teca_cpu_thread_pool< task_t, data_t > Class Template Reference

A class to manage a fixed size pool of threads that dispatch work. More...

#include <teca_cpu_thread_pool.h>

Public Member Functions
	teca_cpu_thread_pool (MPI_Comm comm, int n, bool bind, bool verbose)
	teca_cpu_thread_pool (const teca_cpu_thread_pool &src)=delete
	teca_cpu_thread_pool (teca_cpu_thread_pool &&src)=delete
teca_cpu_thread_pool &	operator= (const teca_cpu_thread_pool &src)=delete
teca_cpu_thread_pool &	operator= (teca_cpu_thread_pool &&src)=delete
void	push_task (task_t &task)
template<template< typename ... > class container_t, typename ... args>
void	wait_all (container_t< data_t, args ... > &data)
template<template< typename ... > class container_t, typename ... args>
int	wait_some (long n_to_wait, long long poll_interval, container_t< data_t, args ... > &data)
unsigned int	size () const noexcept
	get the number of threads More...

Detailed Description

template<typename task_t, typename data_t>
class teca_cpu_thread_pool< task_t, data_t >

A class to manage a fixed size pool of threads that dispatch work.

Constructor & Destructor Documentation

teca_cpu_thread_pool()

template<typename task_t , typename data_t >

teca_cpu_thread_pool< task_t, data_t >::teca_cpu_thread_pool	(	MPI_Comm	comm,
		int	n,
		bool	bind,
		bool	verbose
	)

construct/destruct the thread pool. arguments:

Parameters

[in]	comm	communicator over which to map threads. Use MPI_COMM_SELF for local mapping.
[in]	n	number of threads to create for the pool. -1 will create 1 thread per physical CPU core. all MPI ranks running on the same node are taken into account, resulting in 1 thread per core node wide.
[in]	bind	bind each thread to a specific core.
[in]	verbose	print a report of the thread to core bindings

Member Function Documentation

push_task()

template<typename task_t , typename data_t >

void teca_cpu_thread_pool< task_t, data_t >::push_task

(

task_t &

task

)

add a data request task to the queue, returns a future from which the generated dataset can be accessed.

size()

template<typename task_t , typename data_t >

unsigned int teca_cpu_thread_pool< task_t, data_t >::size ( ) const

inlinenoexcept

get the number of threads

wait_all()

template<typename task_t , typename data_t >

template<template< typename ... > class container_t, typename ... args>

void teca_cpu_thread_pool< task_t, data_t >::wait_all

(

container_t< data_t, args ... > &

data

)

wait for all of the requests to execute and transfer datasets in the order that corresponding requests were added to the queue.

wait_some()

template<typename task_t , typename data_t >

template<template< typename ... > class container_t, typename ... args>

int teca_cpu_thread_pool< task_t, data_t >::wait_some	(	long	n_to_wait,
		long long	poll_interval,
		container_t< data_t, args ... > &	data
	)

wait for some of the requests to execute. datasets will be returned as they become ready. n_to_wait specifies how many datasets to gather but there are three cases when the number of datasets returned differs from n_to_wait. when n_to_wait is larger than the number of tasks remaining, datasets from all of the remaining tasks is returned. when n_to_wait is smaller than the number of datasets ready, all of the currenttly ready data are returned. finally, when n_to_wait is < 1 the call blocks until all of the tasks complete and all of the data is returned.

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The documentation for this class was generated from the following file:

• develop/core/teca_cpu_thread_pool.h