Background
Before describing how to accumulate events in AMI, it is important to understand the architecture of AMI, which consists of workers, local collectors, a global collector, a manager, and a client processes that run on different compute nodes.
The client assembles a graph which is pushed to the manager that then distributes it to the workers and collectors for execution. Nodes in the graph are "colored" worker, localCollector, and globalCollector. Each type of process is responsible for executing a subgraph of nodes of the appropriate color (ie workers execute nodes with the color worker, ...). Events in AMI are distributed to workers by Psana. AMI provides different types of graph nodes for accumulating events across this distributed architecture. Nodes which accumulate events are called global operations and GUI graph nodes are expanded into 3 underlying graph nodes.
PickN
PickN accumulates N events into a list. The GUI node is expanded into 3 underlying PickN graph nodes each with a color of worker, localCollector, and globalCollector. The underlying graph nodes have a different N.
Workers collect: N // num_workers
localCollectors collect: N // num_local_collectors
globalCollector collects: (N // num_workers) * num_workers
In the above example there are 4 workers and 2 localCollectors, if we wanted to collect N = ~25 events then each worker would collect 25 // 4 = 6 events, each localCollector would collect 12 "events" from the workers (ie 2 lists of length 6 each), and the globalCollector would collect (25 // 4) * 4 = 24 "events" from the localCollectors (ie 2 lists of length 12 each).
The PickN will only update when N events have been collected. There is no guarantee on the order in which events are collected.
RollingBuffer
RollingBuffer works similarly to the PickN, only it accumulates to a circular buffer, which shifts left after N elements have been accumulated. Unlike the PickN which only returns when N events have collected, the RollingBuffer returns after event.
For example assume N = 10 and we have 2 workers and 2 localCollectors. The workers and local collectors will both collect 5 events each and the global collector will collect 10 events. Once 10 events have been collected all elements in the list will shift left and the latest event will appear in the buffer at index 9.
worker1 buffer: [1, 2, 3, 4, 5]
worker2 buffer: [6, 7, 8, 9, 10]
localCollector1 buffer: [1, 2, 3, 4, 5]
localCollector2 buffer: [6, 7, 8, 9, 10]
globalCollector: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
worker1 receives NEW EVENT 11
worker1 buffer: [2, 3, 4, 5, 11]
worker2 buffer: [5, 6, 7, 8, 9, 10]
localCollector1 buffer: [2, 3, 4, 5, 11]
localCollector2 buffer: [5, 6, 7, 8, 9, 10]
globalCollector: [6, 7, 8, 9, 10, 2, 3, 4, 5, 11]
Accumulator
An accumulator takes two functions as an argument, a reduction and a res_factory that is called on reset. It accumulates into self.res on each event in the following way: self.res = reduction(self.res, *args) at the end of a heartbeat workers and local collectors reset self.res by calling self.res_factory.
The accumulator is independent of the number of workers and local collectors, but consider the following two worker, two local collector example.
from ami.graphkit_wrapper import Graph
import ami.graph_nodes as gn
def make_graph():
def reduction(res, *rest):
print(res, rest)
return res+rest[0]
def res_factory():
return 0
graph = Graph(name='graph')
graph.add(gn.Accumulator(name='accumulated', inputs=['delta_t'], outputs=['accumulated_delta_t'],
reduction=reduction, res_factor=res_factory))
graph.compile(num_workers=2, num_local_collectors=2)
return graph
graph = make_graph()
graph({'delta_t': 1}, color='worker')
# 0 (1,)
graph({'delta_t': 2}, color='worker')
# 1 (2, )
graph({'delta_t': 3}, color='worker')
# 3 (3, )
graph({'delta_t': 4}, color='worker')
# 6 (4, )
worker1 = graph({'delta_t': 5}, color='worker')
# 10 (5, )
print("worker1 done")
graph2 = make_graph()
for i in range(6, 11):
worker2 = graph2({'delta_t': i}, color='worker')
print("worker2 done")
graph3 = make_graph()
localCollector1 = graph3(worker1, color='localCollector')
# 0 (15, )
print("localCollector1 done")
graph4 = make_graph()
localCollector2 = graph4(worker2, color='localCollector')
# 0 (40, )
print("localCollector2 done")
graph5 = make_graph()
globalCollector = graph5(localCollector1, color='globalCollector')
# 0 (15, )
print("globalCollector")
globalCollector = graph5(localCollector2, color='globalCollector')
# 15 (40, )
print("globalCollector")
print(globalCollector)
# {'accumulated_delta_t': 55}
More complicated examples look at the to_operation method of the Binning2D and Average1D in the Numpy section of AMI:
https://github.com/slac-lcls/ami/blob/master/ami/flowchart/library/Numpy.py#L118-L149
https://github.com/slac-lcls/ami/blob/master/ami/flowchart/library/Numpy.py#L506-L528
ReduceByKey
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