# Data Batches¶

Modern computer architectures have grown more complex due to the fact that the speed of modern CPUs improves much faster than the memory access latency of the RAM. To achieve the highest performance for numerical data processing, data points need to be grouped into smaller subsets which we call batches. A batch can be described as a two dimensional structure. The first dimension, which we call “row”, represents the different data points, and the second dimension, which we refer to as “column”, represents the components of the data space or structure.

Ideally, after creation of the batches, data should be placed dense in the RAM such that the CPU can evaluate the whole batch at once. A typical example of such a structure is a matrix when each single data point is a vector. These single vectors are copied into rows of the matrix to form a batch.

A typical example for an algorithm which hugely benefits from this new grouping is a linear model without offset. For single vectors x, the evaluation of such a linear model would simply be written as

$f(x)=Ax \enspace .$

Here, the computation is a matrix-vector product. Let now X be the matrix holding a batch of n vectors, for all of which we want to compute the response of the linear model. Then, when the data are stored in batches and our model can operate directly on batches, the batch algorithm can be written as

$f(X)=AX^T \enspace .$

Such a transition to aggregate data structures can yield a speedup of at least a factor of two when ATLAS is enabled as linear algebra backend, and potentially even beyond an order of magnitude, depending on the dimensions of A and X. The reason is that the matrix-matrix multiplication can be computed much more memory friendly than a series of matrix-vector products. Small blocks of memory can be reused such that the CPU can hold these parts in its cache. This reduces the number of memory lookups needed from the relatively slow RAM.

## More on batches in Shark¶

Usage of batches in Shark is quite simple in most cases. As long as only standard types are used as inputs, the Data class automatically creates efficient batches using a default size. At the current state of the library, not all algorithms and models make fullest use of the batch interface – but in such cases, safe fallbacks to single element methods are used.

The mapping between points and batches is quite simple in most cases. Here is a small list:

Point type Batch type
double RealVector
int IntVector
RealVector RealMatrix
CompressedIntVector CompressedIntMatrix
T std::vector<T>

T here stands for an arbitrary type. So the default case for non-standard points is std::vector<T>, which enables Shark to generate batches for all types. In other words, Shark’s batch interface works seamlessly with strings and graphs, and whatever data type a user may need.

As you can see, the choice of batches is quite convenient for most cases. Single values are stored in vectors and vectors in matrices. When you write your own programs you can be assured that the batch types are exactly these and do not need to bother about these types.

## Element Access¶

When you want to access the i-th element of the matrix, you can write row(batch,i), or to query the size use batch.size1(). For vectors you can use batch(i) and batch.size(). But what happens when a more general algorithm, like for example the error function, is to be implemented? In this case you do not know which functions or methods to use, since the types shown above have totally different interfaces. Shark circumvents this problem by adapting and extending the interface of boost.range:

Function Meaning
boost::begin(batch) returns an iterator to the beginning of the range of elements
boost::end(batch) returns an iterator to the end of the range of elements
boost::size(batch) returns the number of elements in the batch
shark::get(batch,i) returns a reference to the i-th element of the batch

For typical containers which already support batch.begin(), batch.end() and batch.size(), the default implementation provided by Boost is sufficient. For the ublas matrices, Shark provides reasonably implemented iterators.

## The Batch<T> Traits class¶

Suppose your data points have an arbitrary type T. There are a few things that you want to know:

• What is the Batch type of T and how can it be created?
• How many elements does the batch have?
• How can single elements be accessed?

Typically T cannot answer this question by itself, since we cannot change its definition. Even if we could change it for some, it clearly is not possible for basic types like int or double. Therefore we need an external class that explicitly represents this information for use at compile-time. For Shark batches, this class is the Batch<T> class template. It is a traits class, meaning that it tells you something about a type, in this case T and its batch type. Let’s take a closer look at the basic definition of Batch, for now in include/shark/Data/BatchInterface.h:

template<class T>
struct Batch{
typedef implementation-specific-type type;            //type of a batch
typedef implementation-specific-type reference;       //reference to an element of the batch
typedef implementation-specific-type const_reference; //const_reference to an element of the batch
typedef implementation-specific-type iterator;        //iterator over all elements of the batch
typedef implementation-specific-type const_iterator;  //const_iterator over all elements of the batch

static type createBatch(T const& input, std::size_t size = 1);
};


We introduce class usage in a step-by-step example – improving it as we go along until we do not need to know the type of points any more. We begin with short example code where the point is a vector and the batch is a matrix:

RealVector point(10);
RealMatrix batchOfPoints(5,10);
row(batchOfPoints, 0) = point;
std::cout << batchOfPoints.size1();


Let’s answer the first question: how to query the type of a batch? This is easy using Batch<T>::type:

RealVector point(10);
Batch<RealVector>::type batchOfPoints(5,10);
row(batchOfPoints, 0) = point;
std::cout << batchOfPoints.size1();


Not bad. Still, we explicitly use that batch is a Matrix by calling its size1 member. That’s fine, as long as we know that T can only be a vector. But sometimes we do not even know that, typically in generic code. Let’s begin improving it, by changing the element access as well as the size query. For the first, we use get, and for the second, size, both from the previous section:

RealVector point(10);
Batch<RealVector>::type batchOfPoints(5,10);
get(batchOfPoints, 0) = point;
std::cout << size(batchOfPoints);


Now the last thing missing is the creation of the batch. We always need an element to create a batch from it. It serves as blueprint ensuring that the batch can store the elements. For example in the case of RealVector, it ensures that the matrix has the same amount of columns as the vector has dimensions. In this case, we use the point available:

RealVector point(10);
Batch<RealVector>::type batchOfPoints = Batch<RealVector>::createBatch(point, 5);
get(batchOfPoints, 0) = point;
std::cout << size(batchOfPoints);


While this surely looks more difficult than the first version, it is also completely type independent. Note that even when creating batches of size 1, it is still necessary to assign the point after batch initialization (as in line three of the above snippet). Otherwise, the batches point would be uninitialized.

## Adapting Batch<T> for a user defined structure¶

Let’s face it: even though we like to pretend that all our data points are vectors, we often only make our data look like it when in fact it is a collection of different types – for example mixing reals, categorical data and sparse binary features with strings of varying lengths, or even graphs. We often have routines that produce these features and then spend a lot of time writing code that creates data vectors out of the points. Sometimes this is exactly what we want (for example when the model is a neural net which does not have a sense of data structure at all). But for more specialized methods we might want to represent this structure explicitly. At the same time, we want the efficiency of a good data representation in batches. So how do we solve it? By creating a batch type and specializing Batch<T> on it!

struct Point{
RealVector feature1;
SparseIntVector feature2;
};


There is an easy automatic way to create a sufficient specialisation of batch for Point in Shark using the macro SHARK_CREATE_BATCH_INTERFACE:

#define PointVars (feature1)(feature2)
#define PointTypes (RealVector)(SparseIntVector)
struct Batch< Point >{
SHARK_CREATE_BATCH_INTERFACE( Point,
(RealVector, feature1),(SparseIntVector, feature2))
};


This also works when Point is templatized, for example like this:

template<class Type1, class Type2>
struct Point{
RealVector feature1;
SparseIntVector feature2;
};


Todo

TG: should it be Type1 feature1; Type2 feature2; ??? mt: i second this question

In this case, we have to add the template parameters to the Batch specialisation:

#define PointVars (feature1)(feature2)
#define PointTypes (Type1)(Type2)
#define PointName Point<Type1,Type2>
template<class Type1,class Type2>
struct Batch< Point >{
SHARK_CREATE_BATCH_INTERFACE( PointName, PointVars, PointTypes )
};
#undef PointVars
#undef PointTypes


Todo

mt: undef pointname also?

You see how the convenience macro enables the definition of Shark batch types for arbitrary data structures without much trouble. Recall that this is only necessary for user defined non-vectorial data formats. The standard cases, such as stacking vectors into matrices, are already covered by the Shark library itself.