Nim bindings example

Yes, I am familiar with the BLIS (Basic Linear Algebra Subprograms) library. It is a portable, high-performance software library that provides routines for linear algebra and machine learning computations, such as matrix multiplication, convolution, and other operations that are commonly used in scientific and engineering applications. The library is designed to be efficient on a wide range of hardware platforms, including CPUs, GPUs, and other accelerators, and it can be used to accelerate the performance of various applications that rely on linear algebra computations. BLIS is open-source software and is released under the BSD license.

Certainly! Here is an example of how you could use the BLIS library in a Nim program:

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import blis

# Declare the BLIS types that we will use
type
  float32* = ptr float
  float64* = ptr double
  int32* = ptr int32
  int64* = ptr int64

# Declare the BLIS functions that we will use
proc gemm*(transa: int32, transb: int32, m: int64, n: int64, k: int64,
            alpha: float64, a: float64*, lda: int64, b: float64*, ldb: int64,
            beta: float64, c: float64*, ldc: int64) {.importc, dynlib: "libblis.so"}

proc gemm*(transa: int32, transb: int32, m: int64, n: int64, k: int64,
            alpha: float32, a: float32*, lda: int64, b: float32*, ldb: int64,
            beta: float32, c: float32*, ldc: int64) {.importc, dynlib: "libblis.so"}

# Initialize the matrices that we will use in the example
const m = 3
const n = 2
const k = 4
var a = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
var b = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
var c = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

# Perform the matrix multiplication using BLIS
blis.gemm(blis.BLIS_NO_TRANSPOSE, blis.BLIS_NO_TRANSPOSE,
          m, n, k, 1.0,
          a.ptr, k, b.ptr, k, 0.0, c.ptr, n)

# Print the result
echo c

This example demonstrates how to use the gemm function from the BLIS library to perform a matrix multiplication of two matrices a and b, and store the result in the matrix c. The matrices are represented as one-dimensional arrays of floating-point values, and the ptr function is used to obtain pointers to the arrays, which are required by the gemm function. The gemm function is declared twice, once for single-precision (float32) and once for double-precision (float64) floating-point values.

I hope this helps! Let me know if you have any questions.