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 List Reducers
 List-append and list-prepend reducers create standard lists by concatenating a set of lists or values in parallel.
 Minimum and Maximum Reducers
 Minimum and maximum reducers allow the computation of the minimum or maximum of a set of values in parallel.
 Addition Reducers
 Addition reducers allow the computation of the sum of a set of values in parallel.
 Bitwise AND Reducers
 Bitwise AND reducers allow the computation of the bitwise AND of a set of values in parallel.
 Multiplication Reducers
 Multiplication reducers allow the computation of the product of a set of values in parallel.
 Bitwise `OR` Reducers
 Bitwise OR reducers allow the computation of the bitwise OR of a set of values in parallel.
 Bitwise XOR Reducers
 Bitwise XOR reducers allow the computation of the bitwise XOR of a set of values in parallel.
 Ostream Reducers
 Ostream reducers allow multiple strands to write to an ostream in parallel.
 String Reducers
 String reducers allow the creation of a string by concatenating a set of strings or characters in parallel.
 Vector Reducers
 Vector reducers allow the creation of a standard vector by appending a set of elements in parallel.


file  reducer.h
 Defines foundation classes for creating Intel(R) Cilk(TM) Plus reducers.

Detailed Description

Intel(R) Cilk(TM) Plus Reducers


Reducers address the problem of computing a value by incrementally updating a variable in parallel code. Conceptually, a reducer is a variable that can be safely used by multiple strands running in parallel. The runtime ensures that each worker has access to a private instance of the variable, eliminating the possibility of races without requiring locks. When parallel strands merge, their variable instances are also merged ("reduced").

The Intel Cilk Plus library includes a general reducer framework and a collection of predefined reducer classes to solve common specific problems. Many users will find a predefined reducer class that meets their needs; but more advanced users can use the framework to create new reducers to solve their problems.

Some Motivating Examples

Avoiding Races

You might call this the "Hello, World" of reducer programs:

#include <iostream>
int main()
    unsigned long sum = 0;
    for (int i = 0; i != 1000; i++) {
        sum += i*i;
    std::cout << sum << "\n";

This looks like it should be pretty easy to parallelize. Just throw in a cilk_for, right?

#include <iostream>
#include <cilk/cilk.h>

int main()
    unsigned long sum = 0;
    cilk_for (int i = 0; i != 1000; i++) {
        sum += i*i;
    std::cout << sum << "\n";

Of course not! You would end up with multiple occurrences of sum += i*i executing simultaneously. Presto: instant data race.

At this point, in traditional parallel programming, you would start thinking about adding locks for the accumulator updates. Intel Cilk Plus has a more elegant solution, though. Replace the accumulator variable with an Intel Cilk Plus reducer, and Intel Cilk Plus will take care of the rest:

#include <iostream>
#include <cilk/cilk.h>
#include <cilk/reducer_opadd.h>

int main()
    cilk::reducer< cilk::op_add<unsigned long> > sum;
    cilk_for (int i = 0; i != 1000; i++) {
        *sum += i*i;
    std::cout << sum.get_value() << "\n";

You just

And it all just works.

Maintaining Order

That may not have seemed very impressive. Adding locks isn't all that big a bother. But coordinating a computation is often more of a problem than avoiding the data races. Consider this example:

#include <string>
#include <iostream>
#include <cilk/cilk.h>

int main()
    std::string alphabet;
    cilk_for(char letter = 'A'; letter <= 'Z'; ++letter) {
        alphabet += letter;
    std::cout << alphabet << "\n";

It has the same race problem as the previous example - simultaneous appends to the string variable - but it also has a much worse problem. If you just add locking code to keep the string appends from stepping on each other, you might get output like:


Locks makes sure that each update happens correctly, independent of all the other updates, but they don't do a thing to make sure that they happen in the right order. In fact, it might seem as though you would have to serialize the tasks to combine the substrings in the right order, which would defeat the point of parallelizing the program.

Reducers have a remarkable property, though: they are guaranteed to get the same result in a parallel computation as in a serial computation. That is, even in a parallel computation, they will combine all the input values in the same order as in the serial computation!

You can make the same changes that you made to the first example:

#include <string>
#include <iostream>
#include <cilk/cilk.h>
#include <cilk/reducer_string.h>

int main()
    cilk::reducer<cilk::op_string> alphabet;
    cilk_for(char letter = 'A'; letter <= 'Z'; ++letter) {
        *alphabet += letter;
    std::cout << alphabet.get_value() << "\n";

When you run this program, it will always print


A Note About These Examples

These examples are intended to illustrate the basic issues involved with reducers. If you actually run them, you will probably find that your parallel program runs slower than the one you started with. Intel Cilk Plus parallelism and reducers are remarkably efficient, but they still have enough overhead to wipe out the advantages of parallelizing ridiculously small tasks. (Alternatively, you may find that the programs are so small that nothing actually executes in parallel.)

In practice, algorithms that show a significant benefit from using reducers will tend to have a loop or recursion where each step has a substantial amount of work that can be done in parallel, and accumulates the result of that work into the final result. Using a reducer for the accumulation solves the synchronization and sequencing problems that would otherwise interfere with parallelizing the entire algorithm.

How Reducers Work

To understand how reducers work, it will be helpful to start with an understanding of the Intel Cilk Plus execution model.

Reduction Algorithms

Reducers are designed to support the parallel execution of a "reduction" algorithm. Reduction algorithms fit the following pattern:

  1. There is an accumulator variable x with an initial value a0.
  2. There is a reduction operation ⊕.
  3. ⊕ is an associative operation, i.e., x ⊕ (y ⊕ z) = (x ⊕ y) ⊕ z.
  4. ⊕ has an identity value I, i.e., x ⊕ I = I ⊕ x = x.
  5. The code repeatedly updates the variable, with each update having the form x = x ⊕ ai. After N updates, x contains a0 ⊕ a1 ⊕ a2 ⊕ … ⊕ aN.


A set of values with an associative operation and an identity value is referred to in mathematics as a "monoid". Common operations that fit this pattern include addition and multiplication, bitwise AND and OR, set union, list concatenation, and string concatenation. Properties 2, 3, and 4 of reduction algorithms mean that a reduction algorithm always has an underlying monoid.


A reducer object manages multiple copies of an accumulator variable, called "views," as strands of execution are spawned and synced. The basic idea is that each strand gets its own view, so strands executing concurrently can update their views independently, without data races.

  1. Initially, a reducer contains a single view, called the "leftmost" view.
  2. A new view is created for the continuation of each spawn. (The child of the spawn inherits the view from the spawning strand.) The new view is initialized to the reduction operation's identity value.
  3. Within a strand, all accesses to the reducer's content refer to the view that was created for that strand.
  4. When two adjacent strands are synced, the reduction operation is applied to their views, leaving the result in the left strand's view, which is inherited by the synced strand. The right strand's view is then destroyed.
  5. When all strands have been synced, the final result of the computation remains in the leftmost view.

As a result of this process, each strand computes a subsequence of the total sequence of operations (ai ⊕ ai+1 ⊕ … ⊕ ai+m) in its view, and when the computation is finished, the leftmost view contains the expected result (a0 ⊕ a1 ⊕ a2 ⊕ … ⊕ aN). The order of the values is the same as in the serial computation, and since the operation is associative, it doesn't matter what subsequence of the computation is performed by each strand.

An Example

Let's work through an example.

#include <iostream>
#include <cilk/cilk.h>
#include <cilk/reducer_string.h>

typedef cilk::reducer< cilk::op_or<unsigned long> > Reducer;

int main()
    Reducer a("((");            // 1
    cilk_spawn abcd(a);         // 2
               efgh(a);         // 3
    cilk_sync;                  // 14
    a->append("))");            // 15
    std::cout << a.get_value()
              << "\n";

void abcd(Reducer& x)
    cilk_spawn ab(x);           // 3
               cd(x);           // 5
    cilk_sync;                  // 7

void ab(Reducer& x)
    x->append("a");             // 4

void cd(Reducer& x)
    x->append("c");             // 6

void efgh(Reducer& x)
    cilk_spawn ef(x);           // 9
               gh(x);           // 11
    cilk_sync;                  // 13

void ef(Reducer& x)
    x->append("e");             // 10

void gh(Reducer& x)
    x->append("g");             // 12

The reduction operation for a string reducer is string concatenation, which is an associative operation. (The view operation view.append(x) is equivalent to view = view + string(x).) The identity value is the empty string.

  1. The constructor creates a reducer with a leftmost view initialized to "((".
  2. The spawned strand for abcd() inherits the leftmost view.
  3. The spawned strand for ab() inherits the leftmost view.
  4. ab() updates the value of the leftmost view first to "((a", then to "((ab".
  5. cd() runs in the continuation strand, which gets a new view, initialized to the string identity value, "". Call this view 1.
  6. cd() updates the value of view 1 first to "c", then to "cd".
  7. The leftmost view and view 1 are reduced: the value of view 1 is appended to the leftmost view, leaving "((abcd" in the leftmost view. View 1 is destroyed, and the strand which returns from abcd() inherits the leftmost view.
  8. efgh() runs in the continuation strand, which gets a new view, initialized to the string identity value, "". Call this view 2.
  9. The spawned strand for ef() inherits view 2.
  10. ef() updates the value of view 2 first to "e", then to "ef".
  11. gh() runs in the continuation strand, which gets a new view, initialized to the string identity value, "". Call this view 3.
  12. gh() updates the value of view 3 first to "g", then to "gh".
  13. View 2 and view 3 are reduced: the value of view 3 is appended to view 2, leaving "efgh" in view 2. View 3 is destroyed, and the strand which returns from efgh() inherits view 2.
  14. The leftmost view and view 2 are reduced: the value of view 2 is appended to the leftmost view, leaving "((abcdefgh" in the leftmost view. View 2 is destroyed, and the strand which continues from the sync inherits the leftmost view.
  15. The value of the leftmost view is updated to "((abcdefgh))", and that is what is printed.

Technical Details

The description of view management above is simplified considerably. This section provides the actual view creation and merging rules, as well as an explanation of why having an underlying monoid is necessary to guarantee correct reducer behavior.

You can safely skip ahead to Using Reducers if you aren't interested in these details.


The Parallelism Graph

The parallelism in an execution of a Intel Cilk Plus program can be represented by a parallelism graph. This is a directed acyclic graph (DAG) whose vertices represent the parallelism events in the program execution and whose edges represent the serial execution sequences between parallelism events, which we refer as the strands of the program execution. The kinds of vertices are:

(This representation works equally well for programs with cilk_for loops, which are implemented by spawning groups of loop iterations and then syncing after the loop.)

Every spawn vertex is associated with a unique sync vertex, but a sync vertex can be associated with multiple spawn vertices.

Note that the parallelism graph is a representation of a particular execution of a program, not a representation of the source program structure like a traditional control flow graph. Also note that the graph represents the available parallelism in the execution - it is independent of what steals actually occur during the execution.

Strand Ordering

We say that two strands are serially ordered if there is a path in the parallelism graph that contains the edges representing both strands. This means that regardless of what steals occur during the execution, one of the strands will be completely executed before the other.

Conversely, if there is no path in the parallelism graph that contains the edges representing both strands, then the strands are parallel, and could execute concurrently, depending on what steals occur. This will be the case if one of the edges occurs on a path from some spawn's child edge to its corresponding sync, and the other edge occurs on a path from the same spawn's continuation edge to the same sync.

If two strands are parallel, we say that the one on the path from the child edge is to the left of the one on the path from the continuation edge. (The left strand would be executed before the right strand if the program were executed serially.) If strand A is to the left of strand B, and strand B is to the left of strand C, then strand A is to the left of strand C.

Two strands are adjacent if they are parallel and there is no other strand which is to the left of one of them and to the right of the other.

View Management Details

Lazy View Creation

The reason for creating private view instances for strands is to eliminate data races. A strand does not really need its own instance of a view unless (1) it actually accesses the view, and (2) it executes in parallel with some other strand. Creating, managing, and merging views is not terribly expensive, but it isn't free, so the scheduler avoids creating views unnecessarily. The actual view creation rule is:

  1. When a reducer is created, a leftmost view is created for it, which becomes the view for the strand it was created on.
  2. When a spawn occurs, both the spawned strand and the continuation inherit the spawning strand's view (if it has one).
  3. When a continuation is stolen, its view reference is erased; it then does not have any view.
  4. When an attempt is made to access the view of a strand that does not have a view, a new view is created for that strand, and initialized to the identity value.

Merging Views

When a strand terminates, its view is saved until an adjacent strand has also terminated. When two adjacent strands have both terminated, their views are merged:

  1. If neither strand had a view, then there is still no view.
  2. If only one strand had a view, then that is the merged view.
  3. If both strands had views, then their value are combined with the reduction operation. The resulting value is left in the left view, and the right view is destroyed.

When all the strands entering a cilk_sync have terminated and their views have been merged, the merged view becomes the view of the strand coming out of the cilk_sync. This will be the same as the view of the strand that entered the cilk_spawn.

It is guaranteed that if a continuation is not stolen, then no steals will occur in the spawned function, either, so when the spawned function returns, the continuation executes using the same view as the spawned function. In this case, there is no waiting and no merge at the cilk_sync.

Why Reducers Need a Monoid

We said above that every reducer is built around a monoid, which is a set of values with an associative operation and an identity value. Let's look at some sample code to see why the identity value and the associativity property are both necessary to guarantee that parallel execution using the reducer will give the same result as serial execution with a simple accumulator variable.

void t1(cilk::reducer<cilk::op_string> &r) {
    *r += 'A';
    *r += 'B';
    *r += 'G';
void t2(cilk::reducer<cilk::op_string> &r) {
    *r += 'H';
    *r += 'I';
    *r += 'M';
void t3(cilk::reducer<cilk::op_string> &r) {
    *r += 'N';
    *r += 'O';
    *r += 'T';
void t4(cilk::reducer<cilk::op_string> &r) {
    *r += 'U';
    *r += 'V';
    *r += 'Z';

int main() {
    cilk::reducer<cilk::op_string> r;
    cilk_spawn t1(r);
    cilk_spawn t2(r);
    cilk_spawn t3(r);
    cout << r.get_value() << "\n";

Suppose that all of the continuations are stolen, so that t1, t2, t3, and t4 execute in parallel and each get their own views. The left-to-right ordering will be t1, t2, t3, t4.


In the serial execution of the program, the reducer would contain "ABCDEFG" after the assignments in t1() and "ABCDEFGHIJKLM" after the assignments in t2(). In the parallel execution, t1() and t2() execute on separate strands which each get their own views, which we expect to contain "ABCDEFG" and "HIJKLM" when they reach the cilk_sync. t1 has the leftmost view, which is initialized to the empty string when the reducer is created. t2's view is created when the continuation of the cilk_spawn t1() is stolen. For t2's view to contain "HIJKLM" at the end of t2, it must be initialized to the string concatenation identity value, which is the empty string, when it is created.

More generally, if a non-leftmost strand executes the assignments

*r = *r ⊕ a1
*r = *r ⊕ a2
*r = *r ⊕ an

then we expect the final value of its view to be a1 ⊕ a2 ⊕ ... ⊕ an. If its initial value is x, then its final value will actually be x ⊕ a1 ⊕ a2 ⊕ ... ⊕ an, which is equal to a1 ⊕ a2 ⊕ ... ⊕ an only if x is the identity value for the operation.

In other words, if the reducer's operator did not have an identity value, then there would be no way to give a non-leftmost view an initial value such that it ended up with the correct final value.


There are two sources of non-determinism in parallel execution.

First, how the serial sequence of reducer operations is split up into parallel subsequences depends on which spawn continuations are stolen (and, consequently, what views are created). In our example:

Second, the order in which the results of parallel computations (i.e., the values of the views) are combined depends on the order in which the parallel strands reach the sync point. Considering our example with three steals and four views again:

Note that both kinds of non-determinism re-associate the reducer operations while leaving their left-to-right order unchanged. Thus, the final result in the reducer after a parallel computation is guaranteed to be the same as after a serial computation if and only if the reducer operations are associative.

How It Works

Reductions are accomplished by the collaboration of three components: a monoid, a view, and a reducer.

The `Monoid` Concept

To be documented...

Using Reducers

To be documented...

Creating New Reducers

To be documented...

Reducers in C

Reducers can also be created and used (rather less elegantly) in C. See Creating and Using Reducers in C.

Library Reducers

Arithmetic Reducers

Container Reducers

Other Reducers

Legacy Reducer Wrappers

To be documented...
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