As part of an advent of code challenge, I've written the following functions in Haskell:
simulateUntilRepeat_int a b i = if (a /= b) then (simulateUntilRepeat_int a (updateCycle b) (i+1)) else i
simulateUntilRepeat a = simulateUntilRepeat_int a (updateCycle a) 1
The purpose of this is to take a list of moons and simulate their movement until they resume their original position, returning the number of cycles it took for them to get there. (the function updateCycle does one iteration of the simulation). However, when I attempt to run this it uses all available memory and then gets killed by the operating system. The question does admit that this may take a very large number of cycles.
Googling around about this problem I find the usual fix is to make some of the parameters strict, but I think I've experimented with all possible permutations of strictness on the parameters to no avail. By the looks of this function, I'd have anticipated the compiler would be able to use the tail recursion optimisation and turn it into a loop, but this seems to not be happening somehow.
A friend of mine, who is knowledgeable in haskell suggested changing the form of the function to the following:
f a b0 = length (takeWhile (/= a) (iterate updateCycle b0))
But doing this didn't fix it either, leaving me out of ideas.
The comments are undoubtedly correct that your approach is not the intended solution method.
However, the functions you've posted would not, in and of themselves, cause a memory leak, fail to tail recurse, or lead to poor performance. Given your code above plus the definitions:
updateCycle 4686774942 = 0
updateCycle n = n+1
main = do
print $ simulateUntilRepeat (0 :: Int)
and compiling with -O2, the program runs in constant memory on my laptop in about 30 seconds. Adding explicit type signatures to use Int in place of Integer for the iteration count:
simulateUntilRepeat_int :: Int -> Int -> Int -> Int
simulateUntilRepeat :: Int -> Int
it runs in about 2.4 seconds.
So, to understand why your program is gobbling all available memory or why your strictness annotations failed to make a difference, it would probably be necessary to see the whole working program (or preferably a minimal example that illustrates the performance problem). If the program is short, and the question is "why is the performance of this program totally unreasonable?" instead of "how can I optimize my program to run as fast as possible?", it might still be a good SO question. Otherwise, the Code Review site might be better -- you can post a larger program there and ask for general performance advice, and that's considered on-topic for that site.
I am trying to learn recursion. for the starting problem of it , which is calculating factorial of a number I have accomplished it using two methods.
the first one being the normal usual approach.
the second one i have tried to do something different.
in the second one i return the value of n at the end rather than getting the starting value as in the first one which uses backtracking.
my question is that does my approach has any advantages over backtracking?
if asked to chose which one would be a better solution?
//first one is ,
ll factorial(int n)
{
if(n==1)
return 1 ;
return n*factorial(n-1) ;
}
int main()
{
factorial(25) ;
return 0 ;
}
// second one is ,
ll fact(ll i,ll n)
{
if(i==0)return n ;
n=(n*i)
i--;
n=fact(i,n);
}
int main()
{
int n ;
cin>>n ;
cout<<fact(n,1) ;
return 0 ;
}
// ll is long long int
First of all I want to point out that premature optimization at the expense of readibility is almost always a mistake, especially when the need for optimization comes from intuition and not from measurements.
"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%" - Donald Knuth
But let's say we care about the 3% in this case, because all our program ever does is compute lots of factorials. To keep it short: You will never be smarter than the compiler.
If this seems slightly crazy then this definitely applies to you and you should stop thinking about 'micromanaging/optimizing your code'. If you are a very skilled C++ programmer this will still apply to you in most cases, but you will recognize the opportunities to help your compiler out.
To back this up with some fact, we can compile the code (with automatic optimization) and (roughly) compare the assembly output. I will use the wonderful website godbolt.org
Don't be discouraged by the crazy assembler code, we don't need to understand it. But we can see that both methods
are basically the same length when compiled as assembler code
contain almost the same instructions
So to recap, readability should be your number one priority. In case a speed measurement shows that this one part of your code really is a big performance problem, really think about if you can make a change that structurally improves the algorithm (i.e. by decreasing the complexity). Otherwise your compiler will take care of it for you.
20-30 years ago arithmetic operations like division were one of the most costly operations for CPUs. Saving one division in a piece of repeatedly called code was a significant performance gain. But today CPUs have fast arithmetic operations and since they heavily use instruction pipelining, conditionals can disrupt efficient execution. If I want to optimize code for speed, should I prefer arithmetic operations in favor of conditionals?
Example 1
Suppose we want to implement operations modulo n. What will perform better:
int c = a + b;
result = (c >= n) ? (c - n) : c;
or
result = (a + b) % n;
?
Example 2
Let's say we're converting 24-bit signed numbers to 32-bit. What will perform better:
int32_t x = ...;
result = (x & 0x800000) ? (x | 0xff000000) : x;
or
result = (x << 8) >> 8;
?
All the low hanging fruits are already picked and pickled by authors of compilers and guys who build hardware. If you are the kind of person who needs to ask such question, you are unlikely to be able to optimize anything by hand.
While 20 years ago it was possible for a relatively competent programmer to make some optimizations by dropping down to assembly, nowadays it is the domain of experts, specializing in the target architecture; also, optimization requires not only knowing the program, but knowing the data it will process. Everything comes down to heuristics, tests under different conditions etc.
Simple performance questions no longer have simple answers.
If you want to optimise for speed, you should just tell your compiler to optimise for speed. Modern compilers will generally outperform you in this area.
I've sometimes been surprised trying to relate assembly code back to the original source for this very reason.
Optimise your source code for readability and let the compiler do what it's best at.
I expect that in example #1, the first will perform better. The compiler will probably apply some bit-twiddling trick to avoid a branch. But you're taking advantage of knowledge that it's extremely unlikely that the compiler can deduce: namely that the sum is always in the range [0:2*n-2] so a single subtraction will suffice.
For example #2, the second way is both faster on modern CPUs and simpler to follow. A judicious comment would be appropriate in either version. (I wouldn't be surprised to see the compiler convert the first version into the second.)
Firstly, apologies for the vague title, but I'm not sure exactly what I'm asking here(!).
After encountering Haskell at university, I've recently started using it in anger and so am working through the Project Euler problems as an extended Hello World, really. I've encountered a bug in one of my answers that seems to suggest a misunderstanding of a fundamental part of the language, and it's not something I could work out from the tutorials, nor something I know enough about to start Googling for.
A brief description of the issue itself - the solution relates to primes, so I wanted an infinite list of prime numbers which I implemented (without optimisation yet!) thusly:
isPrime :: Int -> Bool
isPrime n = isPrime' 2 n
where isPrime' p n | p >= n = True
| n `mod` p == 0 = False
| otherwise = isPrime' (p+1) n
primes :: [Int]
primes = filter isPrime [2..]
Since infinite lists can be a little tedious to evaluate, I'll of course be using lazy evaluation to ensure that just the bits I want get evaulatued. So, for example, I can ask GHCI for the prime numbers less than 100:
*Main> takeWhile (< 100) primes
[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97]
Now here's the part that I don't understand at all - when the upper limit gets large enough, I get no answers back at all. In particular:
*Main> takeWhile (< 4000000000) primes
[]
This isn't a problem with takeWhile itself, or the partially-applied function, as takeWhile (< 4000000000) [2..] works as I would expect. It's not a problem with my use of filter (within the definition of primes), since takeWhile (< 4000000000) (filter even [2..]) also returns the expected result.
Through binary search I found that the greatest upper limit that works is 2^31 - 1, so this would certainly seem to imply some kind of space-based constraint (i.e. largest positive signed integer). However:
I was of the impression that Haskell had no language limits on the size of integers, and they were bounded only by the amount of free memory.
This number only appears in the less-than predicate, which I know works as expected in at least some cases. Surely when it's applied to elements of a list, it shouldn't care where they come from? Looking solely at the first element, I know that the predicate returns true for the input 2 when it comes from filter even [2..]; I know that primes returns 2 as its first element. So how can my list be empty, how is this predicate failing "for some values of 2"?
Any thoughts would be grateful as I don't have enough experience to know where to start with this one. Thanks for taking the time to take a look.
There are 2 built-in integral types in haskell: Int and Integer. Integer is the default and is unbounded. Int however is bounded. Since you're explicitly using Int in the type for isPrime 4000000000 is used as an Int and overflows. If you change the type of isPrime to Integer -> Bool or even better Integral a => a -> Bool (read: a function that can take any kind of Integral value and returns a Bool), it will work as expected.
The important thing to take away here (other than the difference between Int and Integer) is that the type of 4000000000 depends on how it is used. If it is used as an argument to a function that takes an Int, it will be an Int (and on 32-bit systems it will overflow). If it is used as an argument to a function that takes an Integer, it will be an Integer (and never overflow). If it is used as an argument to a function that takes any kind of Integral, it will also be an Integer because Integer is the default instance of Integral.
That's an easy answer (...which I see has already been partly answered) - "premature specialization".
The first part of your definition, the type signature, specifies:
isPrime :: Int -> Bool
An Int is not just a "shortcut" way to say Integer - they are different types! To be a nit-picker (which in turn invites every one else to tear apart the many places here, where I am not accurate), there are never "different values of 2" - it has to be of type Int, because that's how you specified the function (you compare 2 to the function's argument n and you're only allowed to compare values of the same type, so your 2 is "pinned down" to the Int type.
Oh, and just as a warning, the Int type is a type just rife with corner case potential. If your system is built in a 64-bit environment, then your Int will also be based on a 64-bit representation, and your example will work up to 2^63-1, instead of 2^31-1 as yours did. Note my phrasing: I have a 64-bit computer with an MS Windows OS, which means that there is not yet an official 64-bit MinGW toolchain - my OS is 64-bit, but the GHC version I have was compiled with 32-bit libraries, so it has 32-bit-based Ints. When I use Linux, even in a VM, it has a 64-bit toolchain, so Ints are 64 bits. If you had used one of those, you may not have even noticed the behavior!
So, I guess that's just one more reason to be careful when reasoning about your types. (Especially in Haskell, anyway....)
In one of my first attempts to create functional code, I ran into a performance issue.
I started with a common task - multiply the elements of two arrays and sum up the results:
var first:Array[Float] ...
var second:Array[Float] ...
var sum=0f;
for (ix<-0 until first.length)
sum += first(ix) * second(ix);
Here is how I reformed the work:
sum = first.zip(second).map{ case (a,b) => a*b }.reduceLeft(_+_)
When I benchmarked the two approaches, the second method takes 40 times as long to complete!
Why does the second method take so much longer? How can I reform the work to be both speed efficient and use functional programming style?
The main reasons why these two examples are so different in speed are:
the faster one doesn't use any generics, so it doesn't face boxing/unboxing.
the faster one doesn't create temporary collections and, thus, avoids extra memory copies.
Let's consider the slower one by parts. First:
first.zip(second)
That creates a new array, an array of Tuple2. It will copy all elements from both arrays into Tuple2 objects, and then copy a reference to each of these objects into a third array. Now, notice that Tuple2 is parameterized, so it can't store Float directly. Instead, new instances of java.lang.Float are created for each number, the numbers are stored in them, and then a reference for each of them is stored into the Tuple2.
map{ case (a,b) => a*b }
Now a fourth array is created. To compute the values of these elements, it needs to read the reference to the tuple from the third array, read the reference to the java.lang.Float stored in them, read the numbers, multiply, create a new java.lang.Float to store the result, and then pass this reference back, which will be de-referenced again to be stored in the array (arrays are not type-erased).
We are not finished, though. Here's the next part:
reduceLeft(_+_)
That one is relatively harmless, except that it still do boxing/unboxing and java.lang.Float creation at iteration, since reduceLeft receives a Function2, which is parameterized.
Scala 2.8 introduces a feature called specialization which will get rid of a lot of these boxing/unboxing. But let's consider alternative faster versions. We could, for instance, do map and reduceLeft in a single step:
sum = first.zip(second).foldLeft(0f) { case (a, (b, c)) => a + b * c }
We could use view (Scala 2.8) or projection (Scala 2.7) to avoid creating intermediary collections altogether:
sum = first.view.zip(second).map{ case (a,b) => a*b }.reduceLeft(_+_)
This last one doesn't save much, actually, so I think the non-strictness if being "lost" pretty fast (ie, one of these methods is strict even in a view). There's also an alternative way of zipping that is non-strict (ie, avoids some intermediary results) by default:
sum = (first,second).zipped.map{ case (a,b) => a*b }.reduceLeft(_+_)
This gives much better result that the former. Better than the foldLeft one, though not by much. Unfortunately, we can't combined zipped with foldLeft because the former doesn't support the latter.
The last one is the fastest I could get. Faster than that, only with specialization. Now, Function2 happens to be specialized, but for Int, Long and Double. The other primitives were left out, as specialization increases code size rather dramatically for each primitive. On my tests, though Double is actually taking longer. That might be a result of it being twice the size, or it might be something I'm doing wrong.
So, in the end, the problem is a combination of factors, including producing intermediary copies of elements, and the way Java (JVM) handles primitives and generics. A similar code in Haskell using supercompilation would be equal to anything short of assembler. On the JVM, you have to be aware of the trade-offs and be prepared to optimize critical code.
I did some variations of this with Scala 2.8. The loop version is as you write but the
functional version is slightly different:
(xs, ys).zipped map (_ * _) reduceLeft(_ + _)
I ran with Double instead of Float, because currently specialization only kicks in for Double. I then tested with arrays and vectors as the carrier type. Furthermore, I tested Boxed variants which work on java.lang.Double's instead of primitive Doubles to measure
the effect of primitive type boxing and unboxing. Here is what I got (running Java 1.6_10 server VM, Scala 2.8 RC1, 5 runs per test).
loopArray 461 437 436 437 435
reduceArray 6573 6544 6718 6828 6554
loopVector 5877 5773 5775 5791 5657
reduceVector 5064 4880 4844 4828 4926
loopArrayBoxed 2627 2551 2569 2537 2546
reduceArrayBoxed 4809 4434 4496 4434 4365
loopVectorBoxed 7577 7450 7456 7463 7432
reduceVectorBoxed 5116 4903 5006 4957 5122
The first thing to notice is that by far the biggest difference is between primitive array loops and primitive array functional reduce. It's about a factor of 15 instead of the 40 you have seen, which reflects improvements in Scala 2.8 over 2.7. Still, primitive array loops are the fastest of all tests whereas primitive array reduces are the slowest. The reason is that primitive Java arrays and generic operations are just not a very good fit. Accessing elements of primitive Java arrays from generic functions requires a lot of boxing/unboxing and sometimes even requires reflection. Future versions of Scala will specialize the Array class and then we should see some improvement. But right now that's what it is.
If you go from arrays to vectors, you notice several things. First, the reduce version is now faster than the imperative loop! This is because vector reduce can make use of efficient bulk operations. Second, vector reduce is faster than array reduce, which illustrates the inherent overhead that arrays of primitive types pose for generic higher-order functions.
If you eliminate the overhead of boxing/unboxing by working only with boxed java.lang.Double values, the picture changes. Now reduce over arrays is a bit less than 2 times slower than looping, instead of the 15 times difference before. That more closely approximates the inherent overhead of the three loops with intermediate data structures instead of the fused loop of the imperative version. Looping over vectors is now by far the slowest solution, whereas reducing over vectors is a little bit slower than reducing over arrays.
So the overall answer is: it depends. If you have tight loops over arrays of primitive values, nothing beats an imperative loop. And there's no problem writing the loops because they are neither longer nor less comprehensible than the functional versions. In all other situations, the FP solution looks competitive.
This is a microbenchmark, and it depends on how the compiler optimizes you code. You have 3 loops composed here,
zip . map . fold
Now, I'm fairly sure the Scala compiler cannot fuse those three loops into a single loop, and the underlying data type is strict, so each (.) corresponds to an intermediate array being created. The imperative/mutable solution would reuse the buffer each time, avoiding copies.
Now, an understanding of what composing those three functions means is key to understanding performance in a functional programming language -- and indeed, in Haskell, those three loops will be optimized into a single loop that reuses an underlying buffer -- but Scala cannot do that.
There are benefits to sticking to the combinator approach, however -- by distinguishing those three functions, it will be easier to parallelize the code (replace map with parMap etc). In fact, given the right array type, (such as a parallel array) a sufficiently smart compiler will be able to automatically parallelize your code, yielding more performance wins.
So, in summary:
naive translations may have unexpected copies and inefficiences
clever FP compilers remove this overhead (but Scala can't yet)
sticking to the high level approach pays off if you want to retarget your code, e.g. to parallelize it
Don Stewart has a fine answer, but it might not be obvious how going from one loop to three creates a factor of 40 slowdown. I'll add to his answer that Scala compiles to JVM bytecodes, and not only does the Scala compiler not fuse the three loops into one, but the Scala compiler is almost certainly allocating all the intermediate arrays. Notoriously, implementations of the JVM are not designed to handle the allocation rates required by functional languages. Allocation is a significant cost in functional programs, and that's one the loop-fusion transformations that Don Stewart and his colleagues have implemented for Haskell are so powerful: they eliminate lots of allocations. When you don't have those transformations, plus you're using an expensive allocator such as is found on a typical JVM, that's where the big slowdown comes from.
Scala is a great vehicle for experimenting with the expressive power of an unusual mix of language ideas: classes, mixins, modules, functions, and so on. But it's a relatively young research language, and it runs on the JVM, so it's unreasonable to expect great performance except on the kind of code that JVMs are good at. If you want to experiment with the mix of language ideas that Scala offers, great—it's a really interesting design—but don't expect the same performance on pure functional code that you'd get with a mature compiler for a functional language, like GHC or MLton.
Is scala functional programming slower than traditional coding?
Not necessarily. Stuff to do with first-class functions, pattern matching, and currying need not be especially slow. But with Scala, more than with other implementations of other functional languages, you really have to watch out for allocations—they can be very expensive.
The Scala collections library is fully generic, and the operations provided are chosen for maximum capability, not maximum speed. So, yes, if you use a functional paradigm with Scala without paying attention (especially if you are using primitive data types), your code will take longer to run (in most cases) than if you use an imperative/iterative paradigm without paying attention.
That said, you can easily create non-generic functional operations that perform quickly for your desired task. In the case of working with pairs of floats, we might do the following:
class FastFloatOps(a: Array[Float]) {
def fastMapOnto(f: Float => Float) = {
var i = 0
while (i < a.length) { a(i) = f(a(i)); i += 1 }
this
}
def fastMapWith(b: Array[Float])(f: (Float,Float) => Float) = {
val len = a.length min b.length
val c = new Array[Float](len)
var i = 0
while (i < len) { c(i) = f(a(i),b(i)); i += 1 }
c
}
def fastReduce(f: (Float,Float) => Float) = {
if (a.length==0) Float.NaN
else {
var r = a(0)
var i = 1
while (i < a.length) { r = f(r,a(i)); i += 1 }
r
}
}
}
implicit def farray2fastfarray(a: Array[Float]) = new FastFloatOps(a)
and then these operations will be much faster. (Faster still if you use Double and 2.8.RC1, because then the functions (Double,Double)=>Double will be specialized, not generic; if you're using something earlier, you can create your own abstract class F { def f(a: Float) : Float } and then call with new F { def f(a: Float) = a*a } instead of (a: Float) => a*a.)
Anyway, the point is that it's not the functional style that makes functional coding in Scala slow, it's that the library is designed with maximum power/flexibility in mind, not maximum speed. This is sensible, since each person's speed requirements are typically subtly different, so it's hard to cover everyone supremely well. But if it's something you're doing more than just a little, you can write your own stuff where the performance penalty for a functional style is extremely small.
I am not an expert Scala programmer, so there is probably a more efficient method, but what about something like this. This can be tail call optimized, so performance should be OK.
def multiply_and_sum(l1:List[Int], l2:List[Int], sum:Int):Int = {
if (l1 != Nil && l2 != Nil) {
multiply_and_sum(l1.tail, l2.tail, sum + (l1.head * l2.head))
}
else {
sum
}
}
val first = Array(1,2,3,4,5)
val second = Array(6,7,8,9,10)
multiply_and_sum(first.toList, second.toList, 0) //Returns: 130
To answer the question in the title: Simple functional constructs may be slower than imperative on the JVM.
But, if we consider only simple constructs, then we might as well throw out all modern languages and stick with C or assembler. If you look a the programming language shootout, C always wins.
So why choose a modern language? Because it lets you express a cleaner design. Cleaner design leads to performance gains in the overall operation of the application. Even if some low-level methods can be slower. One of my favorite examples is the performance of BuildR vs. Maven. BuildR is written in Ruby, an interpreted, slow, language. Maven is written in Java. A build in BuildR is twice as fast as Maven. This is due mostly to the design of BuildR which is lightweight compared with that of Maven.
Your functional solution is slow because it is generating unnecessary temporary data structures. Removing these is known as deforesting and it is easily done in strict functional languages by rolling your anonymous functions into a single anonymous function and using a single aggregator. For example, your solution written in F# using zip, map and reduce:
let dot xs ys = Array.zip xs ys |> Array.map (fun (x, y) -> x * y) -> Array.reduce ( * )
may be rewritten using fold2 so as to avoid all temporary data structures:
let dot xs ys = Array.fold2 (fun t x y -> t + x * y) 0.0 xs ys
This is a lot faster and the same transformation can be done in Scala and other strict functional languages. In F#, you can also define the fold2 as inline in order to have the higher-order function inlined with its functional argument whereupon you recover the optimal performance of the imperative loop.
Here is dbyrnes solution with arrays (assuming Arrays are to be used) and just iterating over the index:
def multiplyAndSum (l1: Array[Int], l2: Array[Int]) : Int =
{
def productSum (idx: Int, sum: Int) : Int =
if (idx < l1.length)
productSum (idx + 1, sum + (l1(idx) * l2(idx))) else
sum
if (l2.length == l1.length)
productSum (0, 0) else
error ("lengths don't fit " + l1.length + " != " + l2.length)
}
val first = (1 to 500).map (_ * 1.1) toArray
val second = (11 to 510).map (_ * 1.2) toArray
def loopi (n: Int) = (1 to n).foreach (dummy => multiplyAndSum (first, second))
println (timed (loopi (100*1000)))
That needs about 1/40 of the time of the list-approach. I don't have 2.8 installed, so you have to test #tailrec yourself. :)