Sage-Code Laboratory

Bee Concurrence

In Bee we have special statements to start a process asynchronously and run in parallel in separate threads. We can synchronieze the processes after execution.

Multi threading

Bee has capability to run in parallel multiple instances of a rule. For this we need to start a rule asynchronously and run other rules or the same rule in a parallel thread. This is called multi-threading application. For this we introduce new kwyeords:

begin call a rule asynchronously and create a new thread
wait suspend a thread for specific number of seconds
yield suspend current thread and give priority to other threads

Example 1:

Asynchronous call can be done using a control cycle and keyword "begin". This is significant because a rule need parameters. By using keyword "begin" we initiate one rule "test" 4 times but this rule does absolutely nothing, just wait about 30 seconds.

** suspend n seconds
rule test:
    wait 10 + i*5;

** prepare for execution 4 threads
rule main:
    for ∀ i ∈ (1..4) do
       begin test(i);
    wait; -- wait for other threads to finish;

Example 2:

A big problem in asynchronous call is to capture the results of a rule that run in parallel mode. In the next example we use map-reduce design pattern to create sum of numbers in parallel. The results are captured into a list. Then we reduce the results by making sum of sums and create the output.

bee rule

Map-Reduce Pattern

** suspend n seconds
rule sum(a, b ∈ Z) => (r ∈ Z):
    new i := 0
  for ∀ i ∈ (a..b) do
    let r += i;

** prepare for execution on 4 threads
rule main:
    new results <: List; -- partial sum collection
    ** use a mapping technigue to split data in 4 equal batches
        new i ∈ N;
    for ∀ i ∈ (1.!100:25) do
        begin test(i,i+25) +> results;
    wait; -- wait for other threads to finish;
    new output ∈ Z;
    ** reduce results into output
    for ∀ partial ∈ results do
       let output += partial;
    print output; -- 5050
    expect output = 5050;

Note: There is a smarter way of doing this sum using a formula: (1+100)*50 = 5050. But of course we pretend we don't know this and do it in the hard way. The point is we have done it in parallel on a multithread application.

Bee Coroutines

Bee is designed for high performance computing using multi-core processors. There is a second resource for concurency: high performance video cards or add-in cards that enable parallel processing. After we finish the design, we will research hardware sollutions to optimize de compiler.


Coroutines are rules that can be suspended with all states ready then resumed on demand multiple times. Each time we resume a coroutine this can produce one result that we capture. So a coroutine can produce multiple results until finishes.


Coroutines can be used as a branch of main thread. This is not yet running in parallel but is an example for how a rule can play as a coroutine.

rule test(n ∈ N) => (result ∈ N):
  ** can generate n numbers
    new i ∈ N;
  for ∀ i ∈ (1..n) do
    let result := i;
    ** suspend execution
    yield; -- wait for the main thread
  result = 0; -- finalization signal

rule main:
    ** start secondary process
    begin test(9)     -- initialize coroutine
      new  r ∈ N;  -- result reference
      yield r <- test;  -- capture next result
      write (r, ",");
    repeat if r > 0;

    print;  -- 0,1,2,3,4,5,6,7,8,9,

Note:&In the example above, the program is still working in serial mode, except that has a branch running on a separated thread. When the coroutine function the main thread is on hold, and when the main thread function the coroutine is waiting.

Producer consumer

Producer consumer patern is using coroutines that can work in palallel in asynchronous mode. That means we start coroutines and do not wait to finish but let the main thread to continue. A producer is preparing the work and the consumer is doing the work.

If you do not know how this works, take a look to the diagram below. We have one rule called "producer" one list that act as queue and a routine called "consumer". The queue has a limited capacity. We can use same consumer twice, if we do then the application is also multi-threading.


Producer Consumer



Info: This example is complex so we have chosen to keep it on GitHub with the Bee project. If you want to visualize it you need Notepad++ and color syntax highlighter. Go back to index page and follow the instructions at the bottom of the page.

Read more: Graphics