# Execution network algorithm¶

## Problem description¶

This algorithm describes how to get the execution network from a node after fully expanding its composite algorithms.

The requirements are:

a network of connected algorithms (composite or not). They are connected via their output sources which connect to one or more input sink(s). Every sink is only connected to a single source.

Composite define a

`declareProcessOrder()`

method, which defines the steps to be taken when this algorithm should be executed. The steps can be one of two types:`ChainFrom(algo)`

: which runs the given algorithm and all its dependencies which are contained inside the Composite`SingleShot(algo)`

: which runs the given algorithm once

As the steps are declared in sequential order in the

`declareProcessOrder()`

method, they should also be run that way, which means that the next step should depend on the completion of all the previous ones [1].

Composite algorithms are described more in details in the Composite algorithm description page.

## Algorithm description¶

The following algorithms is used. It is written in pseudo-code that is a mix of C++ (for showing types) and Python (for avoiding boilerplate code that comes with C++). Hopefully, it makes for a clear reading.

### Structures used¶

```
// A Node represents a node in the execution graph, and as such points to an algorithm it
// represents and has a list of children which execution should come after this one.
class Node {
// the algorithm that this node represents in the execution tree
Algorithm* algo;
// the algorithms that need to be run after this one has completed its execution
vector<Node*> children;
};
```

```
// A FractalNode represents a node that can be expanded, by recursively replacing a Composite
// algorithm with its constituent parts. It is a temporary structure only used while computing
// the execution network from the visible network.
class FractalNode : public Node {
// the expanded version of this fractal node
FractalNode* expanded;
// for each source name, the list of expanded (ie: not composite) algorithm which execution
// should be completed before this source is allowed to produce data
map<string, vector<FractalNode*> > innerMap;
// for each source name, the list of visible (ie: composite or not) connected algorithms,
// that is, the list of algorithms that need to be run after this source has produced data
map<string, vector<FractalNode*> > outputMap;
};
```

### Detailed algorithm¶

The algorithm we will use can be summarily described as:

Build visible network from root node using FractalNodes. The visible network is the network we obtain when looking at the explicit connections made by the user.

Build another graph with the same topology where all the nodes have been expanded (i.e.: the Composite algorithms have been replaced by their constituent parts).

Using the connections defined in the first graph, reconnect all nodes in the second graph. This is not as trivial as it seems as source/sink proxies might have different names than the connector they relay.

The pseudo-code algorithm is the following:

```
def buildExecutionNetwork(rootAlgorithm):
# 1- build visible network: this is the tree obtained by setting as children of a node N
# all algorithms which have a sink connected to a source of the algorithm pointed to
# by node N
FractalNode* executionNetworkRoot = visibleNetwork(rootAlgorithm)
# 2- expand all nodes of this first graph
for node in DFS(executionNetworkRoot):
node.expanded = expandNode(node)
# 3- connect expanded network
for node in DFS(executionNetworkRoot):
# connectedNodes is the list of externally connected nodes to the given output
for outputName, connectedNodes in node.outputMap:
# innerNodes (= node.expanded.innerMap[outputName]) is the list of nodes
# from inside the composite connected to a given output. Although we can only
# have 1 source connected to a SourceProxy, we can have multiple algorithms
# that we have to wait for before computing the next algorithm in the tree.
for innerNode in node.expanded.innerMap[outputName]:
# for each expanded node inside the algorithm which outputs data on a given
# source (output), and for each connected algorithm on this source...
for cnode in connectedNodes:
# ... we add the expanded version of the connected algorithm as a
# dependency for the inner node.
connect(innerNode, # expanded algo inside the composite
cnode.expanded) # expanded algo outside, i.e.: inside the visible dependency
# 4- clean up our temporary structure and return the execution network
return cleanedUpExecutionNetworkRoot
# This function expands a given node and fills its innerMap during the process
def expandNode(node):
if not is_composite(node):
# non-composite algorithm: all the inner connections are on the algorithm itself
for outputName in node.algorithm.outputNames:
node.expanded.innerMap[outputName] = [ node.expanded ]
else:
# node is a composite algorithm
for step in algo.processOrder:
stepRoot = step.algorithm
if step.type == 'single':
fillInnerMapWithConnections(stepRoot)
stepRoot.expanded = expandNode(stepRoot)
elif step.type == 'chain':
# simplified; should also fill stepRoot.innerMap while doing this
stepRoot.expanded = buildExecutionNetwork(stepRoot)
```

At the end, we should obtain a Hasse Diagram as a result.

## Execution of the network¶

There are 2 main ways of running a network:

the

*single-threaded*way: in this case, we need to topologically sort the network in order to get the execution order. Once we have the topological order, we can run each algorithm sequentially until the generator signals us that it is over.This is what is implemented in the Network class.

the

*multi-threaded*way: in this case, we would have to create tasks (using a wavefront pattern, for instance) for a task library, such as Intel TBB, and let its scheduler run them.Note that this is not implemented in Essentia 2.0. It had been implemented in a previous version, but the speedup gained from parallelization was not as high as expected, as most feature extractors have a long sequential part at the beginning when loading and computing the FFT, and only after that fan out in a way that is parallelizable. Given that it is very common to have audio loading + FFT taking up to 30% of execution time, Amdahl’s law shows us that the expected returns indeed are not optimal. In practice, we realized that when computing large databases of audio tracks, it is much more adequate to run each extractor in a single-threaded manner but distribute them on the CPU cores, as this scales linearly with the number of cores thrown at it.