Saturday, 4 May 2019

Qi, Lisp and O'Caml compared - performance shootout

An old article by Mark Tarver reproduced here for historical interest:

This test was conducted to determine the relative efficiency and code size of hand-coded Lisp, O'Caml and Qi Turbo-E using the benchmark challenge provided by Jon Harrop.  Dr Harrop also provided the Lisp source written by Andre Thieme, Nathan Forster and Pascal Constanza.
Thanks to many people who corrected these tests and made them possible.
Turbo-E is an unreleased version of Qi which factorises its pattern-matching.
Jon Harrop Challenge Problem
Posted by Dr Harrop
The problem is to simplify symbolic expressions by applying the following rewrite rules from the leaves up:
rational n + rational m -> rational(n + m)
rational n * rational m -> rational(n * m)
symbol x -> symbol x
0+f -> f
f+0 -> f
0*f -> 0
f*0 -> 0
1*f -> f
f*1 -> f
a+(b+c) -> (a+b)+c
a*(b*c) -> (a*b)*c
Thanks to

Nathan Froyd
Andre Thieme
Pascal Constanza
Dan Bensen

for providing code

Jon Harrop (for both providing code and providing motivation to develop Turbo-E)
Marcus Breing (for correcting my first run)
The Results
The benchmark was to simplify [* x [+ [+ [* 12 0] [+ 23 8]] y]]. 10^7 iterations were used as the benchmark. A 2.6GHz, 500Mb RAM machine was used to drive the test. Windows XP was the OS. Times measure only processor time (not real time) and are rounded to 1/10th of a second.
A 2.6GHz, 500Mb RAM machine was used to drive the test. Windows XP was the OS. Times measure only processor time (not real time) and are rounded to 1/10th of a second.
SBCL 1.0 
Qi Turbo-E under SBCL 1.0 
O'Caml 3.09.3 
time vs OCaml1.8x1.7x7.5x4.1x2.6x1x
The timer used in Lisp was
(defun tt (N) (gc) (time (test N)))

(defun test (N)
(cond ((zerop N) 0)
(t (simplify *expr*) (test (1- N)))))
took .2 s for 10^7 iterations with (simplify *expr*) removed. All timings were adjusted by deducting .2s from each.
I could run Jon 's solution in interpreted mode, but could not get my O'Caml compiler to work. Based on the ratio of interpreted time to his compiled time, I could construct a projected time.
The Solutions
Here are the solutions; you can download the code here.
Language: Qi
Author: Mark Tarver
Length: 15 lines
(define simplify
  [Op A B] -> (s Op (simplify A) (simplify B))
  A -> A)
(define s
  + M N -> (+ M N)      where (and (number? M) (number? N))
  + 0 F -> F
  + F 0 -> F
  + A [+ B C] -> (simplify [+ [+ A B] C])
  * M N -> (* M N)    where (and (number? M) (number? N))
  * 0 F -> 0
  * F 0 -> 0
  * F 1 -> F
  * 1 F -> F
  * A [* B C] -> (simplify [* [* A B] C])
  Op A B -> [Op A B])
* Note I have used prefix notation for this problem though Dr Harrop's formulation uses infix. The first Lisp solutions followed prefix and so I chose to make my program comparable.*
The generated code is
(DEFUN simplify (V148)
(IF (CONSP V148)
(LET ((Cdr159 (CDR V148)))
(IF (CONSP Cdr159)
(LET ((Cdr158 (CDR Cdr159)))
(IF (CONSP Cdr158)
(IF (NULL (CDR Cdr158))
(s (CAR V148) (simplify (CAR Cdr159))
(simplify (CAR Cdr158))))
(GO tag154))
(GO tag154)))
(GO tag154))))
(RETURN V148))))

(DEFUN s (V149 V150 V151)
(IF (EQ '+ V149)
(RETURN (THE NUMBER (+ V150 V151)))
(IF (EQL 0 V150) (RETURN V151)
(IF (EQL 0 V151) (RETURN V150)
(IF (CONSP V151)
(LET ((Cdr170 (CDR V151)))
(IF (EQ '+ (CAR V151))
(IF (CONSP Cdr170)
(LET ((Cdr169 (CDR Cdr170)))
(IF (CONSP Cdr169)
(IF (NULL (CDR Cdr169))
(CONS '+
(LIST '+ V150
(CAR Cdr170))
(GO tag160))
(GO tag160)))
(GO tag160))
(GO tag160)))
(GO tag160))))))
(IF (EQ '* V149)
(RETURN (THE NUMBER (* V150 V151)))
(IF (EQL 0 V150) (RETURN 0)
(IF (EQL 0 V151) (RETURN 0)
(IF (EQL 1 V151) (RETURN V150)
(IF (EQL 1 V150) (RETURN V151)
(IF (CONSP V151)
(LET ((Cdr183 (CDR V151)))
(IF (EQ '* (CAR V151))
(IF (CONSP Cdr183)
(LET ((Cdr182 (CDR Cdr183)))
(IF (CONSP Cdr182)
(IF (NULL (CDR Cdr182))
(CONS '*
(LIST '* V150
(GO tag171))
(GO tag171)))
(GO tag171))
(GO tag171)))
(GO tag171))))))))
(RETURN (LIST V149 V150 V151))))))
Language: OCaml
Author: Jon Harrop
Length: 15 lines
let rec ( +: ) f g = match f, g with
  | `Int n, `Int m -> `Int (n +/ m)
  | `Int (Int 0), e | e, `Int (Int 0) -> e
  | f, `Add(g, h) -> f +: g +: h
  | f, g -> `Add(f, g)
let rec ( *: ) f g = match f, g with
  | `Int n, `Int m -> `Int (n */ m)
  | `Int (Int 0), e | e, `Int (Int 0) -> `Int (Int 0)
  | `Int (Int 1), e | e, `Int (Int 1) -> e
  | f, `Mul(g, h) -> f *: g *: h
  | f, g -> `Mul(f, g)
let rec simplify = function
  | `Int _ | `Var _ as f -> f
  | `Add (f, g) -> simplify f +: simplify g
  | `Mul (f, g) -> simplify f *: simplify g
Language: Lisp
Author: Andre Thieme
Length: 23 lines
(defun simplify (a)
   (if (atom a)
       (destructuring-bind (op x y) a
        (let* ((f (simplify x))
               (g (simplify y))
               (nf (numberp f))
               (ng (numberp g))
               (+? (eq '+ op))
               (*? (eq '* op)))
            ((and +? nf ng)                   (+ f g))
            ((and +? nf (zerop f))            g)
            ((and +? ng (zerop g))            f)
            ((and (listp g) (eq op (first g)))
             (destructuring-bind (op2 u v) g
               (simplify `(,op (,op ,f ,u) ,v))))
            ((and *? nf ng)                   (* f g))
            ((and *? (or (and nf (zerop f))
                         (and ng (zerop g)))) 0)
            ((and *? nf (= 1 f))              g)
            ((and *? ng (= 1 g))              f)
            (t                                `(,op ,f ,g)))))))
Language: Lisp
Author: Nathan Froyd
Length: 39 lines
(defun simplify-no-redundant-checks (xexpr)
    (if (atom xexpr)
      (let ((op (first xexpr))
            (z (second xexpr))
            (y (third xexpr)))
        (let* ((f (simplify-no-redundant-checks z))
               (g (simplify-no-redundant-checks y))
               (nf (numberp f))
               (ng (numberp g)))
             (if (eq '+ op) (go OPTIMIZE-PLUS) (go TEST-MULTIPLY))
             (when (and nf ng) (return-from simplify-no-redundant-checks (+ f g)))
             (when (eql f 0) (return-from simplify-no-redundant-checks g))
             (when (eql g 0) (return-from simplify-no-redundant-checks f))
             (go REARRANGE-EXPR)
             (unless (eq '* op) (go REARRANGE-EXPR))
             (when (and nf ng) (return-from simplify-no-redundant-checks (* f g)))
             (when (or (eql f 0) (eql g 0)) (return-from simplify-no-redundant-checks 0))
             (when (eql f 1) (return-from simplify-no-redundant-checks g))
             (when (eql g 1) (return-from simplify-no-redundant-checks f))
             (when (and (listp g) (eq op (first g)))
               (let ((op2 (first g))
                     (u (second g))
                     (v (third g)))
                 (declare (ignore op2))
                 (return-from simplify-no-redundant-checks
                   (simplify-no-redundant-checks (list op (list op f u) v)))))
             (if (and (eq f z) (eq g y))
                 (return-from simplify-no-redundant-checks xexpr)
                 (return-from simplify-no-redundant-checks (list op f g))))))))
Language: Lisp
Author: Pascal Constanza
Length: 25 lines
(defstruct add x y)
(defstruct mul x y)
(defgeneric simplify-add (x y)
   (:method ((x number) (y number)) (+ x y))
   (:method ((x (eql 0)) y) y)
   (:method (x (y (eql 0))) x)
   (:method (x (y add))
    (simplify-add (simplify-add x (add-x y)) (add-y y)))
   (:method (x y) (make-add :x x :y y)))
(defgeneric simplify-mul (x y)
    (:method ((x number) (y number)) (* x y))
   (:method ((x (eql 0)) y) 0)
   (:method (x (y (eql 0))) 0)
   (:method ((x (eql 1)) y) y)
   (:method (x (y (eql 1))) x)
   (:method (x (y mul))
    (simplify-mul (simplify-mul x (mul-x y)) (mul-y y)))
   (:method (x y) (make-mul :x x :y y)))
(defgeneric simplify (exp)
   (:method (exp) exp)
   (:method ((exp add))
    (simplify-add (simplify (add-x exp)) (simplify (add-y exp))))
   (:method ((exp mul))
    (simplify-mul (simplify (mul-x exp)) (simplify (mul-y exp)))))
Language: Lisp
Author: Dan Bensen
Length: 34 lines
(defmacro defop (func op op-symbl ident zero-case)
  (let ((e1 (gensym "E1"))
         (e2 (gensym "E2")))
     `(defun ,func (,e1 ,e2)
       (declare (optimize (speed 3)))
        ,(delete :no-case
          `(case ,e1
            (,ident ,e2)
            (t ,(delete :no-case
               `(case ,e2
                (,ident ,e1)
                (t (cond
                   ((and (rationalp ,e1)
                         (rationalp ,e2))
                    (,op ,e1 ,e2))
                   ((atom ,e2)
                    (list ,e1 ,op-symbl ,e2))
                   (t (case
                        (cadr ,e2)
                      (,op-symbl (,func (,func ,e1 (car ,e2))
                                        (caddr ,e2)))
                      (t  (list ,e1 ,op-symbl ,e2))))))))))))))
(defop apply+ + '+ 0 :no-case)
(defop apply* * '* 1 ( 0  0 ))
(defun simplify (expr)
   (if (atom expr)
     (let ((e1 (simplify (car   expr)))
           (e2 (simplify (caddr expr))))
       (case (cadr expr)
             ('+ (apply+ e1 e2))
             ('* (apply* e1 e2))))))

Monday, 29 April 2019

The “Blub Paradox” and C++

Here is another of my answers from Stack Overflow that is getting down votes:

is there some powerful language feature or idiom that you make use of in a language that would be hard to conceptualize or implement if you were writing only in c++?
Are there any useful concepts or techniques that you have encountered in other languages that you would have found difficult to conceptualize had you been writing or "thinking" in c++?
C++ makes many approaches intractable. I would go so far as to say that most of programming is hard to conceptualize if you limit yourself to C++. Here are some examples of problems that are much more easily solved in ways that C++ makes hard.

Register allocation and calling conventions

Many people think of C++ as a bare metal low level language but it really isn't. By abstracting away important details of the machine, C++ makes it hard to conceptualize practicalities like register allocation and calling conventions.
To learn about concepts like these I recommend having a go at some assembly language programming and check out this article about ARM code generation quality.

Run-time code generation

If you only know C++ then you probably think that templates are the be-all and end-all of metaprogramming. They aren't. In fact, they are an objectively bad tool for metaprogramming. Any program that manipulates another program is a metaprogram, including interpreters, compilers, computer algebra systems and theorem provers. Run-time code generation is a useful feature for this.
I recommend firing up a Scheme implementation and playing with EVAL to learn about metacircular evaluation.

Manipulating trees

Trees are everywhere in programming. In parsing you have abstract syntax trees. In compilers you have IRs that are trees. In graphics and GUI programming you have scene trees.
This "Ridiculously Simple JSON Parser for C++" weighs in at just 484 LOC which is very small for C++. Now compare it with my own simple JSON parser which weighs in at just 60 LOC of F#. The difference is primarily because ML's algebraic datatypes and pattern matching (including active patterns) make it vastly easier to manipulate trees.
Check out red-black trees in OCaml too.

Purely functional data structures

Lack of GC in C++ makes it practically impossible to adopt some useful approaches. Purely functional data structures are one such tool.
For example, check out this 47-line regular expression matcher in OCaml. The brevity is due largely to the extensive use of purely functional data structures. In particular, the use of dictionaries with keys that are sets. That is really hard to do in C++ because the stdlib dictionaries and sets are all mutable but you cannot mutate a dictionary's keys or you break the collection.
Logic programming and undo buffers are other practical examples where purely functional data structures make something that is hard in C++ really easy in other languages.

Tail calls

Not only does C++ not guarantee tail calls but RAII is fundamentally at odds with it because destructors get in the way of a call in tail position. Tail calls let you make an unbounded number of function calls using only a bounded amount of stack space. This is great for implementing state machines, including extensible state machines and it is a great "get out of jail free" card in many otherwise-awkward circumstances.
For example, check out this implementation of the 0-1 knapsack problem using continuation-passing style with memoization in F# from the finance industry. When you have tail calls, continuation passing style can be an obvious solution but C++ makes it intractable.


Another obvious example is concurrent programming. Although this is entirely possible in C++ it is extremely error prone compared to other tools, most notably communicating sequential processes as seen in languages like Erlang, Scala and F#.

Monday, 22 April 2019

In terms of performance speed, is Swift faster than Java?

This is an old 2017 answer of mine from Quora that has been deleted by moderators for violating their rules, kept here for historical interest:
Many people here are claiming "yes" that Swift is fast. However, all of the benchmark results I can find indicate that Swift is much slower than most other languages, up to 24x slower than C++:
  • n-body: swift 24.09s vs Java 22.6s.
  • fannuck-redux: swift 59.5s vs Java 17.41s. Swift is 3.4x slower than Java.
  • spectral-norm: swift 15.7s vs Java 4.28s. Swift is 3.7x slower than Java.
EDIT: The shootout appears to have been updated. The new Swift code is still memory unsafe and still compiled in memory unsafe -Ounchecked mode. Swift now beats Java on 3/7 tasks (fannkuch-redux, mandelbrot and binary-trees), roughly draws on two (nbody and fasta-redux) and loses on two (fasta and spectral norm). Shame there is no memory safe Swift for comparison. I’d also note that many of these benchmarks are apples vs oranges comparisons with, for example, the Java and Swift implementations of binary-trees using completely different algorithms and data structures.
Other (non-Java) benchmarks found:
My impression is that Swift was vastly slower than almost all other languages at many common tasks when it was first released but it has improved very rapidly. However, it still seems to be several times slower than C++ in many cases and, therefore, I expect it is still significantly slower than languages like F# and Scala.
I am also concerned about many of the benchmarks being used. The shootout is notoriously bad science with submissions subjectively "de-optimised" by its owner for being too fast rendering the results total garbage. Numerical benchmarks like DGEMM and FFTs are largely irrelevant. I am much more interested in symbolic performance, not just because that is more relevant to the code I write but because it will stress Swift's reference counting garbage collection which I suspect will be its Achilles heel. The JSON serialization benchmark was very interesting to me as a consequence. I am also disturbed by the use of optimisations that remove memory safety in Swift (-Ounsafe) and the use of inaccurate numerics in C++ (-ffast-math).
A post on Hacker News (Swift Performance: Too Slow for Production) says that short-lived objects in the JSON serializer are to blame for the poor performance. In other words, the reference counted memory management is the problem as I suspected.