Julia Channel HOT!
Let's define a producer task, which produces values via the put! call. To consume values, we need to schedule the producer to run in a new task. A special Channel constructor which accepts a 1-arg function as an argument can be used to run a task bound to a channel. We can then take! values repeatedly from the channel object:
Note that we did not have to explicitly close the channel in the producer. This is because the act of binding a Channel to a Task associates the open lifetime of a channel with that of the bound task. The channel object is closed automatically when the task terminates. Multiple channels can be bound to a task, and vice-versa.
While the Task constructor expects a 0-argument function, the Channel method that creates a task-bound channel expects a function that accepts a single argument of type Channel. A common pattern is for the producer to be parameterized, in which case a partial function application is needed to create a 0 or 1 argument anonymous function.
To orchestrate more advanced work distribution patterns, bind and schedule can be used in conjunction with Task and Channel constructors to explicitly link a set of channels with a set of producer/consumer tasks.
Channels are created via the ChannelT(sz) constructor. The channel will only hold objects of type T. If the type is not specified, the channel can hold objects of any type. sz refers to the maximum number of elements that can be held in the channel at any time. For example, Channel(32) creates a channel that can hold a maximum of 32 objects of any type. A ChannelMyType(64) can hold up to 64 objects of MyType at any time.
Consider a simple example using channels for inter-task communication. We start 4 tasks to process data from a single jobs channel. Jobs, identified by an id (job_id), are written to the channel. Each task in this simulation reads a job_id, waits for a random amount of time and writes back a tuple of job_id and the simulated time to the results channel. Finally all the results are printed out.
yieldto is powerful, but most uses of tasks do not invoke it directly. Consider why this might be. If you switch away from the current task, you will probably want to switch back to it at some point, but knowing when to switch back, and knowing which task has the responsibility of switching back, can require considerable coordination. For example, put! and take! are blocking operations, which, when used in the context of channels maintain state to remember who the consumers are. Not needing to manually keep track of the consuming task is what makes put! easier to use than the low-level yieldto.
When a channel is bound to multiple tasks, the first task to terminate will close the channel. When multiple channels are bound to the same task, termination of the task will close all of the bound channels.
Channels are a bit similar to coroutines and fibers (or any "light threads") with a FIFO queue so to manage messages. Such a construct introduces a significant overhead due to expensive software-defined context-switches (aka yield that mainly consists in saving/restoring some registers). The negative effect on performance can be delayed. Indeed, light threading systems have their own stack and they own code context. Thus, when the processor do a light-thread context switch, this can cause data/code cache-misses. For more information about how channels you can read the documentation about it (which mention an embedded task scheduler) or directly read the code.
In addition, channels create objects/message than needs to be managed by the garbage collector putting even more pressure on it. In fact, the number of allocation is >3 times bigger in the channel based version. One can argue that the reported GC overhead is low but such metrics often underestimate the overall overhead which include allocations, memory diffusion/fragmentation, GC collections, cache-effects, etc. (and, in this case, even I/O overlapping effects).
I think the main problem with the channel-based implementation is that the channel of your code are unbuffered (see the documentation about it). Using wide buffers can help to significantly reduce the number of context-switches and so the overhead. This may increase the latency but there is often a trade-off to make between latency and throughput (especially in scheduling). Alternatively, note that there are some packages that can be faster than built-ins channels.
Then create your task implicitly using the Channel constructor (which takes a function with a single argument only representing the channel, so we need to wrap the source function around an anonymous function):
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