smqsched

Simple Multi Queue Scheduler

smqsched is a simple multi-threaded task scheduling module with support for multiple queues and directed acyclic graph (DAG) dependencies.

Introduction

smqsched was designed with the following objectives in mind:

Allow existing code to be parallelized without significant refactoring

To this end, smqsched uses promise-style creation of dependency graph, where values queued to be computed in the future can be used as input to newly created tasks in order to signify a dependency on the task producing those values.

For example, the following code:

A = [numpy.random.random((100,100)) for i in range(100)]
B = [None] * 100
C = [None] * (100 ** 2)
for i in range(100):
  B[i] = numpy.linalg.inv(A[i])
    
for i in range(100):
  for k in range(100):
    C[i * 100 + k] = B[i] * B[k]

can be readily parallelized like this:

sch = Scheduler()
Q = Queue(sch)
A = [None] * 100
B = [None] * 100
C = [None] * (100 ** 2)

for i in range(100):
  A[i] = Q.submit(numpy.random.random, (100, 100))
  B[i] = Q.submit(numpy.linalg.inv, A[i])
  
for i in range(100):
  for k in range(100):
    C[i * 100 + k] = Q.submit(lambda a, b: a * b, B[i], B[k])
    
sch.start()
sch.wait()

C = map(lambda a: a.result(), C) # get results

Plan execution in order to get (partial) final results as soon as possible

smqsched is driving execution in such a way that the output of most recently queued tasks (C array in the example above) are delivered as soon as possible. Thus, the goal of smqsched is not simply to run as many tasks in parallel as possible but choose tasks in such a way that the final results start coming in as soon as possible.

For illustration purposes let’s assume that the queue can only run one task at a time. In the example above it would translate to:
– C[9999] asking for execution of B[99]
– B[99] asking for execution of A[99]
– A[99] being executed
– B[99] being executed
– C[9999] being executed
– C[9998] asking for execution of B[98]
– B[98] asking for execution of A[98]
– A[98] being executed
– B[98] being executed
– C[9998] being executed
– and so on…

Why this choice is so important will become clear in the continuation of this document.

Support multiple queues capable of running different numbers of tasks in parallel

A typical use case of smqsched is a situation where the acquisition of the data needs to happen before the processing and use of an instrument/peripheral is required to obtain the data. In such case typically a Queue of size 1 is used for acquisition purposes and Queue of size n_cpus or n_cpus-1 is used for computation. Let’s say that the acquired data comes in two flavors – even and odd and the computation happens for all pairings of even/odd data. To begin obtaining outputs as soon as possible it is necessary to acquire data in interleaved even/odd order. Thanks to smqsched this fact doesn’t have to be stated explicitly, the framework will ensure it happens automatically, at the same time allowing for only one acquisition task at a time, while keeping the computation tasks running in parallel. This is also the reason for the previous point of planning execution in such an order that the final output starts coming in as soon as possible. It can be restated in other words – since smqsched runs multiple queues it tries to keep them all full at all times and prioritizes delivering data to most recently added tasks in case of congestion.

Support progressive task output

smqsched accounts for the fact that a single task might be successively producing a number of results which can be individually useful for dependant tasks. In other words – there might be tasks depending only on a part of the results of another task. This observation is very important for keeping the queues full as much as possible. Therefore smqsched supports generators as tasks. The following example illustrates the situation described above:

def _find_minima(x):
  yield False
  for i in range(1, len(x) - 1):  
    if x[i] < x[i - 1] and x[i] < x[i + 1]:
      yield True
    else:
      yield False
  yield False
      
_find_minima.number_of_results = lambda x: len(x)

def _print_minimum(i, is_minimum):
  if is_minimum:
    print('Found minimum at', i)
    
x = np.random.random(100000)

is_minimum = Q.submit(_find_minima, x)
for i in range(len(x)):
  Q.submit(_print_minimum, i, is_minimum[i])

When executing this code the minima will start getting printed before they are all found.

Support for stopping/resuming the scheduler and adding new jobs at any time

smqsched allows for the Scheduler to be stopped and resumed at any time. Already running tasks cannot be interrupted unless a suitable user-implemented condition is placed in their code however new tasks won’t be started while the scheduler is stopped. Resuming the scheduler will pick up the work without losing any data or the need to re-run any of the already completed tasks. This functionality is of particular importance if tasks have side-effects, particularly on the I/O side, for example temporary files which might be useful for inspection by the developer. New jobs can be added to the queues while the scheduler is already running. This allows to use **smqsched** in a more interactive manner and new dependencies will be taken into account as soon as the next scheduling cycle occurs.

Final Thoughts

smqsched can be used in multiple situations involving data acquisition, network communication as well as pure computation. It currently supports thread-based parallelism but can be easily adapted to support process-based or cluster-based setups.

License

smqsched is licensed under 2-clause “Simplified” BSD License.

Links

smqsched is available for download from: https://github.com/sadaszewski/smqsched

smqsched – Simple Multi Queue Scheduler