ANT a NdArry
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ANT is a NdArray that is developing with c++ and Cuda from scratch. Currently, it supports some basic array operations just like NumPy or Cupy but in language, C++ instate of python. It is an educational project to understand how a NumPy-like library works, and how automatic broadcasting work. Apart from this project also helps to learn Cuda programming and how our GPU work to do parallel programming. To learn how this ant array work visits the documentation and test folder.
API Documentations: here
Some functionalities of ant NdArray
Array Creation
#include "array.h"
ndarray::Shape s = {3};
double a[] = {1,2,3};
ndarray::Array A(s, a);
EXPECT_EQ(A.rank(),1);
EXPECT_EQ(A.size(), 3);
Addition
ndarray::Shape s = {3};
double a[] = {1,2,3};
ndarray::Array A(s, a);
ndarray::Array B(s, a);
auto C = A + B;
EXPECT_EQ(A.rank(),1);
EXPECT_EQ(B.rank(), 1);
EXPECT_EQ(C.rank(), 1);
EXPECT_EQ(C.size(), 3);
double * cActualData = C.hostData();
double cExpectedData[] = {2,4,6};
for(int i =0; i< C.size(); i++){
EXPECT_EQ(cActualData[i], cExpectedData[i]);
}
Broadcasting
ndarray::Shape a_shape = {4,1};
ndarray::Shape b_shape = {1,3};
double a_data[] ={1,2,3,4};
double b_data[] = {1,2,3};
ndarray::Array A(a_shape, a_data);
ndarray::Array B(b_shape, b_data);
auto C = A+B;
double *actual = C.hostData();
double expected[] = {2,3,4,3,4,5,4,5,6,5,6,7};
VectorEQ(C.shape(), {4,3});
DoubleArrayEQ(actual, expected, 12);
Matmul
ndarray::Shape a_shape = {2,4};
ndarray::Shape b_shape = {4,2};
double a_data[] = {1,2,3,4,5,6,7,8};
double b_data[] ={8,7,6,5,4,3,2,1};
ndarray::Array A(a_shape, a_data);
ndarray::Array B(b_shape, b_data);
auto C = A.matmul(B);
double *actual = C.hostData();
double expected[] = {40,30,120,94};
VectorEQ(C.shape(), {2,2});
DoubleArrayEQ(actual, expected, 4);
Matmul with broadcasting
ndarray::Shape a_shape = {2,2};
ndarray::Shape b_shape = {2,2,3};
double a_data[] = {1,2,3,4};
double b_data[] ={12,11,10,9,8,7,6,5,4,3,2,1};
ndarray::Array A(a_shape, a_data);
ndarray::Array B(b_shape, b_data);
auto C = A.matmul(B);
double *actual = C.hostData();
double expected[] = {30,27,24,72,65,58, 12,9,6,30,23,16};
VectorEQ(C.shape(), {2,2,3});
DoubleArrayEQ(actual, expected, 12);
Transpose
ndarray::Shape a_shape = {5,4};
double a_data[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20};
ndarray::Array A(a_shape, a_data);
A.transpose({1,0});
double expected[][5] = {{1,5,9,13,17},{2,6,10,14,18},{3,7,11,15,19},{4,8,12,16,20}};
VectorEQ(A.shape(), {4,5});
VectorEQ(A.stride(), {1,4});
Indexing
ndarray::Shape shape= {2,3};
double data[][3] = {{1,2,3},{10,20,30}};
ndarray::Array A(shape, *data);
EXPECT_EQ(A(0,0), 1);
EXPECT_EQ(A(0,1), 2);
EXPECT_EQ(A(0,2), 3);
EXPECT_EQ(A(1,0), 10);
EXPECT_EQ(A(1,1), 20);
EXPECT_EQ(A(1,2), 30);
Slicing
ndarray::Shape a_shape = {3,3,2};
double a_data[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18};
ndarray::Array A(a_shape, a_data);
ndarray::Array B = A[{{1,5,1}, {0,-1},{1}}];
double *actual = B.hostData();
double expected[] = {8,10,14,16};
VectorEQ(B.shape(), {2,2});
DoubleArrayEQ(actual, expected, 4);