pipeline_process.py revision f2a3ef46f75d2196a93d3ed27f4d1fcf22b54fbe 
1# Copyright (c) 2013 The Chromium OS Authors. All rights reserved.
2# Use of this source code is governed by a BSD-style license that can be
3# found in the LICENSE file.
4"""Pipeline process that encapsulates the actual content.
5
6Part of the Chrome build flags optimization.
7
8The actual stages include the builder and the executor.
9"""
10
11__author__ = 'yuhenglong@google.com (Yuheng Long)'
12
13import multiprocessing
14
15# Pick an integer at random.
16POISONPILL = 975
17
18
19class PipelineProcess(multiprocessing.Process):
20  """A process that encapsulates the actual content pipeline stage.
21
22  The actual pipeline stage can be the builder or the tester.  This process
23  continuously pull tasks from the queue until a poison pill is received.
24  Once a job is received, it will hand it to the actual stage for processing.
25
26  Each pipeline stage contains three modules.
27  The first module continuously pulls task from the input queue. It searches the
28  cache to check whether the task has encountered before. If so, duplicate
29  computation can be avoided.
30  The second module consists of a pool of workers that do the actual work, e.g.,
31  the worker will compile the source code and get the image in the builder
32  pipeline stage.
33  The third module is a helper that put the result cost to the cost field of the
34  duplicate tasks. For example, if two tasks are equivalent, only one task, say
35  t1 will be executed and the other task, say t2 will not be executed. The third
36  mode gets the result from t1, when it is available and set the cost of t2 to
37  be the same as that of t1.
38  """
39
40  def __init__(self, num_processes, name, cache, stage, task_queue, helper,
41               worker, result_queue):
42    """Set up input/output queue and the actual method to be called.
43
44    Args:
45      num_processes: Number of helpers subprocessors this stage has.
46      name: The name of this stage.
47      cache: The computed tasks encountered before.
48      stage: An int value that specifies the stage for this pipeline stage, for
49        example, build stage or test stage. This value will be used to retrieve
50        the keys in different stage. I.e., the flags set is the key in build
51        stage and the checksum is the key in the test stage. The key is used to
52        detect duplicates.
53      task_queue: The input task queue for this pipeline stage.
54      helper: The method hosted by the helper module to fill up the cost of the
55        duplicate tasks.
56      worker: The method hosted by the worker pools to do the actual work, e.g.,
57        compile the image.
58      result_queue: The output task queue for this pipeline stage.
59    """
60
61    multiprocessing.Process.__init__(self)
62
63    self._name = name
64    self._task_queue = task_queue
65    self._result_queue = result_queue
66
67    self._helper = helper
68    self._worker = worker
69
70    self._cache = cache
71    self._stage = stage
72    self._num_processes = num_processes
73
74    # the queues used by the modules for communication
75    manager = multiprocessing.Manager()
76    self._helper_queue = manager.Queue()
77    self._work_queue = manager.Queue()
78
79  def run(self):
80    """Busy pulling the next task from the queue for execution.
81
82    Once a job is pulled, this stage invokes the actual stage method and submits
83    the result to the next pipeline stage.
84
85    The process will terminate on receiving the poison pill from previous stage.
86    """
87
88    # the worker pool
89    work_pool = multiprocessing.Pool(self._num_processes)
90
91    # the helper process
92    helper_process = multiprocessing.Process(
93        target=self._helper,
94        args=(self._stage, self._cache, self._helper_queue, self._work_queue,
95              self._result_queue))
96    helper_process.start()
97    mycache = self._cache.keys()
98
99    while True:
100      task = self._task_queue.get()
101      if task == POISONPILL:
102        # Poison pill means shutdown
103        self._result_queue.put(POISONPILL)
104        break
105
106      task_key = task.GetIdentifier(self._stage)
107      if task_key in mycache:
108        # The task has been encountered before. It will be sent to the helper
109        # module for further processing.
110        self._helper_queue.put(task)
111      else:
112        # Let the workers do the actual work.
113        work_pool.apply_async(
114            self._worker,
115            args=(self._stage, task, self._work_queue, self._result_queue))
116        mycache.append(task_key)
117
118    # Shutdown the workers pool and the helper process.
119    work_pool.close()
120    work_pool.join()
121
122    self._helper_queue.put(POISONPILL)
123    helper_process.join()
124