The Common Lisp Cookbook – Threads

Introduction

By threads, we mean separate execution strands within a single Lisp process, sharing the same address space. Typically, execution is automatically switched between these strands by the system (either by the lisp kernel or by the operating system) so that tasks appear to be completed in parallel (asynchronously). This page discusses the creation and management of threads and some aspects of interactions between them. For information about the interaction between lisp and other processes, see Interfacing with your OS.

An instant pitfall for the unwary is that most implementations refer (in nomenclature) to threads as processes - this is a historical feature of a language which has been around for much longer than the term thread. Call this maturity a sign of stable implementations, if you will.

The ANSI Common Lisp standard doesn’t mention this topic. We will present here the portable bordeaux-threads library, SBCL threads and the lparallel library.

Why bother?

The first question to resolve is: why bother with threads? Sometimes your answer will simply be that your application is so straightforward that you need not concern yourself with threads at all. But in many other cases it’s difficult to imagine how a sophisticated application can be written without multi-threading. For example:

  • you might be writing a server which needs to be able to respond to more than one user / connection at a time (for instance: a web server - see this example on the Sockets page);
  • you might want to perform some background activity, without halting the main application while this is going on;
  • you might want your application to be notified when a certain time has elapsed;
  • you might want to keep the application running and active while waiting for some system resource to become available;
  • you might need to interface with some other system which requires multithreading (for example, “windows” under Windows which generally run in their own threads);
  • you might want to associate different contexts (e.g. different dynamic bindings) with different parts of the application;
  • you might even have the simple need to do two things at once.

What is Concurrency? What is Parallelism?

Credit: The following was first written on z0ltan.wordpress.com by Timmy Jose.

Concurrency is a way of running different, possibly related, tasks seemingly simultaneously. What this means is that even on a single processor machine, you can simulate simultaneity using threads (for instance) and context-switching them.

In the case of system (native OS) threads, the scheduling and context switching is ultimately determined by the OS. This is the case with Java threads and Common Lisp threads.

In the case of “green” threads, that is to say threads that are completely managed by the program, the scheduling can be completely controlled by the program itself. Erlang is a great example of this approach.

So what is the difference between Concurrency and Parallelism? Parallelism is usually defined in a very strict sense to mean independent tasks being run in parallel, simultaneously, on different processors or on different cores. In this narrow sense, you really cannot have parallelism on a single-core, single-processor machine.

It rather helps to differentiate between these two related concepts on a more abstract level – concurrency primarily deals with providing the illusion of simultaneity to clients so that the system doesn’t appear locked when a long running operation is underway. GUI systems are a wonderful example of this kind of system. Concurrency is therefore concerned with providing good user experience and not necessarily concerned with performance benefits.

Java’s Swing toolkit and JavaScript are both single-threaded, and yet they can give the appearance of simultaneity because of the context switching behind the scenes. Of course, concurrency is implemented using multiple threads/processes in most cases.

Parallelism, on the other hand, is mostly concerned with pure performance gains. For instance, if we are given a task to find the squares of all the even numbers in a given range, we could divide the range into chunks which are then run in parallel on different cores or different processors, and then the results can be collated together to form the final result. This is an example of Map-Reduce in action.

So now that we have separated the abstract meaning of Concurrency from that of Parallelism, we can talk a bit about the actual mechanism used to implement them. This is where most of the confusion arise for a lot of people. They tend to tie down abstract concepts with specific means of implementing them. In essence, both abstract concepts may be implemented using the same mechanisms! For instance, we may implement concurrent features and parallel features using the same basic thread mechanism in Java. It’s only the conceptual intertwining or independence of tasks at an abstract level that makes the difference for us.

For instance, if we have a task where part of the work can be done on a different thread (possibly on a different core/processor), but the thread which spawns this thread is logically dependent on the results of the spawned thread (and as such has to “join” on that thread), it is still Concurrency!

So the bottomline is this – Concurrency and Parallelism are different concepts, but their implementations may be done using the same mechanisms — threads, processes, etc.

Bordeaux threads

The Bordeaux library provides a platform independent way to handle basic threading on multiple Common Lisp implementations. The interesting bit is that it itself does not really create any native threads — it relies entirely on the underlying implementation to do so.

On there other hand, it does provide some useful extra features in its own abstractions over the lower-level threads.

Also, you can see from the demo programs that a lot of the Bordeaux functions seem quite similar to those used in SBCL. I don’t really think that this is a coincidence.

You can refer to the documentation for more details (check the “Wrap-up” section).

Installing Bordeaux Threads

First let’s load up the Bordeaux library using Quicklisp:

CL-USER> (ql:quickload :bt-semaphore)
To load "bt-semaphore":
  Load 1 ASDF system:
    bt-semaphore
; Loading "bt-semaphore"

(:BT-SEMAPHORE)

Checking for thread support in Common Lisp

Regardless of the Common Lisp implementation, there is a standard way to check for thread support availability:

CL-USER> (member :thread-support *FEATURES*)
(:THREAD-SUPPORT :SWANK :QUICKLISP :ASDF-PACKAGE-SYSTEM :ASDF3.1 :ASDF3 :ASDF2
 :ASDF :OS-MACOSX :OS-UNIX :NON-BASE-CHARS-EXIST-P :ASDF-UNICODE :64-BIT
 :64-BIT-REGISTERS :ALIEN-CALLBACKS :ANSI-CL :ASH-RIGHT-VOPS :BSD
 :C-STACK-IS-CONTROL-STACK :COMMON-LISP :COMPARE-AND-SWAP-VOPS
 :COMPLEX-FLOAT-VOPS :CYCLE-COUNTER :DARWIN :DARWIN9-OR-BETTER :FLOAT-EQL-VOPS
 :FP-AND-PC-STANDARD-SAVE :GENCGC :IEEE-FLOATING-POINT :INLINE-CONSTANTS
 :INODE64 :INTEGER-EQL-VOP :LINKAGE-TABLE :LITTLE-ENDIAN
 :MACH-EXCEPTION-HANDLER :MACH-O :MEMORY-BARRIER-VOPS :MULTIPLY-HIGH-VOPS
 :OS-PROVIDES-BLKSIZE-T :OS-PROVIDES-DLADDR :OS-PROVIDES-DLOPEN
 :OS-PROVIDES-PUTWC :OS-PROVIDES-SUSECONDS-T :PACKAGE-LOCAL-NICKNAMES
 :PRECISE-ARG-COUNT-ERROR :RAW-INSTANCE-INIT-VOPS :SB-DOC :SB-EVAL :SB-LDB
 :SB-PACKAGE-LOCKS :SB-SIMD-PACK :SB-SOURCE-LOCATIONS :SB-TEST :SB-THREAD
 :SB-UNICODE :SBCL :STACK-ALLOCATABLE-CLOSURES :STACK-ALLOCATABLE-FIXED-OBJECTS
 :STACK-ALLOCATABLE-LISTS :STACK-ALLOCATABLE-VECTORS
 :STACK-GROWS-DOWNWARD-NOT-UPWARD :SYMBOL-INFO-VOPS :UD2-BREAKPOINTS :UNIX
 :UNWIND-TO-FRAME-AND-CALL-VOP :X86-64)

If there were no thread support, it would show “NIL” as the value of the expression.

Depending on the specific library being used, we may also have different ways of checking for concurrency support, which may be used instead of the common check mentioned above.

For instance, in our case, we are interested in using the Bordeaux library. To check whether there is support for threads using this library, we can see whether the supports-threads-p global variable is set to NIL (no support) or T (support available):

CL-USER> bt:*supports-threads-p*
T

Okay, now that we’ve got that out of the way, let’s test out both the platform-independent library (Bordeaux) as well as the platform-specific support (SBCL in this case).

To do this, let us work our way through a number of simple examples:

  • Basics — list current thread, list all threads, get thread name
  • Update a global variable from a thread
  • Print a message onto the top-level using a thread
  • Print a message onto the top-level — fixed
  • Print a message onto the top-level — better
  • Modify a shared resource from multiple threads
  • Modify a shared resource from multiple threads — fixed using locks
  • Modify a shared resource from multiple threads — using atomic operations
  • Joining on a thread, destroying a thread example

    Basics — list current thread, list all threads, get thread name

    ;;; Print the current thread, all the threads, and the current thread's name
    (defun print-thread-info ()
      (let* ((curr-thread (bt:current-thread))
             (curr-thread-name (bt:thread-name curr-thread))
             (all-threads (bt:all-threads)))
        (format t "Current thread: ~a~%~%" curr-thread)
        (format t "Current thread name: ~a~%~%" curr-thread-name)
        (format t "All threads:~% ~{~a~%~}~%" all-threads))
      nil)

And the output:

    CL-USER> (print-thread-info)
    Current thread: #<THREAD "repl-thread" RUNNING {10043B8003}>

    Current thread name: repl-thread

    All threads:
     #<THREAD "repl-thread" RUNNING {10043B8003}>
    #<THREAD "auto-flush-thread" RUNNING {10043B7DA3}>
    #<THREAD "swank-indentation-cache-thread" waiting on: #<WAITQUEUE  {1003A28103}> {1003A201A3}>
    #<THREAD "reader-thread" RUNNING {1003A20063}>
    #<THREAD "control-thread" waiting on: #<WAITQUEUE  {1003A19E53}> {1003A18C83}>
    #<THREAD "Swank Sentinel" waiting on: #<WAITQUEUE  {1003790043}> {1003788023}>
    #<THREAD "main thread" RUNNING {1002991CE3}>

    NIL

Update a global variable from a thread:

    (defparameter *counter* 0)

    (defun test-update-global-variable ()
      (bt:make-thread
       (lambda ()
         (sleep 1)
         (incf *counter*)))
      *counter*)

We create a new thread using bt:make-thread, which takes a lambda abstraction as a parameter. Note that this lambda abstraction cannot take any parameters.

Another point to note is that unlike some other languages (Java, for instance), there is no separation from creating the thread object and starting/running it. In this case, as soon as the thread is created, it is executed.

The output:

    CL-USER> (test-update-global-variable)

    0
    CL-USER> *counter*
    1

As we can see, because the main thread returned immediately, the initial value of *counter* is 0, and then around a second later, it gets updated to 1 by the anonymous thread.

    ;;; Print a message onto the top-level using a thread

    (defun print-message-top-level-wrong ()
      (bt:make-thread
       (lambda ()
         (format *standard-output* "Hello from thread!")))
      nil)

And the output:

    CL-USER> (print-message-top-level-wrong)
    NIL

So what went wrong? The problem is variable binding. Now, the ’t’ parameter to the format function refers to the top-level, which is a Common Lisp term for the main console stream, also referred to by the global variable *standard-output*. So we could have expected the output to be shown on the main console screen.

The same code would have run fine if we had not run it in a separate thread. What happens is that each thread has its own stack where the variables are rebound. In this case, even for *standard-output*, which being a global variable, we would assume should be available to all threads, is rebound inside each thread! This is similar to the concept of ThreadLocal storage in Java. Print a message onto the top-level — fixed:

So how do we fix the problem of the previous example? By binding the top-level at the time of thread creation of course. Pure lexical scoping to the rescue!

    ;;; Print a message onto the top-level using a thread — fixed

    (defun print-message-top-level-fixed ()
      (let ((top-level *standard-output*))
        (bt:make-thread
         (lambda ()
           (format top-level "Hello from thread!"))))
      nil)

Which produces:

    CL-USER> (print-message-top-level-fixed)
    Hello from thread!
    NIL

Phew! However, there is another way of producing the same result using a very interesting reader macro as we’ll see next.

Let’s take a look at the code first:

    ;;; Print a message onto the top-level using a thread - reader macro

    (eval-when (:compile-toplevel)
      (defun print-message-top-level-reader-macro ()
        (bt:make-thread
         (lambda ()
           (format #.*standard-output* "Hello from thread!")))
        nil))

    (print-message-top-level-reader-macro)

And the output:

    CL-USER> (print-message-top-level-reader-macro)
    Hello from thread!
    NIL

So it works, but what’s the deal with the eval-when and what is that strange #. symbol before *standard-output*?

eval-when controls when evaluation of Lisp expressions takes place. We can have three targets — :compile-toplevel, :load-toplevel, and :execute.

The #. symbol is what is called a “Reader macro”. A reader (or read) macro is called so because it has special meaning to the Common Lisp Reader, which is the component that is responsible for reading in Common Lisp expressions and making sense out of them. This specific reader macro ensures that the binding of *standard-output* is done at read time.

Binding the value at read-time ensures that the original value of *standard-output* is maintained when the thread is run, and the output is shown on the correct top-level.

Now this is where the eval-when bit comes into play. By wrapping the whole function definition inside the eval-when, and ensuring that evaluation takes place during compile time, the correct value of *standard-output* is bound. If we had skipped the eval-when, we would see the following error:

      error:
        don't know how to dump #<SWANK/GRAY::SLIME-OUTPUT-STREAM {100439EEA3}> (default MAKE-LOAD-FORM method called).
        ==>
          #<SWANK/GRAY::SLIME-OUTPUT-STREAM {100439EEA3}>

      note: The first argument never returns a value.
      note:
        deleting unreachable code
        ==>
          "Hello from thread!"


    Compilation failed.

And that makes sense because SBCL cannot make sense of what this output stream returns since it is a stream and not really a defined value (which is what the ‘format’ function expects). That is why we see the “unreachable code” error.

Note that if the same code had been run on the REPL directly, there would be no problem since the resolution of all the symbols would be done correctly by the REPL thread.

Modify a shared resource from multiple threads

Suppose we have the following setup with a minimal bank-account class (no error checks):

    ;;; Modify a shared resource from multiple threads

    (defclass bank-account ()
      ((id :initarg :id
           :initform (error "id required")
           :accessor :id)
       (name :initarg :name
             :initform (error "name required")
             :accessor :name)
       (balance :initarg :balance
                :initform 0
                :accessor :balance)))

    (defgeneric deposit (account amount)
      (:documentation "Deposit money into the account"))

    (defgeneric withdraw (account amount)
      (:documentation "Withdraw amount from account"))

    (defmethod deposit ((account bank-account) (amount real))
      (incf (:balance account) amount))

    (defmethod withdraw ((account bank-account) (amount real))
      (decf (:balance account) amount))

And we have a simple client which apparently does not believe in any form of synchronisation:

    (defparameter *rich*
      (make-instance 'bank-account
                     :id 1
                     :name "Rich"
                     :balance 0))
    ; compiling (DEFPARAMETER *RICH* ...)

    (defun demo-race-condition ()
      (loop repeat 100
         do
           (bt:make-thread
            (lambda ()
              (loop repeat 10000 do (deposit *rich* 100))
              (loop repeat 10000 do (withdraw *rich* 100))))))

This is all we are doing – create a new bank account instance (balance 0), and then create a 100 threads, each of which simply deposits an amount of 100 10000 times, and then withdraws the same amount the same number of times. So the final result should be the same as that of the opening balance, which is 0, right? Let’s check that and see.

On a sample run, we might get the following results:

    CL-USER> (:balance *rich*)
    0
    CL-USER> (dotimes (i 5)
               (demo-race-condition))
    NIL
    CL-USER> (:balance *rich*)
    22844600

Whoa! The reason for this discrepancy is that incf and decf are not atomic operations — they consist of multiple sub-operations, and the order in which they are executed is not in our control.

This is what is called a “race condition” — multiple threads contending for the same shared resource with at least one modifying thread which, more likely than not, reads the wrong value of the object while modifying it. How do we fix it? One simple way it to use locks (mutex in this case, could be semaphores for more complex situations).

Modify a shared resource from multiple threads — fixed using locks

Let’s rest the balance for the account back to 0 first:

    CL-USER> (setf (:balance *rich*) 0)
    0
    CL-USER> (:balance *rich*)
    0

Now let’s modify the demo-race-condition function to access the shared resource using locks (created using bt:make-lock and used as shown):

    (defvar *lock* (bt:make-lock))
    ; compiling (DEFVAR *LOCK* …)

    (defun demo-race-condition-locks ()
      (loop repeat 100
         do
           (bt:make-thread
            (lambda ()
              (loop repeat 10000 do (bt:with-lock-held (*lock*)
                                      (deposit *rich* 100)))
              (loop repeat 10000 do (bt:with-lock-held (*lock*)
                                      (withdraw *rich* 100)))))))
    ; compiling (DEFUN DEMO-RACE-CONDITION-LOCKS ...)

And let’s do a bigger sample run this time around:

    CL-USER> (dotimes (i 100)
               (demo-race-condition-locks))
    NIL
    CL-USER> (:balance *rich*)
    0

Excellent! Now this is better. Of course, one has to remember that using a mutex like this is bound to affect performance. There is a better way in quite a few circumstances — using atomic operations when possible. We’ll cover that next.

Modify a shared resource from multiple threads — using atomic operations

Atomic operations are operations that are guaranteed by the system to all occur inside a conceptual transaction, i.e., all the sub-operations of the main operation all take place together without any interference from outside. The operation succeeds completely or fails completely. There is no middle ground, and there is no inconsistent state.

Another advantage is that performance is far superior to using locks to protect access to the shared state. We will see this difference in the actual demo run.

The Bordeaux library does not provide any real support for atomics, so we will have to depend on the specific implementation support for that. In our case, that is SBCL, and so we will have to defer this demo to the SBCL section.

Joining on a thread, destroying a thread

To join on a thread, we use the bt:join-thread function, and for destroying a thread (not a recommended operation), we can use the bt:destroy-thread function.

A simple demo:

    (defmacro until (condition &body body)
      (let ((block-name (gensym)))
        `(block ,block-name
           (if ,condition
               (return-from ,block-name nil)
               (progn
                 ,@body)))))

    (defun join-destroy-thread ()
      (let* ((s *standard-output*)
            (joiner-thread (bt:make-thread
                            (lambda ()
                              (loop for i from 1 to 10
                                 do
                                   (format s "~%[Joiner Thread]  Working...")
                                   (sleep (* 0.01 (random 100)))))))
            (destroyer-thread (bt:make-thread
                               (lambda ()
                                 (loop for i from 1 to 1000000
                                    do
                                      (format s "~%[Destroyer Thread] Working...")
                                      (sleep (* 0.01 (random 10000))))))))
        (format t "~%[Main Thread] Waiting on joiner thread...")
        (bt:join-thread joiner-thread)
        (format t "~%[Main Thread] Done waiting on joiner thread")
        (if (bt:thread-alive-p destroyer-thread)
            (progn
              (format t "~%[Main Thread] Destroyer thread alive... killing it")
              (bt:destroy-thread destroyer-thread))
            (format t "~%[Main Thread] Destroyer thread is already dead"))
        (until (bt:thread-alive-p destroyer-thread)
               (format t "[Main Thread] Waiting for destroyer thread to die..."))
        (format t "~%[Main Thread] Destroyer thread dead")
        (format t "~%[Main Thread] Adios!~%")))

And the output on a run:

    CL-USER> (join-destroy-thread)

    [Joiner Thread]  Working...
    [Destroyer Thread] Working...
    [Main Thread] Waiting on joiner thread...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Main Thread] Done waiting on joiner thread
    [Main Thread] Destroyer thread alive... killing it
    [Main Thread] Destroyer thread dead
    [Main Thread] Adios!
    NIL

The until macro simply loops around until the condition becomes true. The rest of the code is pretty much self-explanatory — the main thread waits for the joiner-thread to finish, but it immediately destroys the destroyer-thread.

Again, it is not recommended to use bt:destroy-thread. Any conceivable situation which requires this function can probably be done better with another approach.

Now let’s move onto some more comprehensive examples which tie together all the concepts discussed thus far.

Useful functions

Here is a summary of the functions, macros and global variables which were used in the demo examples along with some extras. These should cover most of the basic programming scenarios:

  • bt:*supports-thread-p* (to check for basic thread support)
  • bt:make-thread (create a new thread)
  • bt:current-thread (return the current thread object)
  • bt:all-threads (return a list of all running threads)
  • bt:thread-alive-p (checks if the thread is still alive)
  • bt:thread-name (return the name of the thread)
  • bt:join-thread (join on the supplied thread)
  • bt:interrupt-thread (interrupt the given thread)
  • bt:destroy-thread (attempt to abort the thread)
  • bt:make-lock (create a mutex)
  • bt:with-lock-held (use the supplied lock to protect critical code)

SBCL threads

SBCL provides support for native threads via its sb-thread package. These are very low-level functions, but we can build our own abstractions on top of these as shown in the demo examples.

You can refer to the documentation for more details (check the “Wrap-up” section).

You can see from the examples below that there is a strong correspondence between Bordeaux and SBCL Thread functions. In most cases, the only difference is the change of package name from bt to sb-thread.

It is evident that the Bordeaux thread library was more or less based on the SBCL implementation. As such, explanation will be provided only in those cases where there is a major difference in syntax or semantics.

Basics — list current thread, list all threads, get thread name

The code:

    ;;; Print the current thread, all the threads, and the current thread's name

    (defun print-thread-info ()
      (let* ((curr-thread sb-thread:*current-thread*)
             (curr-thread-name (sb-thread:thread-name curr-thread))
             (all-threads (sb-thread:list-all-threads)))
        (format t "Current thread: ~a~%~%" curr-thread)
        (format t "Current thread name: ~a~%~%" curr-thread-name)
        (format t "All threads:~% ~{~a~%~}~%" all-threads))
      nil)

And the output:

    CL-USER> (print-thread-info)
    Current thread: #<THREAD "repl-thread" RUNNING {10043B8003}>

    Current thread name: repl-thread

    All threads:
     #<THREAD "repl-thread" RUNNING {10043B8003}>
    #<THREAD "auto-flush-thread" RUNNING {10043B7DA3}>
    #<THREAD "swank-indentation-cache-thread" waiting on: #<WAITQUEUE  {1003A28103}> {1003A201A3}>
    #<THREAD "reader-thread" RUNNING {1003A20063}>
    #<THREAD "control-thread" waiting on: #<WAITQUEUE  {1003A19E53}> {1003A18C83}>
    #<THREAD "Swank Sentinel" waiting on: #<WAITQUEUE  {1003790043}> {1003788023}>
    #<THREAD "main thread" RUNNING {1002991CE3}>

    NIL

Update a global variable from a thread

The code:

    ;;; Update a global variable from a thread

    (defparameter *counter* 0)

    (defun test-update-global-variable ()
      (sb-thread:make-thread
       (lambda ()
         (sleep 1)
         (incf *counter*)))
      *counter*)

And the output:

    CL-USER> (test-update-global-variable)
    0

The code:

    ;;; Print a message onto the top-level using a thread

    (defun print-message-top-level-wrong ()
      (sb-thread:make-thread
       (lambda ()
         (format *standard-output* "Hello from thread!")))
      nil)

And the output:

    CL-USER> (print-message-top-level-wrong)
    NIL

Print a message onto the top-level — fixed:

The code:

    ;;; Print a message onto the top-level using a thread - fixed

    (defun print-message-top-level-fixed ()
      (let ((top-level *standard-output*))
        (sb-thread:make-thread
         (lambda ()
           (format top-level "Hello from thread!"))))
      nil)

And the output:

    CL-USER> (print-message-top-level-fixed)
    Hello from thread!
    NIL

The code:

    ;;; Print a message onto the top-level using a thread - reader macro

    (eval-when (:compile-toplevel)
      (defun print-message-top-level-reader-macro ()
        (sb-thread:make-thread
         (lambda ()
           (format #.*standard-output* "Hello from thread!")))
        nil))

And the output:

    CL-USER> (print-message-top-level-reader-macro)
    Hello from thread!
    NIL

Modify a shared resource from multiple threads

The code:

    ;;; Modify a shared resource from multiple threads

    (defclass bank-account ()
      ((id :initarg :id
           :initform (error "id required")
           :accessor :id)
       (name :initarg :name
             :initform (error "name required")
             :accessor :name)
       (balance :initarg :balance
                :initform 0
                :accessor :balance)))

    (defgeneric deposit (account amount)
      (:documentation "Deposit money into the account"))

    (defgeneric withdraw (account amount)
      (:documentation "Withdraw amount from account"))

    (defmethod deposit ((account bank-account) (amount real))
      (incf (:balance account) amount))

    (defmethod withdraw ((account bank-account) (amount real))
      (decf (:balance account) amount))

    (defparameter *rich*
      (make-instance 'bank-account
                     :id 1
                     :name "Rich"
                     :balance 0))

    (defun demo-race-condition ()
      (loop repeat 100
         do
           (sb-thread:make-thread
            (lambda ()
              (loop repeat 10000 do (deposit *rich* 100))
              (loop repeat 10000 do (withdraw *rich* 100))))))

And the output:

    CL-USER> (:balance *rich*)
    0
    CL-USER> (demo-race-condition)
    NIL
    CL-USER> (:balance *rich*)
    3987400

Modify a shared resource from multiple threads — fixed using locks

The code:

    (defvar *lock* (sb-thread:make-mutex))

    (defun demo-race-condition-locks ()
      (loop repeat 100
         do
           (sb-thread:make-thread
            (lambda ()
              (loop repeat 10000 do (sb-thread:with-mutex (*lock*)
                                      (deposit *rich* 100)))
              (loop repeat 10000 do (sb-thread:with-mutex (*lock*)
                                      (withdraw *rich* 100)))))))

The only difference here is that instead of make-lock as in Bordeaux, we have make-mutex and that is used along with the macro with-mutex as shown in the example.

And the output:

    CL-USER> (:balance *rich*)
    0
    CL-USER> (demo-race-condition-locks)
    NIL
    CL-USER> (:balance *rich*)
    0

Modify a shared resource from multiple threads — using atomic operations

First, the code:

    ;;; Modify a shared resource from multiple threads - atomics

    (defgeneric atomic-deposit (account amount)
      (:documentation "Atomic version of the deposit method"))

    (defgeneric atomic-withdraw (account amount)
      (:documentation "Atomic version of the withdraw method"))

    (defmethod atomic-deposit ((account bank-account) (amount real))
      (sb-ext:atomic-incf (car (cons (:balance account) nil)) amount))

    (defmethod atomic-withdraw ((account bank-account) (amount real))
      (sb-ext:atomic-decf (car (cons (:balance account) nil)) amount))

    (defun demo-race-condition-atomics ()
      (loop repeat 100
         do (sb-thread:make-thread
             (lambda ()
               (loop repeat 10000 do (atomic-deposit *rich* 100))
               (loop repeat 10000 do (atomic-withdraw *rich* 100))))))

And the output:

    CL-USER> (dotimes (i 5)
               (format t "~%Opening: ~d" (:balance *rich*))
               (demo-race-condition-atomics)
               (format t "~%Closing: ~d~%" (:balance *rich*)))

    Opening: 0
    Closing: 0

    Opening: 0
    Closing: 0

    Opening: 0
    Closing: 0

    Opening: 0
    Closing: 0

    Opening: 0
    Closing: 0
    NIL

As you can see, SBCL’s atomic functions are a bit quirky. The two functions used here: sb-ext:incf and sb-ext:atomic-decf have the following signatures:

Macro: atomic-incf [sb-ext] place &optional diff

and

Macro: atomic-decf [sb-ext] place &optional diff

The interesting bit is that the “place” parameter must be any of the following (as per the documentation):

  • a defstruct slot with declared type (unsigned-byte 64) or aref of a (simple-array (unsigned-byte 64) (*)) The type sb-ext:word can be used for these purposes.
  • car or cdr (respectively first or REST) of a cons.
  • a variable defined using defglobal with a proclaimed type of fixnum.

This is the reason for the bizarre construct used in the atomic-deposit and atomic-decf methods.

One major incentive to use atomic operations as much as possible is performance. Let’s do a quick run of the demo-race-condition-locks and demo-race-condition-atomics functions over 1000 times and check the difference in performance (if any):

With locks:

    CL-USER> (time
                        (loop repeat 100
                          do (demo-race-condition-locks)))
    Evaluation took:
      57.711 seconds of real time
      431.451639 seconds of total run time (408.014746 user, 23.436893 system)
      747.61% CPU
      126,674,011,941 processor cycles
      3,329,504 bytes consed

    NIL

With atomics:

    CL-USER> (time
                        (loop repeat 100
                         do (demo-race-condition-atomics)))
    Evaluation took:
      2.495 seconds of real time
      8.175454 seconds of total run time (6.124259 user, 2.051195 system)
      [ Run times consist of 0.420 seconds GC time, and 7.756 seconds non-GC time. ]
      327.66% CPU
      5,477,039,706 processor cycles
      3,201,582,368 bytes consed

    NIL

The results? The locks version took around 57s whereas the lockless atomics version took just 2s! This is a massive difference indeed!

Joining on a thread, destroying a thread example

The code:

    ;;; Joining on and destroying a thread

    (defmacro until (condition &body body)
      (let ((block-name (gensym)))
        `(block ,block-name
           (if ,condition
               (return-from ,block-name nil)
               (progn
                 ,@body)))))

    (defun join-destroy-thread ()
      (let* ((s *standard-output*)
            (joiner-thread (sb-thread:make-thread
                            (lambda ()
                              (loop for i from 1 to 10
                                 do
                                   (format s "~%[Joiner Thread]  Working...")
                                   (sleep (* 0.01 (random 100)))))))
            (destroyer-thread (sb-thread:make-thread
                               (lambda ()
                                 (loop for i from 1 to 1000000
                                    do
                                      (format s "~%[Destroyer Thread] Working...")
                                      (sleep (* 0.01 (random 10000))))))))
        (format t "~%[Main Thread] Waiting on joiner thread...")
        (bt:join-thread joiner-thread)
        (format t "~%[Main Thread] Done waiting on joiner thread")
        (if (sb-thread:thread-alive-p destroyer-thread)
            (progn
              (format t "~%[Main Thread] Destroyer thread alive... killing it")
              (sb-thread:terminate-thread destroyer-thread))
            (format t "~%[Main Thread] Destroyer thread is already dead"))
        (until (sb-thread:thread-alive-p destroyer-thread)
               (format t "[Main Thread] Waiting for destroyer thread to die..."))
        (format t "~%[Main Thread] Destroyer thread dead")
        (format t "~%[Main Thread] Adios!~%")))

And the output:

    CL-USER> (join-destroy-thread)

    [Joiner Thread]  Working...
    [Destroyer Thread] Working...
    [Main Thread] Waiting on joiner thread...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Joiner Thread]  Working...
    [Main Thread] Done waiting on joiner thread
    [Main Thread] Destroyer thread alive... killing it
    [Main Thread] Destroyer thread dead
    [Main Thread] Adios!
    NIL

Useful functions

Here is a summarised list of the functions, macros and global variables used in the examples along with some extras:

  • (member :thread-support *features*) (check thread support)
  • sb-thread:make-thread (create a new thread)
  • sb-thread:*current-thread* (holds the current thread object)
  • sb-thread:list-all-threads (return a list of all running threads)
  • sb-thread:thread-alive-p (checks if the thread is still alive)
  • sb-thread:thread-name (return the name of the thread)
  • sb-thread:join-thread (join on the supplied thread)
  • sb-thread:interrupt-thread (interrupt the given thread)
  • sb-thread:destroy-thread (attempt to abort the thread)
  • sb-thread:make-mutex (create a mutex)
  • sb-thread:with-mutex (use supplied lock to protect critical code)

Wrap-up

As you can see, concurrency support is rather primitive in Common Lisp, but that’s primarily due to the glaring absence of this important feature in the ANSI Common Lisp specification. That does not detract in the least from the support provided by Common Lisp implementations, nor wonderful libraries like the Bordeaux library.

You should follow up on your own by reading a lot more on this topic. I share some of my own references here:

Next up, the final post in this mini-series: parallelism in Common Lisp using the lparallel library.

Parallel programming with lparallel

It is important to note that lparallel also provides extensive support for asynchronous programming, and is not a purely parallel programming library. As stated before, parallelism is merely an abstract concept in which tasks are conceptually independent of one another.

The lparallel library is built on top of the Bordeaux threading library.

As mentioned previously, parallelism and concurrency can be (and usually are) implemented using the same means — threads, processes, etc. The difference between lies in their conceptual differences.

Note that not all the examples shown in this post are necessarily parallel. Asynchronous constructs such as Promises and Futures are, in particular, more suited to concurrent programming than parallel programming.

The modus operandi of using the lparallel library (for a basic use case) is as follows:

  • Create an instance of what the library calls a kernel using lparallel:make-kernel. The kernel is the component that schedules and executes tasks.
  • Design the code in terms of futures, promises and other higher level functional concepts. To this end, lparallel provides support for channels, promises, futures, and cognates.
  • Perform operations using what the library calls cognates, which are simply functions which have equivalents in the Common Lisp language itself. For instance, the lparallel:pmap function is the parallel equivalent of the Common Lisp map function.
  • Finally, close the kernel created in the first step using lparallel:end-kernel.

Note that the onus of ensuring that the tasks being carried out are logically parallelisable as well as taking care of all mutable state is on the developer.

Credit: this article first appeared on z0ltan.wordpress.com.

Installation

Let’s check if lparallel is available for download using Quicklisp:

CL-USER> (ql:system-apropos "lparallel")
#<SYSTEM lparallel / lparallel-20160825-git / quicklisp 2016-08-25>
#<SYSTEM lparallel-bench / lparallel-20160825-git / quicklisp 2016-08-25>
#<SYSTEM lparallel-test / lparallel-20160825-git / quicklisp 2016-08-25>
; No value

Looks like it is. Let’s go ahead and install it:

CL-USER> (ql:quickload :lparallel)
To load "lparallel":
  Load 2 ASDF systems:
    alexandria bordeaux-threads
  Install 1 Quicklisp release:
    lparallel
; Fetching #<URL "http://beta.quicklisp.org/archive/lparallel/2016-08-25/lparallel-20160825-git.tgz">
; 76.71KB
==================================================
78,551 bytes in 0.62 seconds (124.33KB/sec)
; Loading "lparallel"
[package lparallel.util]..........................
[package lparallel.thread-util]...................
[package lparallel.raw-queue].....................
[package lparallel.cons-queue]....................
[package lparallel.vector-queue]..................
[package lparallel.queue].........................
[package lparallel.counter].......................
[package lparallel.spin-queue]....................
[package lparallel.kernel]........................
[package lparallel.kernel-util]...................
[package lparallel.promise].......................
[package lparallel.ptree].........................
[package lparallel.slet]..........................
[package lparallel.defpun]........................
[package lparallel.cognate].......................
[package lparallel]
(:LPARALLEL)

And that’s all it took! Now let’s see how this library actually works.

Preamble - get the number of cores with a call to CFFI

First, let’s get hold of the number of threads that we are going to use for our parallel examples. Ideally, we’d like to have a 1:1 match between the number of worker threads and the number of available cores.

We can use the wonderful cffi library to this end. I plan to have a detailed blog post for this extremely useful library soon, but for now, let’s get on with it:

Install CFFI:

CL-USER> (ql:quickload :cffi)
To load "cffi":
  Load 4 ASDF systems:
    alexandria babel trivial-features uiop
  Install 1 Quicklisp release:
    cffi
; Fetching #<URL "http://beta.quicklisp.org/archive/cffi/2016-03-18/cffi_0.17.1.tgz">
; 234.48KB
==================================================
240,107 bytes in 5.98 seconds (39.22KB/sec)
; Loading "cffi"
[package cffi-sys]................................
[package cffi]....................................
..................................................
[package cffi-features]
(:CFFI)

Write C code to get the number of logical cores on the machine:

#include <stdio.h>
#include <sys/types.h>
#include <sys/sysctl.h>

int get_core_count();

int main()
{
    printf("%d\n", get_core_count());

    return 0;
}

int32_t get_core_count()
{
    const char* s = "hw.logicalcpu";
    int32_t core_count;
    size_t len = sizeof(core_count);

    sysctlbyname(s, &core_count, &len, NULL, 0);

    return core_count;
}

Bundle the C code into a shared library (note, I am using Mac OS X which comes bundled with Clang. For pure gcc, refer to the relevant documentation): 1

Timmys-MacBook-Pro:Parallelism z0ltan$ clang -dynamiclib get_core_count.c -o libcorecount.dylib

Invoke the function from Common Lisp:

CL-USER> (cffi:use-foreign-library "libcorecount.dylib")
#<CFFI:FOREIGN-LIBRARY LIBCORECOUNT.DYLIB-853 "libcorecount.dylib">
CL-USER> (cffi:foreign-funcall "get_core_count" :int)
8

We can see that the result is 8 cores on the machine (which is correct) and can be verified from the command line as well:

Timmys-MacBook-Pro:Parallelism z0ltan$ sysctl -n "hw.logicalcpu"

Common Setup

In this example, we will go through the initial setup bit, and also show some useful information once the setup is done.

Load the library:

CL-USER> (ql:quickload :lparallel)
To load "lparallel":
  Load 1 ASDF system:
    lparallel
; Loading "lparallel"

(:LPARALLEL)

Initialise the lparallel kernel:

CL-USER> (setf lparallel:*kernel* (lparallel:make-kernel 8 :name "custom-kernel"))
#<LPARALLEL.KERNEL:KERNEL :NAME "custom-kernel" :WORKER-COUNT 8 :USE-CALLER NIL :ALIVE T :SPIN-COUNT 2000 {1003141F03}>

Note that the *kernel* global variable can be rebound — this allows multiple kernels to co-exist during the same run. Now, some useful information about the kernel:

CL-USER> (defun show-kernel-info ()
           (let ((name (lparallel:kernel-name))
                 (count (lparallel:kernel-worker-count))
                 (context (lparallel:kernel-context))
                 (bindings (lparallel:kernel-bindings)))
             (format t "Kernel name = ~a~%" name)
             (format t "Worker threads count = ~d~%" count)
             (format t "Kernel context = ~a~%" context)
             (format t "Kernel bindings = ~a~%" bindings)))


WARNING: redefining COMMON-LISP-USER::SHOW-KERNEL-INFO in DEFUN
SHOW-KERNEL-INFO

CL-USER> (show-kernel-info)
Kernel name = custom-kernel
Worker threads count = 8
Kernel context = #<FUNCTION FUNCALL>
Kernel bindings = ((*STANDARD-OUTPUT* . #<SLIME-OUTPUT-STREAM {10044EEEA3}>)
                   (*ERROR-OUTPUT* . #<SLIME-OUTPUT-STREAM {10044EEEA3}>))
NIL

End the kernel (this is important since *kernel* does not get garbage collected until we explictly end it):

CL-USER> (lparallel:end-kernel :wait t)
(#<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {100723FA83}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {100723FE23}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {10072581E3}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {1007258583}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {1007258923}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {1007258CC3}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {1007259063}>
 #<SB-THREAD:THREAD "custom--kernel" FINISHED values: NIL {1007259403}>)

Let’s move on to some more examples of different aspects of the lparallel library.

For these demos, we will be using the following initial setup from a coding perspective:

(require ‘lparallel)
(require ‘bt-semaphore)

(defpackage :lparallel-user
  (:use :cl :lparallel :lparallel.queue :bt-semaphore))

(in-package :lparallel-user)

;;; initialise the kernel
(defun init ()
  (setf *kernel* (make-kernel 8 :name "channel-queue-kernel")))

(init)

So we will be using a kernel with 8 worker threads (one for each CPU core on the machine).

And once we’re done will all the examples, the following code will be run to close the kernel and free all used system resources:

;;; shut the kernel down
(defun shutdown ()
  (end-kernel :wait t))

(shutdown)

Using channels and queues

First some definitions are in order.

A task is a job that is submitted to the kernel. It is simply a function object along with its arguments.

A channel in lparallel is similar to the same concept in Go. A channel is simply a means of communication with a worker thread. In our case, it is one particular way of submitting tasks to the kernel.

A channel is created in lparallel using lparallel:make-channel. A task is submitted using lparallel:submit-task, and the results received via lparallel:receive-result.

For instance, we can calculate the square of a number as:

(defun calculate-square (n)
  (let* ((channel (lparallel:make-channel))
         (res nil))
    (lparallel:submit-task channel #'(lambda (x)
                                       (* x x))
                           n)
    (setf res (lparallel:receive-result channel))
    (format t "Square of ~d = ~d~%" n res)))

And the output:

LPARALLEL-USER> (calculate-square 100)
Square of 100 = 10000
NIL

Now let’s try submitting multiple tasks to the same channel. In this simple example, we are simpy creating three tasks that square, triple, and quadrupls the supplied input respectively.

Note that in case of multiple tasks, the output will be in non-deterministic order:

(defun test-basic-channel-multiple-tasks ()
  (let ((channel (make-channel))
        (res '()))
    (submit-task channel #'(lambda (x)
                             (* x x))
                 10)
    (submit-task channel #'(lambda (y)
                             (* y y y))
                 10)
    (submit-task channel #'(lambda (z)
                             (* z z z z))
                 10)
     (dotimes (i 3 res)
       (push (receive-result channel) res))))

And the output:

LPARALLEL-USER> (dotimes (i 3)
                              (print (test-basic-channel-multiple-tasks)))

(100 1000 10000)
(100 1000 10000)
(10000 1000 100)
NIL

lparallel also provides support for creating a blocking queue in order to enable message passing between worker threads. A queue is created using lparallel.queue:make-queue.

Some useful functions for using queues are:

  • lparallel.queue:make-queue: create a FIFO blocking queue
  • lparallel.queue:push-queue: insert an element into the queue
  • lparallel.queue:pop-queue: pop an item from the queue
  • lparallel.queue:peek-queue: inspect value without popping it
  • lparallel.queue:queue-count: the number of entries in the queue
  • lparallel.queue:queue-full-p: check if the queue is full
  • lparallel.queue:queue-empty-p:check if the queue is empty
  • lparallel.queue:with-locked-queue: lock the queue during access

A basic demo showing basic queue properties:

    (defun test-queue-properties ()
      (let ((queue (make-queue :fixed-capacity 5)))
        (loop
           when (queue-full-p queue)
           do (return)
           do (push-queue (random 100) queue))
         (print (queue-full-p queue))
        (loop
           when (queue-empty-p queue)
           do (return)
           do (print (pop-queue queue)))
        (print (queue-empty-p queue)))
      nil)

Which produces:

    LPARALLEL-USER> (test-queue-properties)

    T
    17
    51
    55
    42
    82
    T
    NIL

Note: lparallel.queue:make-queue is a generic interface which is actually backed by different types of queues. For instance, in the previous example, the actual type of the queue is lparallel.vector-queue since we specified it to be of fixed size using the :fixed-capacity keyword argument.

The documentation doesn’t actually specify what keyword arguments we can pass to lparallel.queue:make-queue, so let’s and find that out in a different way:

    LPARALLEL-USER> (describe 'lparallel.queue:make-queue)
    LPARALLEL.QUEUE:MAKE-QUEUE
      [symbol]

    MAKE-QUEUE names a compiled function:
      Lambda-list: (&REST ARGS)
      Derived type: FUNCTION
      Documentation:
        Create a queue.

        The queue contents may be initialized with the keyword argument
        `initial-contents'.

        By default there is no limit on the queue capacity. Passing a
        `fixed-capacity' keyword argument limits the capacity to the value
        passed. `push-queue' will block for a full fixed-capacity queue.
      Source file: /Users/z0ltan/quicklisp/dists/quicklisp/software/lparallel-20160825-git/src/queue.lisp

    MAKE-QUEUE has a compiler-macro:
      Source file: /Users/z0ltan/quicklisp/dists/quicklisp/software/lparallel-20160825-git/src/queue.lisp
    ; No value

So, as we can see, it supports the following keyword arguments: :fixed-capacity, and initial-contents.

Now, if we do specify :fixed-capacity, then the actual type of the queue will be lparallel.vector-queue, and if we skip that keyword argument, the queue will be of type lparallel.cons-queue (which is a queue of unlimited size), as can be seen from the output of the following snippet:

    (defun check-queue-types ()
      (let ((queue-one (make-queue :fixed-capacity 5))
            (queue-two (make-queue)))
        (format t "queue-one is of type: ~a~%" (type-of queue-one))
        (format t "queue-two is of type: ~a~%" (type-of queue-two))))


    LPARALLEL-USER> (check-queue-types)
    queue-one is of type: VECTOR-QUEUE
    queue-two is of type: CONS-QUEUE
    NIL

Of course, you can always create instances of the specific queue types yourself, but it is always better, when you can, to stick to the generic interface and letting the library create the proper type of queue for you.

Now, let’s just see the queue in action!

    (defun test-basic-queue ()
      (let ((queue (make-queue))
            (channel (make-channel))
            (res '()))
        (submit-task channel #'(lambda ()
                         (loop for entry = (pop-queue queue)
                            when (queue-empty-p queue)
                            do (return)
                            do (push (* entry entry) res))))
        (dotimes (i 100)
          (push-queue i queue))
        (receive-result channel)
        (format t "~{~d ~}~%" res)))

Here we submit a single task that repeatedly scans the queue till it’s empty, pops the available values, and pushes them into the res list.

And the output:

    LPARALLEL-USER> (test-basic-queue)
    9604 9409 9216 9025 8836 8649 8464 8281 8100 7921 7744 7569 7396 7225 7056 6889 6724 6561 6400 6241 6084 5929 5776 5625 5476 5329 5184 5041 4900 4761 4624 4489 4356 4225 4096 3969 3844 3721 3600 3481 3364 3249 3136 3025 2916 2809 2704 2601 2500 2401 2304 2209 2116 2025 1936 1849 1764 1681 1600 1521 1444 1369 1296 1225 1156 1089 1024 961 900 841 784 729 676 625 576 529 484 441 400 361 324 289 256 225 196 169 144 121 100 81 64 49 36 25 16 9 4 1 0
    NIL

Killing tasks

A small note mentioning the lparallel:kill-task function would be apropos at this juncture. This function is useful in those cases when tasks are unresponsive. The lparallel documentation clearly states that this must only be used as a last resort.

All tasks which are created are by default assigned a category of :default. The dynamic property, *task-category* holds this value, and can be dynamically bound to different values (as we shall see).

;;; kill default tasks
(defun test-kill-all-tasks ()
  (let ((channel (make-channel))
        (stream *query-io*))
    (dotimes (i 10)
      (submit-task channel #'(lambda (x)
                               (sleep (random 10))
                               (format stream "~d~%" (* x x))) (random 10)))
    (sleep (random 2))
    (kill-tasks :default)))

Sample run:

LPARALLEL-USER> (test-kill-all-tasks)
16
1
8
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.

Since we had created 10 tasks, all the 8 kernel worker threads were presumably busy with a task each. When we killed tasks of category :default, all these threads were killed as well and had to be regenerated (which is an expensive operation). This is part of the reason why lparallel:kill-tasks must be avoided.

Now, in the example above, all running tasks were killed since all of them belonged to the :default category. Suppose we wish to kill only specific tasks, we can do that by binding *task-category* when we create those tasks, and then specifying the category when we invoke lparallel:kill-tasks.

For example, suppose we have two categories of tasks – tasks which square their arguments, and tasks which cube theirs. Let’s assign them categories ’squaring-tasks and ’cubing-tasks respectively. Let’s then kill tasks of a randomly chosen category ’squaring-tasks or ’cubing-tasks.

Here is the code:

;;; kill tasks of a randomly chosen category
(defun test-kill-random-tasks ()
  (let ((channel (make-channel))
        (stream *query-io*))
    (let ((*task-category* 'squaring-tasks))
      (dotimes (i 5)
        (submit-task channel #'(lambda (x)
                                 (sleep (random 5))
                                 (format stream "~%[Squaring] ~d = ~d" x (* x x))) i)))
    (let ((*task-category* 'cubing-tasks))
      (dotimes (i 5)
        (submit-task channel #'(lambda (x)
                                 (sleep (random 5))
                                 (format stream "~%[Cubing] ~d = ~d" x (* x x x))) i)))
    (sleep 1)
    (if (evenp (random 10))
        (progn
          (print "Killing squaring tasks")
          (kill-tasks 'squaring-tasks))
        (progn
          (print "Killing cubing tasks")
          (kill-tasks 'cubing-tasks)))))

And here is a sample run:

LPARALLEL-USER> (test-kill-random-tasks)

[Cubing] 2 = 8
[Squaring] 4 = 16
[Cubing] 4
 = [Cubing] 643 = 27
"Killing squaring tasks"
4
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.

[Cubing] 1 = 1
[Cubing] 0 = 0

LPARALLEL-USER> (test-kill-random-tasks)

[Squaring] 1 = 1
[Squaring] 3 = 9
"Killing cubing tasks"
5
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.

[Squaring] 2 = 4
WARNING: lparallel: Replacing lost or dead worker.
WARNING: lparallel: Replacing lost or dead worker.

[Squaring] 0 = 0
[Squaring] 4 = 16

Using promises and futures

Promises and Futures provide support for Asynchronous Programming.

In lparallel-speak, a lparallel:promise is a placeholder for a result which is fulfilled by providing it with a value. The promise object itself is created using lparallel:promise, and the promise is given a value using the lparallel:fulfill macro.

To check whether the promise has been fulfilled yet or not, we can use the lparallel:fulfilledp predicate function. Finally, the lparallel:force function is used to extract the value out of the promise. Note that this function blocks until the operation is complete.

Let’s solidify these concepts with a very simple example first:

(defun test-promise ()
  (let ((p (promise)))
    (loop
       do (if (evenp (read))
              (progn
                (fulfill p 'even-received!)
                (return))))
    (force p)))

Which generates the output:

LPARALLEL-USER> (test-promise)
5
1
3
10
EVEN-RECEIVED!

Explanation: This simple example simply keeps looping forever until an even number has been entered. The promise is fulfilled inside the loop using lparallel:fulfill, and the value is then returned from the function by forcing it with lparallel:force.

Now, let’s take a bigger example. Assuming that we don’t want to have to wait for the promise to be fulfilled, and instead have the current do some useful work, we can delegate the promise fulfillment to external explicitly as seen in the next example.

Consider we have a function that squares its argument. And, for the sake of argument, it consumes a lot of time doing so. From our client code, we want to invoke it, and wait till the squared value is available.

(defun promise-with-threads ()
  (let ((p (promise))
        (stream *query-io*)
        (n (progn
             (princ "Enter a number: ")
             (read))))
    (format t "In main function...~%")
    (bt:make-thread
     #'(lambda ()
         (sleep (random 10))
         (format stream "Inside thread... fulfilling promise~%")
         (fulfill p (* n n))))
    (bt:make-thread
     #'(lambda ()
         (loop
            when (fulfilledp p)
            do (return)
            do (progn
                 (format stream "~d~%" (random 100))
                 (sleep (* 0.01 (random 100)))))))
    (format t "Inside main function, received value: ~d~%" (force p))))

And the output:

LPARALLEL-USER> (promise-with-threads)
Enter a number: 19
In main function...
44
59
90
34
30
76
Inside thread... fulfilling promise
Inside main function, received value: 361
NIL

Explanation: There is nothing much in this example. We create a promise object p, and we spawn off a thread that sleeps for some random time and then fulfills the promise by giving it a value.

Meanwhile, in the main thread, we spawn off another thread that keeps hecking if the promise has been fulfilled or not. If not, it prints some random number and continues checking. Once the promise has been fulfilled, we can extract the value using lparallel:force in the main thread as shown.

This shows that promises can be fulfilled by different threads while the code that created the promise need not wait for the promise to be fulfilled. This is especially important since, as mentioned before, lparallel:force is a blocking call. We want to delay forcing the promise until the value is actually available.

Another point to note when using promises is that once a promise has been fulfilled, invoking force on the same object will always return the same value. That is to say, a promise can be successfully fulfilled only once.

For instance:

(defun multiple-fulfilling ()
  (let ((p (promise)))
    (dotimes (i 10)
      (fulfill p (random 100))
      (format t "~d~%" (force p)))))

Which produces:

LPARALLEL-USER> (multiple-fulfilling)
15
15
15
15
15
15
15
15
15
15
NIL

So how does a future differ from a promise?

A lparallel:future is simply a promise that is run in parallel, and as such, it does not block the main thread like a default use of lparallel:promise would. It is executed in its own thread (by the lparallel library, of course).

Here is a simple example of a future:

(defun test-future ()
  (let ((f (future
             (sleep (random 5))
             (print "Hello from future!"))))
    (loop
       when (fulfilledp f)
       do (return)
       do (sleep (* 0.01 (random 100)))
         (format t "~d~%" (random 100)))
    (format t "~d~%" (force f))))

And the output:

LPARALLEL-USER> (test-future)
5
19
91
11
Hello from future!
NIL

Explanation: This exactly is similar to the promise-with-threads example. Observe two differences, however - first of all, the lparallel:future macro has a body as well. This allows the future to fulfill itself! What this means is that as soon as the body of the future is done executing, lparallel:fulfilledp will always return true for the future object.

Secondly, the future itself is spawned off on a separate thread by the library, so it does not interfere with the execution of the current thread very much unlike promises as could be seen in the promise-with-threads example (which needed an explicit thread for the fulfilling code in order to avoid blocking the current thread).

The most interesting bit is that (even in terms of the actual theory propounded by Dan Friedman and others), a Future is conceptually something that fulfills a Promise. That is to say, a promise is a contract that some value will be generated sometime in the future, and a future is precisely that “something” that does that job.

What this means is that even when using the lparallel library, the basic use of a future would be to fulfill a promise. This means that hacks like promise-with-threads need not be made by the user.

Let’s take a small example to demonstrate this point (a pretty contrived example, I must admit!).

Here’s the scenario: we want to read in a number and calculate its square. So we offload this work to another function, and continue with our own work. When the result is ready, we want it to be printed on the console without any intervention from us.

Here’s how the code looks:

;;; Callback example using promises and futures
(defun callback-promise-future-demo ()
  (let* ((p (promise))
         (stream *query-io*)
         (n (progn
              (princ "Enter a number: ")
              (read)))
         (f (future
              (sleep (random 10))
              (fulfill p (* n n))
              (force (future
                       (format stream "Square of ~d = ~d~%" n (force p)))))))
    (loop
       when (fulfilledp f)
       do (return)
       do (sleep (* 0.01 (random 100))))))

And the output:

LPARALLEL-USER> (callback-promise-future-demo)
Enter a number: 19
Square of 19 = 361
NIL

Explanation: All right, so first off, we create a promise to hold the squared value when it is generated. This is the p object. The input value is stored in the local variable n.

Then we create a future object f. This future simply squares the input value and fulfills the promise with this value. Finally, since we want to print the output in its own time, we force an anonymous future which simply prints the output string as shown.

Note that this is very similar to the situation in an environment like Node, where we pass callback functions to other functions with the understanding that the callback will be called when the invoked function is done with its work.

Finally note that the following snippet is still fine (even if it uses the blocking lparallel:force call because it’s on a separate thread):

(force (future
(format stream "Square of ~d = ~d~%" n (force p))))

To summarise, the general idiom of usage is: define objects which will hold the results of asynchronous computations in promises, and use futures to fulfill those promises.

Using cognates - parallel equivalents of Common Lisp counterparts

Cognates are argubaly the raison d’etre of the lparallel library. These constructs are what truly provide parallelism in the lparallel. Note, however, that most (if not all) of these constructs are built on top of futures and promises.

To put it in a nutshell, cognates are simply functions that are intended to be the parallel equivalents of their Common Lisp counterparts. However, there are a few extra lparallel cognates that have no Common Lisp equivalents.

At this juncture, it is important to know that cognates come in two basic flavours:

  • Constructs for fine-grained parallelism: defpun, plet, plet-if, etc.
  • Explicit functions and macros for performing parallel operations - pmap, preduce, psort, pdotimes, etc.

In the first case we don’t have much explicit control over the operations themselves. We mostly rely on the fact that the library itself will optimise and parallelise the forms to whatever extent it can. In this post, we will focus on the second category of cognates.

Take, for instance, the cognate function lparallel:pmap is exactly the same as the Common Lisp equivalent, map, but it runs in parallel. Let’s demonstrate that through an example.

Suppose we had a list of random strings of length varying from 3 to 10, and we wished to collect their lengths in a vector.

Let’s first set up the helper functions that will generate the random strings:

(defvar *chars*
  (remove-duplicates
   (sort
    (loop for c across "The quick brown fox jumps over the lazy dog"
       when (alpha-char-p c)
       collect (char-downcase c))
    #'char<)))

(defun get-random-strings (&optional (count 100000))
  "generate random strings between lengths 3 and 10"
  (loop repeat count
     collect
       (concatenate 'string  (loop repeat (+ 3 (random 8))
                           collect (nth (random 26) *chars*)))))

And here’s how the Common Lisp map version of the solution might look like:

;;; map demo
(defun test-map ()
  (map 'vector #'length (get-random-strings 100)))

And let’s have a test run:

LPARALLEL-USER> (test-map)
#(7 5 10 8 7 5 3 4 4 10)

And here’s the lparallel:pmap equivalent:

;;;pmap demo
(defun test-pmap ()
  (pmap 'vector #'length (get-random-strings 100)))

which produces:

LPARALLEL-USER> (test-pmap)
#(8 7 6 7 6 4 5 6 5 7)
LPARALLEL-USER>

As you can see from the definitions of test-map and test-pmap, the syntax of the lparallel:map and lparallel:pmap functions are exactly the same (well, almost - lparallel:pmap has a few more optional arguments).

Some useful cognate functions and macros (all of them are functions except when marked so explicitly. Note that there are quite a few cognates, and I have chosen a few to try and represent every category through an example:

lparallel:pmap: parallel version of map.

Note that all the mapping functions (lparallel:pmap, lparallel:pmapc,lparallel:pmapcar, etc.) take two special keyword arguments

  • :size, specifiying the number of elements of the input sequence(s) to process, and
  • :parts which specifies the number of parallel parts to divide the sequence(s) into.
    ;;; pmap - function
    (defun test-pmap ()
      (let ((numbers (loop for i below 10
                        collect i)))
        (pmap 'vector #'(lambda (x)
                          (* x x))
              :parts (length numbers)
              numbers)))

Sample run:

    LPARALLEL-USER> (test-pmap)

    #(0 1 4 9 16 25 36 49 64 81)

lparallel:por: parallel version of or.

The behaviour is that it returns the first non-nil element amongst its arguments. However, due to the parallel nature of this macro, that element varies.

    ;;; por - macro
    (defun test-por ()
      (let ((a 100)
            (b 200)
            (c nil)
            (d 300))
        (por a b c d)))

Sample run:

    LPARALLEL-USER> (dotimes (i 10)
                      (print (test-por)))

    300
    300
    100
    100
    100
    300
    100
    100
    100
    100
    NIL

In the case of the normal or operator, it would always have returned the first non-nil element viz. 100.

lparallel:pdotimes: parallel version of dotimes.

Note that this macro also take an optional :parts argument.

    ;;; pdotimes - macro
    (defun test-pdotimes ()
      (pdotimes (i 5)
        (declare (ignore i))
        (print (random 100))))

Sample run:

    LPARALLEL-USER> (test-pdotimes)

    39
    29
    81
    42
    56
    NIL

lparallel:pfuncall: parallel version of funcall.

    ;;; pfuncall - macro
    (defun test-pfuncall ()
      (pfuncall #'* 1 2 3 4 5))

Sample run:

    LPARALLEL-USER> (test-pfuncall)

    120

lparallel:preduce: parallel version of reduce.

This very important function also takes two optional keyword arguments: :parts (same meaning as explained), and :recurse. If :recurse is non-nil, it recursively applies lparallel:preduce to its arguments, otherwise it default to using reduce.

    ;;; preduce - function
    (defun test-preduce ()
      (let ((numbers (loop for i from 1 to 100
                        collect i)))
        (preduce #'+
                 numbers
                 :parts (length numbers)
                 :recurse t)))

Sample run:

    LPARALLEL-USER> (test-preduce)

    5050

lparallel:premove-if-not: parallel version of remove-if-not.

This is essentially equivalent to “filter” in Functional Programming parlance.

    ;;; premove-if-not
    (defun test-premove-if-not ()
      (let ((numbers (loop for i from 1 to 100
                        collect i)))
        (premove-if-not #'evenp numbers)))

Sample run:

    LPARALLEL-USER> (test-premove-if-not)

    (2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
     56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100)

lparallel:pevery: parallel version of every.

    ;;; pevery - function
    (defun test-pevery ()
      (let ((numbers (loop for i from 1 to 100
                        collect i)))
        (list (pevery #'evenp numbers)
              (pevery #'integerp numbers))))

Sample run:

    LPARALLEL-USER> (test-pevery)

    (NIL T)

In this example, we are performing two checks - firstly, whether all the numbers in the range [1,100] are even, and secondly, whether all the numbers in the same range are integers.

lparallel:count: parallel version of count.

    ;;; pcount - function
    (defun test-pcount ()
      (let ((chars "The quick brown fox jumps over the lazy dog"))
        (pcount #\e chars)))

Sample run:

    LPARALLEL-USER> (test-pcount)

    3

lparallel:psort: parallel version of sort.

    ;;; psort - function
    (defstruct person
      name
      age)

    (defun test-psort ()
      (let* ((names (list "Rich" "Peter" "Sybil" "Basil" "Candy" "Slava" "Olga"))
             (people (loop for name in names
                        collect (make-person :name name :age (+ (random 20) 20)))))
        (print "Before sorting...")
        (print people)
        (fresh-line)
        (print "After sorting...")
        (psort
         people
         #'(lambda (x y)
             (< (person-age x)
                (person-age y)))
         :test #'=)))

Sample run:

    LPARALLEL-USER> (test-psort)

    "Before sorting..."
    (#S(PERSON :NAME "Rich" :AGE 38) #S(PERSON :NAME "Peter" :AGE 24)
     #S(PERSON :NAME "Sybil" :AGE 20) #S(PERSON :NAME "Basil" :AGE 22)
     #S(PERSON :NAME "Candy" :AGE 23) #S(PERSON :NAME "Slava" :AGE 37)
     #S(PERSON :NAME "Olga" :AGE 33))

    "After sorting..."
    (#S(PERSON :NAME "Sybil" :AGE 20) #S(PERSON :NAME "Basil" :AGE 22)
     #S(PERSON :NAME "Candy" :AGE 23) #S(PERSON :NAME "Peter" :AGE 24)
     #S(PERSON :NAME "Olga" :AGE 33) #S(PERSON :NAME "Slava" :AGE 37)
     #S(PERSON :NAME "Rich" :AGE 38))

In this example, we first define a structure of type person for storing information about people. Then we create a list of 7 people with randomly generated ages (between 20 and 39). Finally, we sort them by age in non-decreasing order.

Error handling

To see how lparallel handles error handling (hint: with lparallel:task-handler-bind), please read https://z0ltan.wordpress.com/2016/09/10/basic-concurrency-and-parallelism-in-common-lisp-part-4b-parallelism-using-lparallel-error-handling/.

References

There are, of course, a lot more functions, objects, and idiomatic ways of performing parallel computations using the lparallel library. This post barely scratches the surface on those. However, the general flow of operation is amply demonstrated here, and for further reading, you may find the following resources useful: