## Parallelism

A large fraction of studies using `Pythia` follow the same general steps: first the `Pythia` object(s) and histograms are initialized. Then there is a loop where events are generated using `Pythia::next`. These events are subsequently analyzed one by one, and the resulting statistics are stored in histograms. Finally, the histograms are normalized and the results are plotted.

In this procedure, usually the events are independently generated, and in principle it should be possible to generate them in parallel. The `PythiaParallel` class provides a simple interface for doing this. Objects of this class behave similarly to `Pythia` objects in that they can be configured using `readString` and `readFile`, and are initialized using `init`. The difference is that instead of having a `next` method that generates a single event, they have the method `run` which generates a number of events in parallel and analyzes them using a used-defined callback function. The program flow using the parallelism framework is as follows:

1. In addition to the standard header file, the parallel framework header must be included.

           #include "Pythia8/Pythia.h"
           #include "Pythia8/PythiaParallel.h"

2. The next step is to create, configure, and initialize a parallel generator object. This process is identical to that for a standard `Pythia` object.

           PythiaParallel pythia;
           pythia.readString("HardQCD:all = on");
           pythia.init();

3. Finally, the `PythiaParallel` object is run with a user function which performs the necessary analysis on the events. In this example, an event multiplicity histogram is filled.

           Hist mult("mult", 100, -0.5, 799.5);
           pythia.run(10000, [&](Pythia* pythiaPtr) {
             int nFinal = 0;
             for (int i = 0; i < pythiaPtr->event.size(); ++i)
               if (pythiaPtr->event[i].isFinal()) ++nFinal;
             mult.fill( nFinal );
           });

   Here the syntax `[&](Pythia* pythiaPtr)` may be unfamiliar. It defines a <ie>lambda</ie> function that takes a pointer to a `Pythia` object as an argument. The `[&]` indicates that all local variables used by the lambda function (in this case only the `mult` histogram object) are passed by reference.

The scope of this class is to provide a lightweight way of parallelising simple `Pythia` studies. As such, it may not offer the necessary features to support more complicated use cases. The specific features are documented below, and examples are given in `main161`, `main162`, `main163`, and `main204`.

vector<long> PythiaParallel::run(long nEvents, function<void(Pythia*)> callback)
vector<long> PythiaParallel::run( function<void(Pythia*)> callback)
this is the main method of `PythiaParallel`, analogous to `Pythia::next`. The method generates `nEvents` events in parallel, distributing the tasks automatically to different `Pythia` instances. If `nEvents` is not specified, `Main:numberOfEvents` is used instead. The `callback` is a function that will be called when each event is generated. It takes a pointer to the `Pythia` instance that generated the event as an argument, and the event can then be accessed through `Pythia::event`. If an event fails to generate successfully, it will not be passed to the callback. By default, callbacks are synchronized. That is, only one callback can be active at the same time, which means it is safe to e.g. write to histograms from within the callback. Asynchronous processing of callbacks can be enabled by setting `Parallelism:processAsync = on` (see below). Returns a `vector<long>` containing the number of events successfully generated by each thread. If any events fail to generate, the entries will sum to a number that is smaller than the requested number of events.

bool PythiaParallel::init()
bool PythiaParallel::init( function<void(Pythia*)> customInit)
initialize each `Pythia` instance and returns whether successful.
`argument` customInit : If specified, this function will be called for each `Pythia` instance after it has been constructed and its settings have been set, but before calling `Pythia::init`. This can be useful for example for setting a `UserHooks` object on each instance.

void PythiaParallel::foreach(function<void(Pythia*)> action)
perform the specified action for each `Pythia` instance. This can be useful for doing custom finalization on each instance, e.g. combining histograms.

void PythiaParallel::foreachAsync( function<void(Pythia*)> action)
as `PythiaParallel::foreach`, but the actions are performed for all `Pythia` instances in parallel.

double PythiaParallel::weightSum() const
returns the sum of weights from all `Pythia` instances, as given by `Info::weightSum()`.

double PythiaParallel::sigmaGen() const
returns the weighted average of the generated cross section for each `Pythia` instances, as given by `Info::sigmaGen()`.

The following settings are available for the parallelism framework.

`mode` Parallelism:numThreads (`default = 0`; `minimum = 0`)
Number of threads to run in parallel. If set to 0, the number of threads will be estimated using `std::thread::hardware_concurrency`; if the program is unable to determine the number of threads this way, initialization will fail.

`mvec` Parallelism:seeds (`default = {}`)
The seeds to use for each Pythia object. If empty, `Random:seed` will be used, incrementing the seed by 1 for each object. If non-empty, it must have a number of entries equal to `Parallelism:numThreads`.

`flag` Parallelism:processAsync (`default = off`)
By default, `PythiaParallel::run` will generate events in parallel, which are then processed serially. The advantage of this serial kind of processing is that it prevents race conditions, which can occur for example if two threads are trying to simultaneously write to the same histogram. Normally, event generation is far more time-consuming than analysis, and the time loss from processing in serial is completely negligible. However, there are situations where the analysis takes a non-negligible amount of time compared to event generation. In such scenarios, the user may enable parallel processing of the analysis by setting `Parallelism:processAsync = on`. In this case, the user is responsible for preventing race conditions, possibly by using `mutex` and `lock_guard` objects. An example of how this can be done is shown in `main163.cc`.

`flag` Parallelism:doNext (`default = on`)
By default, `PythiaParallel` generates events, then passes the `Pythia` object to the callback function. If this flag is turned off, the event is not automatically generated before calling the callback, so that the user is responsible for calling `Pythia::next`. This can be useful for example if you want to change the energy on an event-by-event basis, or otherwise want to do checks at the beginning of the event loop before performing the actual generation. If this flag is off, you must also set `Parallelism:processAsync = on`. If it is off,

`mode` Parallelism:index (`default = -1`)
When a `Pythia` instance is passed to the callback function in `PythiaParallel::run`, this setting will contain an index that is unique to each instance, starting at 0 for the first instance. This index is particularly useful if `Parallelism:processAsync = on`. For example if each instance writes to different histograms, this index can be used to specify which histogram to write to. Note that the user should not write to this setting directly.

`flag` Parallelism:balanceLoad (`default = on`)
By default, the event generation is divided evenly between the `Pythia` instances. This way, each instance will generate the same number of events each run with their respective seeds, so that the final statistics will be exactly the same between runs (except events might be processed in a different order). By turning this flag off, instead each `Pythia` instance will generate events until the desired number has been generated. Thus, different runs might result in slightly different statistics, even with the exact same input settings. The advantage of this is that it can be significantly more efficient if the event generation time can vary significantly (e.g. as it does in central vs. peripheral heavy ion collisions).