Guy Lewis Steele's Wordsplit and the Reducers


UPDATE: 05 September 2013

Guy Lewis Steele’s Wordsplit, Redux, in which we address the issue raised below.

UPDATE: 04 September 2013

A reader pointed out an issue with the benchmark, namely some lazy seqs not being realized. I updated the code and post. Now the parallel speedup, while existing, is not as impressive as previously measured. The initial observations regarding the use of reducers for this problem are still relevant. More work to come.

At the 2009 ICFP, Guy L. Steel gave the talk Organizing Functional Code for Parallel Execution: or, foldl and foldr Considered Slightly Harmful (slides) where he described algebraic properties necessary for parallel execution. One example was a wordsplit algorithm composed of associative operations, thus parallelizable.

This talk and Haskell’s Iteratee library inspired Rich Hickey to add reducers to the core Clojure library.

So can we port GLS’s wordsplit from Fortress to Clojure and run it on clojure.core.reducers/fold?

Yes, but you won’t like the results!

That’s the bad news. The good news is we can run it on a variation of fold for a parallel speed boost.


All code snippets here are REPL-friendly.
You can also find the project here:

the port

On Slide 58 GLS shows an iterative word-split. The fastest such split near-to-hand is java.util.regex.Pattern::split. You might try besting it, but chances of success are sad. So …

(require '[clojure.string :as str])
(import '[java.util.regex Pattern])

(set! *warn-on-reflection* true)

(defn split [^Pattern re s]
  (remove str/blank? (str/split s re)))

(split #"\s" "The\n quick   brown fox  ")
;; => ("The" "quick" "brown" "fox")

The heart of GLS’ parallel word-split is an associatively composable abstraction for text segments.

We achieve parallelism by arbitrarily partitioning the larger text into smaller pieces for splitting. As these smaller pieces are represented by the abstraction, we combine after splitting those pieces.

(defrecord Chunk [s])
(defrecord Segment [l m r])

(defn chunk
  ([a1] (->Chunk (str a1)))
  ([] (chunk ""))
  ([a1 a2] (chunk (str a1 a2))))

(defn segment
  ([l m r] (->Segment l m r))
  ([] (segment "" [] "")))

(def ^:const C-ZERO (chunk))
(def ^:const S-ZERO (segment))

Chunks have a string with no word boundaries.

Segments have three ordered parts:

Chunks and Segments also have zero or identity values (i.e combining the respective zero value with another Chunk or Segment produces the same Chunk or Segment).

how to combine?

The Fortress example uses a double-dispatch method, ⊕. Clojure doesn’t have double-dispatch per-se, but it has something more powerful - multimethods.

(defn maybe-word [s]
  (if (str/blank? s)

(defmulti plus #(map type %&))

(defmethod plus '() []

(defmethod plus [Chunk Chunk] [^Chunk a ^Chunk b]
  (chunk (.-s a) (.-s b)))

(defmethod plus [Chunk Segment] [^Chunk a ^Segment b]
  (segment (str (.-s a) (.-l b))
           (.-m b)
           (.-r b)))

(defmethod plus [Segment Chunk] [^Segment a ^Chunk b]
  (segment (.-l a)
           (.-m a)
           (str (.-r a) (.-s b))))

(defmethod plus [Segment Segment] [^Segment a ^Segment b]
  (segment (.-l a)
           (vec (concat (.-m a)
                          (maybe-word (str (.-r a) (.-l b)))
                          (.-m b)))
           (.-r b)))

Note that plus invoked with no arguments returns the Chunk zero value. This is an important property of combine functions for use with clojure.core.reducers/fold.

To recap, we have an abstraction for text sections that has:

  1. an associative, binary combine operation between sections - order doesn’t matter
  2. zero values

Sniff, sniff. I smell a monoid.


This is fantastic because fold needs exactly that - a monoid.

Out-of-the-box, fold doesn’t work on strings, but that is easily fixed.

First, we create a helper foldstr based on foldvec. There is one key difference beyond replacing subvec with .substring; we will discuss this later.

 1(require '[clojure.core.reducers :as r])
 3;; In clojure, "private" isn't.
 4(def ^:const fjinvoke @#'r/fjinvoke)
 5(def ^:const fjfork @#'r/fjfork)
 6(def ^:const fjjoin @#'r/fjjoin)
 7(def ^:const fjtask @#'r/fjtask)
 9(defn foldstr [^String s n combinef sequential-reducef]
10  (cond
11   (empty? s) (combinef)
12   (<= (count s) n) (sequential-reducef s)
13   :else
14   (let [split (quot (count s) 2)
15         v1 (.substring s 0 split)
16         v2 (.substring s split (count s))
17         fc (fn [child] #(foldstr child n combinef sequential-reducef))]
18     (fjinvoke #(let [f1 (fc v1)
19                      t2 (fjtask (fc v2))]
20                  (fjfork t2)
21                  (combinef (f1) (fjjoin t2)))))))

Finally, some protocol magic and strings are foldable!

(extend-protocol r/CollFold
  (coll-fold [s n combinef reducef]
    (foldstr s n combinef #(reduce reducef (combinef) %))))

the agony

And we’ve arrived. GLS word split on Clojure reducers!

(defn reducer [result ch]
  (plus result (if (= ch \space)
                 (chunk ch))))

(defn wordstate->words [{:keys [s l m r] :as word-state}]
  (condp = (type word-state)
    Chunk (maybe-word s)
    Segment (concat (maybe-word l) m (maybe-word r))))

(defn split-reducers
  ([s] (split-reducers 8192 s))
  ([chunk-size s]
     (wordstate->words (r/fold chunk-size plus reducer s))))

Does it work?

(split-reducers " The quick   brown fox  ")
;; => ("The" "quick" "brown" "fox")
(split-reducers 2 " The quick   brown fox  ")
;; => ("The" "quick" "brown" "fox")


(split-reducers a-moderate-amount-of-text)
;; => weeping and wailing and gc thrashing of teeth

Great multi-order-of-magnitude-worse-than-iterative-split Batman!


So what gives?

Clojure reducers/fold diverge from GLS talk in one key way.
It uses the combine function (plus) at the partition boundaries and at the leaves. You can specify a reducing function separate from the combine function, but each intermediate reduction must be consumable by combine.

It is an elegant simplification that works well for some domains, especially when the accumulator is a primitive.
But the simplification is poorly suited if the reduce function allocates lots of heap memory for each reduction step. Welcome to garbage-collection purgatory.

batch it in place

Plusing each char onto the accumulated result is a non-starter.

What we need is a function that jumps straight from a string to a wordstate object without all the intermediate Chunks and Segments.

(defn string->wordstate [^Pattern re ^String s]
  (let [splits (str/split s re)
        splitcount (count splits)]
    (if (= 0 splitcount)
      (let [last-split (nth splits (dec splitcount))
            end-match? (not= (.lastIndexOf s ^String last-split)
                             (- (count s) (count last-split)))]
        (if (and (= 1 splitcount) (not end-match?))
          (chunk last-split)
          (segment (first splits)
                   (->> (subvec splits 1 (- splitcount (if end-match? 0 1)))
                        (remove str/blank?))
                   (if end-match? "" last-split)))))))

That should make your eyes bleed a bit.

Pattern::split inconsistently represents word boundary matches in the splits array, so we must resort to some deviousness. But the payoff? string->wordstate minimizes intermediate throwaway objects.

i wanna fold

Now that we have our fancy “batch-reduce”, can we go back to fold?

Nope. Fold still takes a combine function and forces an element-wise reduce at the leaves.

So fold is out.

But we can still use foldstr. Remember our key deviation from foldvec?

;; in foldvec ...
(<= (count v) n) (reduce reducef (combinef) v)

;; vs. in foldstr ...
(<= (count s) n) (sequential-reducef s)

take two

(defn guess-chunk-size [s]
  (/ (count s) (.availableProcessors (Runtime/getRuntime))))

(defn psplit
  ([^Pattern re s]
     (psplit re (guess-chunk-size s) s))
  ([^Pattern re chunk-size s]
     (wordstate->words (foldstr s chunk-size plus #(string->wordstate re %)))))

And now we have parallel split.

To test this, we’ll leverage criterium and data.generators to generate random input.

;; Test Setup:
;; amd64 Linux 3.2.0-52-generic, 6-cores
;; Java HotSpot(TM) 64-Bit Server VM 23.21-b01
;; Runtime arguments: -XX:-UseConcMarkSweepGC
;; note: caution when testing on leiningen,
;;       use :jvm-opts ^:replace [...]

(require '[criterium.core :refer :all])
(require '[pmbauer.text.generator :refer [corpus]])

;; avg word length = (1 + 16) / 2 = 8.5
;; avg space length = (1 + 2) / 2 = 1.5
;; avg length of text = (avg word length + avg space length) * nr of words
;; at 16 bits per word 1 million words is 16 * ~10 * 1 million = ~19MB
(def text (corpus 1000000))

(bench (def splits (into [] (split #"\s" text))))
;; => Execution time mean : 247.904786 ms

(bench (def splits (into [] (psplit #"\s" text))))
;; => Execution time mean : 200.225173 ms

Well, that is … better, but a little disappointing. The memory-copying overhead when two Segments are combined is quite significant.

to be continued …

Guy Lewis Steele’s Wordsplit, Redux, in which we address the issue above.

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