> rows, cols;
    std::vector<double> vals;       // or int64_t, float, std::complex<double>, etc.
} mat;

fast_matrix_market::read_matrix_market_triplet(
                input_stream,
                mat.nrows, mat.ncols,
                mat.rows, mat.cols, mat.vals);
```

Doublet sparse vectors, composed of index and value vectors, are supported in a similar way by `read_matrix_market_doublet()`.

CSC and CSR matrices composed of `indptr`, `indices`, and `values` arrays can be written directly with `write_matrix_market_csc()`.

## Dense arrays

Any vector class that can be resized and iterated like `std::vector` will work.

Be mindful of whether your code expects row or column major ordering.

```c++
#include <fast_matrix_market/fast_matrix_market.hpp>

struct array_matrix {
    int64_t nrows = 0, ncols = 0;
    std::vector<double> vals;       // or int64_t, float, std::complex<double>, etc.
} mat;

fast_matrix_market::read_matrix_market_array(
                input_stream,
                mat.nrows, mat.ncols,
                mat.vals,
                fast_matrix_market::row_major);
```

1D dense vectors supported by the same method.

## GraphBLAS
`GrB_Matrix` and `GrB_Vector`s are supported, with zero-copy where possible. See [GraphBLAS README](README.GraphBLAS.md).
```c++
#include <fast_matrix_market/app/GraphBLAS.hpp>

GrB_Matrix A;
fast_matrix_market::read_matrix_market_graphblas(input_stream, &A);
```

## Eigen
Sparse and dense matrices and vectors are supported. See [Eigen README](README.Eigen.md).
```c++
#include <fast_matrix_market/app/Eigen.hpp>

Eigen::SparseMatrix<double> mat;
fast_matrix_market::read_matrix_market_eigen(input_stream, mat);
```

## SuiteSparse CXSparse
`cs_xx` structures (in both COO and CSC modes) are supported. See [CXSparse README](README.CXSparse.md).
```c++
#include <fast_matrix_market/app/CXSparse.hpp>

cs_dl *A;
fast_matrix_market::read_matrix_market_cxsparse(input_stream, &A, cs_dl_spalloc);
```

## Blaze
[Blaze](https://bitbucket.org/blaze-lib/blaze) sparse and dense matrices and vectors are supported. See [Blaze README](README.Blaze.md).
```c++
#include <fast_matrix_market/app/Blaze.hpp>

blaze::CompressedMatrix<double> A;
fast_matrix_market::read_matrix_market_blaze(input_stream, A);
```

## Armadillo
[Armadillo](https://arma.sourceforge.net/) sparse and dense matrices are supported. See [Armadillo README](README.Armadillo.md).
```c++
#include <fast_matrix_market/app/Armadillo.hpp>

arma::SpMat<double> A;
fast_matrix_market::read_matrix_market_arma(input_stream, A);
```

## Your Own

Use the provided methods to read or write the header.

Next read or write the body. You'll mostly just need to provide `parse_handler` (reads) and `formatter` (writes) classes to read and write from/to your datastructure, respectively. The class you need is likely already in the library, though subtle differences between datastructures mean each one tends to need some customization.

Follow the example of the triplet and array implementations in [include/fast_matrix_market/app/](include/fast_matrix_market/app).

## Generator

The `fast_matrix_market` write mechanism can write procedurally generated data as well as materialized datastructures.
See [generator README](README.generator.md).

For example, write a 10-by-10 identity matrix to `output_stream`:
```c++
#include <fast_matrix_market/app/generator.hpp>

fast_matrix_market::write_matrix_market_generated_triplet<int64_t, double>(
    output_stream, {10, 10}, 10,
    [](auto coo_index, auto& row, auto& col, auto& value) {
        row = coo_index;
        col = coo_index;
        value = 1;
    });
```

# Installation

`fast_matrix_market` is written in C++17. Parallelism uses C++11 threads. Header-only if optional dependencies are disabled.

### CMake

```cmake
include(FetchContent)
FetchContent_Declare(
        fast_matrix_market
        GIT_REPOSITORY https://github.com/alugowski/fast_matrix_market
        GIT_TAG main
        GIT_SHALLOW TRUE
)
FetchContent_MakeAvailable(fast_matrix_market)

target_link_libraries(YOUR_TARGET fast_matrix_market::fast_matrix_market)
```

Alternatively copy or checkout the repo into your project and:
```cmake
add_subdirectory(fast_matrix_market)
```
See [examples/](examples) for what parts of the repo are needed.

### Manual Copy
You may also copy `include/fast_matrix_market` into your project's `include` directory.
See also [dependencies](dependencies).

fast_matrix_market

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