CLAWS - Work-stealing scheduler for LispWorks
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README.md

claws

CLAWS - Work-stealing scheduler for LispWorks

CLAWS is a simple work-stealing scheduler for LispWorks, to support fork/join computations for parallel programming. It is inspired by other work-stealing schedulers, such as those of Cilk and Threading Building Blocks, and provides a simple parallel programming model that works well in many cases. Through work stealing, it automatically balances the computational load over several worker threads, which ensures good performance even for irregular computations that are hard to schedule statically.

The API consists of two essential functions: 'fork and 'sync.

Fork takes a thunk (a function without parameters) and places it on a thread-local queue. It will subsequently be either stolen by some worker thread and executed in parallel, or else eventually be executed by the current thread.

Return values from such thunks are ignored. If you need to return results from parallel computations, the forked thunks need to close over some variables in the lexical variables and store their results there, or use global variables to communicate results.

Sync is used for synchronization. It ensures that previously spawned thunks have finished their execution, so their results, if any, can be safely used.

To make it easier to deal with results from parallel computations, there is also a convenience macro 'spawn. Spawn takes a list of variable names and a body of code. It converts the code body into a thunk that sets the given variable names to the result(s) of executing that code body. (For multiple variables, the code body must return the results as multiple values.)

Another convenience macro 'parlet can be used a binding form for parallel computations. It also deals with potential multiple return values, and 'seqlet is provided as an equivalent sequential binding form that can be used interchangeably. See the Fibonacci example that illustrates the use of 'parlet and 'seqlet.

Finally, 'reset-workers is a function that tells the runtime how many threads the work-stealing scheduler should use to execute parallel computations. By default, only one thread is assigned, which means that you get (very inefficient) single-threaded, sequential execution. To use more than one thread, 'reset-workers needs to be called with the total number of threads, including the current main thread.

Reset-workers must not be called while a parallel computation is currently being executed, so it's best to call it once at the beginning of an application. However, it's safe to call 'reset-workers while the task queues are all empty. It will kill all current helper threads, and then create new ones according to the requested total number of threads.

Helper threads will continuously look for work to steal from other threads. However, if they don't find any work, they will pause in order not to keep threads busy while other applications may have better uses for them. However, during parallel computations, expect that most to all worker threads will be busy, so be careful with picking the right number of worker threads, especially for desktop applications.

When using CLAWS to map a function over several elements in parallel, it's best to use a tree-recursive form rather than forking/spawning the function for each single element. Here is a rough sketch how to express such tree-recursive functions:

(defun parallel-for-each (function vector &optional (threshold 10))
  (labels ((recur (start end &aux (length (- end start)))
             (if (< length threshold)
               (loop for i from start below end
                     do (funcall function (aref vector i)))
               (let* ((half (floor length 2))
                      (mid (+ start half)))
                 (spawn () (recur start mid))
                 (recur mid end)
                 (sync)))))
    (recur 0 (length vector))))

It's also a good idea to find a threshold below which an algorithm switches to sequential execution, since scheduling incurs an overhead, and tasks need to have a minimum execution cost before it pays off to schedule them to parallel threads.