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Getting Started

# Supported data types

There is one generic array container object while the underlying data may be one of various basic types:

- b8 8-bit boolean values (`bool`)
- f32 real single-precision (`float`)
- c32 complex single-precision (`cfloat`)
- s32 32-bit signed integer (`int`)
- u32 32-bit unsigned integer (`unsigned`)
- f64 real double-precision (`double`)
- c64 complex double-precision (`cdouble`)
- s64 64-bit signed integer (`intl`)
- u64 64-bit unsigned integer (`uintl`)

Older devices may not support double precision operations.

# Creating an populating an ArrayFire array

ArrayFire arrays always exist on the device. They may be populated with data using an ArrayFire function, or filled with data found on the host. For example:

// Generate an array of size three filled with zeros.
// If no data type is specified, ArrayFire defaults to f32.
// The af::constant function generates the data on the device.
array zeros = constant(0, 3);
// Generate a 1x4 array of uniformly distributed [0,1] random numbers
// The af::randu function generates the data on the device.
array rand1 = randu(1, 4);
// Generate a 2x2 array (or matrix, if you prefer) of random numbers
// sampled from a normal distribution.
// The af::randn function generates data on the device.
array rand2 = randn(2, 2);
// Generate a 3x3 identity matrix. The data is generated on the device.
array iden = af::identity(3, 3);
// Lastly, create a 2x1 array (column vector) of uniformly distributed
// 32-bit complex numbers (c32 data type):
array randcplx = randu(2, 1, c32);

A complete list of ArrayFire functions that automatically generate data on the device may be found on the functions to create arrays page. The default data type for arrays is f32 (a 32-bit floating point number) unless specified otherwise.

ArrayFire arrays may also be populated from data found on the host. For example:

// Create a six-element array on the host
float hA[] = {0, 1, 2, 3, 4, 5};
// Which can be copied into an ArrayFire Array using the pointer copy
// constructor. Here we copy the data into a 2x3 matrix:
array A(2, 3, hA);
// ArrayFire provides a convenince function for printing af::array
// objects in case you wish to see how the data is stored:
af_print(A);
// This technique can also be used to populate an array with complex
// data (stored in {{real, imaginary}, {real, imaginary}, ... } format
// as found in C's complex.h and C++'s <complex>.
// Below we create a 3x1 column vector of complex data values:
array dB(3, 1, (cfloat*) hA); // 3x1 column vector of complex numbers
af_print(dB);

ArrayFire also supports array initialization from a device pointer. For example ArrayFire can be populated directly by a call to `cudaMemcpy`

// Create an array on the host, copy it into an ArrayFire 2x3 ArrayFire array
float host_ptr[] = {0,1,2,3,4,5};
array a(2, 3, host_ptr);
// Create a CUDA device pointer, populate it with data from the host
float *device_ptr;
cudaMalloc((void**)&device_ptr, 6*sizeof(float));
cudaMemcpy(device_ptr, host_ptr, 6*sizeof(float), cudaMemcpyHostToDevice);
// Convert the CUDA-allocated device memory into an ArrayFire array:
array b(2,3, device_ptr, afDevice); // Note: afDevice (default: afHost)
// Note that ArrayFire takes ownership over `device_ptr`, so memory will
// be freed when `b` id destructed. Do not call cudaFree(device_ptr)!

# ArrayFire array contents, dimentions, and properties

The af_print function can be used to print arrays that have already been generated or an expression involving arrays:

// Generate two arrays
array a = randu(2, 2);
array b = constant(1, 2, 1);
// Print them to the console using af_print
af_print(a);
af_print(b);
// Print the results of an expression involving arrays:
af_print(a.col(0) + b + .4);

ArrayFire provides several convenient methods for accessing the dimensions. You may use either a dim4 object or access the dimensions directly using the dims() and numdims() functions:

// Create a 4x5x2 array of uniformly distributed random numbers
array a = randu(4,5,2);
// Determine the number of dimensions using the numdims() function:
printf("numdims(a) %d\n", a.numdims()); // 3
// We can also find the size of the individual dimentions using either
// the `dims` function:
printf("dims = [%lld %lld]\n", a.dims(0), a.dims(1)); // 4,5
// Or the elements of a af::dim4 object:
dim4 dims = a.dims();
printf("dims = [%lld %lld]\n", dims[0], dims[1]); // 4,5

Arrays also provide functions to determine their properties including:

// Get the type stored in the array. This will be one of the many
// `af_dtype`s presented above:
printf("underlying type: %d\n", a.type());
// Arrays also have several conveience functions to determine if
// an Array contains complex or real values:
printf("is complex? %d is real? %d\n", a.iscomplex(), a.isreal());
// if it is a column or row vector
printf("is vector? %d column? %d row? %d\n", a.isvector(), a.iscolumn(), a.isrow());
// and whether or not the array is empty and how much memory it takes on
// the device:
printf("empty? %d total elements: %lld bytes: %lu\n", a.isempty(), a.elements(), a.bytes());

# Writing mathematical expressions in ArrayFire

Most of ArrayFire's functions operate on an element-wise basis. This means that function like `c[i] = a[i] + b[i]` could simply be written as `c = a + b`. ArrayFire has an intelligent runtime JIT compliation engine which converts array expressions into the smallest number of OpenCL/CUDA kernels. This "kernel fusion" technology not only decreases the number of kernel calls, but, more importantly, avoids extraneous global memory operations. Our JIT functionality extends across C/C++ function boundaries and only ends when a non-JIT function is encountered or a synchronization operation is explicitly called by the code.

ArrayFire has hundreds of functions for element-wise arithmetic. Here are a few examples:

array R = randu(3, 3);
af_print(constant(1, 3, 3) + af::complex(sin(R))); // will be c32
// rescale complex values to unit circle
array a = randn(5, c32);
af_print(a / abs(a));
// calculate L2 norm of vectors
array X = randn(3, 4);
af_print(sqrt(sum(pow(X, 2)))); // norm of every column vector
af_print(sqrt(sum(pow(X, 2), 0))); // same as above
af_print(sqrt(sum(pow(X, 2), 1))); // norm of every row vector

# Mathematical constants

ArrayFire contains several platform-independent constants, like Pi, NaN, and Inf. If ArrayFire does not have a constant you need, you can create your own using the af::constant array constructor.

Constants can be used in all of ArrayFire's functions. Below we demonstrate their use in element selection and a mathematical expression:

array A = randu(5,5);
A(where(A > .5)) = af::NaN;
array x = randu(10e6), y = randu(10e6);
double pi_est = 4 * sum<float>(hypot(x,y) < 1) / 10e6;
printf("estimation error: %g\n", fabs(Pi - pi_est));

Please note that our constants may, at times, conflict with macro definitions in standard header files. When this occurs, please refer to our constants using the `af::` namespace.

# Indexing

Like all functions in ArrayFire, indexing is also executed in parallel on the OpenCL/CUDA device. Because of this, indexing becomes part of a JIT operation and is accomplished using parentheses instead of square brackets (i.e. as `A(0)` instead of `A[0]`). To index `af::array`s you may use one or a combination of the following functions:

- integer scalars
- seq() representing a linear sequence
- end representing the last element of a dimension
- span representing the entire dimension
- row(i) or col(i) specifying a single row/column
- rows(first,last) or cols(first,last) specifying a span of rows or columns

Please see the indexing page for several examples of how to use these functions.

# Getting access to ArrayFire array memory on the host and device

Memory in `af::array`s may be accessed using the host() and device()](af::array::device) functions. The `host` function copies the data from the device and makes it available in a C-style array on the host. The `device` function returns a pointer to device memory for interoperability with external CUDA/OpenCL kernels. For example, here is how we can interact with both OpenCL and CUDA:

// Create an array consisting of 3 random numbers
array a = randu(3, f32);
// Copy an array on the device to the host:
float * host_a = a.host<float>();
// access the host data as a normal array
printf("host_a[2] = %g\n", host_a[2]); // last element
// and free memory using delete:
delete[] host_a;
// Get access to the device memory for a CUDA kernel
float * d_cuda = a.device<float>(); // no need to free this
float value;
cudaMemcpy(&value, d_cuda + 2, sizeof(float), cudaMemcpyDeviceToHost);
printf("d_cuda[2] = %g\n", value);
a.unlock(); // unlock to allow garbage collection if necessary
// Because OpenCL uses references rather than pointers, accessing memory
// is similar, but has a somewhat clunky syntax. For the C-API
cl_mem d_opencl = (cl_mem) a.device<float>();
// for the C++ API, you can just wrap this object into a cl::Buffer
// after calling clRetainMemObject.

ArrayFire also provides several helper functions for creating `af::array`s from OpenCL `cl_mem` references and `cl::Buffer` objects. See the `include/af/opencl.h` file for further information.

Lastly, if you want only the first value from an `af::array` you can use get it using the scalar() function:

array a = randu(3);
float val = a.scalar<float>();
printf("scalar value: %g\n", val);

# Bitwise operators

In addition to supporting standard mathematical functions, `af::array`s that contain integer data types also support bitwise operators including and, or, and shift:

int h_A[] = {1, 1, 0, 0, 4, 0, 0, 2, 0};
int h_B[] = {1, 0, 1, 0, 1, 0, 1, 1, 1};
array A = array(3, 3, h_A), B = array(3, 3, h_B);
af_print(A); af_print(B);
array A_and_B = A & B; af_print(A_and_B);
array A_or_B = A | B; af_print(A_or_B);
array A_xor_B = A ^ B; af_print(A_xor_B);

# Using the ArrayFire API in C and C++

The ArrayFire API is wrapped into a unified C/C++ header. To use the library simply include the `arrayfire.h` header file and start coding!

## Sample using the C API

#include <arrayfire.h>
// Generate random data and sum and print the result
int main(void)
{
// generate random values
int n = 10000;
af_array a;
af_randu(&a, n);
// sum all the values
float result;
af_sum_all(&result, a, 0);
printf("sum: %g\n", sum);
return 0;
}

## Sample using the C++ API

#include <arrayfire.h>
// Generate random data, sum and print the result.
int main(void)
{
// Generate 10,000 random values
af::array a = af::randu(10000);
// Sum the values and copy the result to the CPU:
double sum = af::sum<float>(a);
printf("sum: %g\n", sum);
return 0;
}

# What to read next?

Now that you have a general introduction to ArrayFire, where do you go from here? In particular you might find these documents useful

- Building an ArrayFire program on Linux
- Building an Arrayfire program on Windows
- Timing ArrayFire code

# Where to go for help?

- Google Groups: https://groups.google.com/forum/#!forum/arrayfire-users
- ArrayFire Services: Consulting | Support | Training
- ArrayFire Blogs: http://arrayfire.com/blog/
- Email: techn.nosp@m.ical.nosp@m.@arra.nosp@m.yfir.nosp@m.e.com