# Algorithm Anatomy¶

oneDAL primarily targets algorithms that are extensively used in data analytics. These algorithms typically have many parameters, i.e. knobs to control its internal behavior and produced result. In machine learning, those parameters are often referred as meta-parameters to distinguish them from the model parameters learned during the training. Some algorithms define a dozen meta-parameters, while others depend on another algorithm as, for example, the logistic regression training procedure depends on optimization algorithm.

Besides meta-parameters, machine learning algorithms may have different stages, such as training and inference. Moreover, the stages of an algorithm may be implemented in a variety of computational methods. For instance, a linear regression model could be trained by solving a system of linear equations [Friedman17] or by applying an iterative optimization solver directly to the empirical risk function [Zhang04].

From computational perspective, algorithm implementation may rely on different floating-point types, such as float, double or bfloat16. Having a capability to specify what type is needed is important for the end user as their precision requirements vary depending on a workload.

To best tackle the mentioned challenges, each algorithm is decomposed into descriptors and operations.

## Descriptors¶

A descriptor is an object that represents an algorithm including all its meta-parameters, dependencies on other algorithms, floating-point types, and computational methods. A descriptor serves as:

• A dispatching mechanism for operations. Based on a descriptor type, an operation executes a particular algorithm implementation.

• An aggregator of meta-parameters. It provides an interface for setting up meta-parameters at either compile-time or run-time.

• An object that stores the state of the algorithm. In the general case, a descriptor is a stateful object whose state changes after an operation is applied.

Each oneDAL algorithm has its own dedicated namespace, where the corresponding descriptor is defined (for more details, see Namespaces). Descriptor, in its turn, defines the following:

• Template parameters. A descriptor is allowed to have any number of template parameters, but shall support at least two:

• Float is a floating-point type that the algorithm uses for computations. This parameter is defined first and has the onedal::default_float_t default value.

• Method is a tag-type that specifies the computational method. This parameter is defined second and has the method::by_default default value.

• Properties. A property is a run-time parameter that can be accessed by means of the corresponding getter and setter methods.

The following code sample shows the common structure of a descriptor’s definition for an abstract algorithm. To define a particular algorithm, the following strings shall be substituted:

• %ALGORITHM% is the name of an algorithm and its namespace. All classes and structures related to that algorithm are defined within the namespace.

• %PROPERTY_NAME% and %PROPERTY_TYPE% are the name and the type of one of the algorithm’s properties.

namespace onedal::%ALGORITHM% {

template <typename Float  = default_float_t,
typename Method = method::by_default,
/* more template parameters */>
class descriptor {
public:
/* Getter & Setter for the property called %PROPERTY_NAME% */
descriptor& set_%PROPERTY_NAME%(%PROPERTY_TYPE% value);
%PROPERTY_TYPE% get_%PROPERTY_NAME%() const;

/* more properties */
};

} // namespace onedal::%ALGORITHM%


Each meta-parameter of an algorithm is mapped to a property that shall satisfy the following requirements:

• Properties are defined with getter and setter methods. The underlying class member variable that stores the property’s value is never exposed in the descriptor interface.

• The getter returns the value of the underlying class member variable.

• The setter accepts only one parameter of the property’s type and assigns it to the underlying class member variable.

• Most of the properties are preset with default values, others are initialized by passing the required parameters to the constructor.

• The setter returns a reference to the descriptor object to allow chaining calls as shown in the example below.

auto desc = descriptor{}
.set_property_name_1(value_1)
.set_property_name_2(value_2)
.set_property_name_3(value_3);


### Floating-point Types¶

It is required for each algorithm to support at least one implementation-defined floating-point type. Other floating-point types are optional, for example float, double, float16, and bfloat16. It is up to a specific oneDAL implementation whether or not to support these types.

The floating-point type used as a default in descriptors is implementation-defined and shall be declared within the top-level namespace.

namespace onedal {
using default_float_t = /* implementation defined */;
} // namespace onedal


### Computational Methods¶

The supported computational methods are declared within the %ALGORITHM%::method namespace using tag-types. Algorithm shall support at least one computational method and declare the by_default type alias that refers to one of the computational methods as shown in the example below.

namespace onedal::%ALGORITHM% {
namespace method {
struct x {};
struct y {};
using by_default = x;
} // namespace method
} // namespace onedal::%ALGORITHM%