Warning! possibly a very dumb question
Does functional programming eat up more memory than procedural programming?
I mean ... if your objects(data structures whatever) are all imutable. Don't you end up having more object in the memory at a given time.
Doesn't this eat up more memory?
It depends on what you're doing. With functional programming you don't have to create defensive copies, so for certain problems it can end up using less memory.
Many functional programming languages also have good support for laziness, which can further reduce memory usage as you don't create objects until you actually use them. This is arguably something that's only correlated with functional programming rather than a direct cause, however.
Persistent values, that functional languages encourage but which can be implemented in an imperative language, make sharing a no-brainer.
Although the generally accepted idea is that with a garbage collector, there is some amount of wasted space at any given time (already unreachable but not yet collected blocks), in this context, without a garbage collector, you end up very often copying values that are immutable and could be shared, just because it's too much of a mess to decide who is responsible for freeing the memory after use.
These ideas are expanded on a bit in this experience report which does not claim to be an objective study but only anecdotal evidence.
Apart from avoiding defensive copies by the programmer, a very smart implementation of pure functional programming languages like Haskell or Standard ML (which lack physical pointer equality) can actively recover sharing of structurally equal values in memory, e.g. as part of the memory management and garbage collection.
Thus you can have automatic hash consing provided by your programming language runtime-system.
Compare this with objects in Java: object identity is an integral part of the language definition. Even just exchanging one immutable String for another poses semantic problems.
There is indeed at least a tendency to regard memory as affluent ressource (which, in fact, it really is in most cases), but this applies to modern programming as a whole.
With multiple cores, parallel garbage collectors and available RAM in the gigabytes, one used to concentrate on different aspects of a program than in earlier times, when every byte one could save counted. Remember when Bill Gates said "640K should be enough for every program"?
I know that I'm a lot late on this question.
Functional languages does not in general use more memory than imperative or OO languages. It depends more on the code you write. Yes F#, SML, Haskell and such has immutable values (not variables), but for all of them it goes without saying that if you update f.x. a single linked list, it re-compute only what is necessary.
Say you got a list of 5 elements, and you are removing the first 3 and adding a new one in front of it. it will simply get the pointer that points to the fourth element and let the new list point to that point of data i.e. reusing data. as seen below.
old list
[x0,x1,x2]
\
[x3,x4]
new list /
[y0,y1]
If it was an imperative language we could not do this because the values x3 and x4 could very well change over time, the list [x3,x4] could change too. Say that the 3 elements removed are not used afterward, the memory they use can be cleaned up right away, in contrast to unused space in an array.
That all data are immutable (except IO) are a strength. It simplifies the data flow analysis from a none trivial computation to a trivial one. This combined with a often very strong type system, will give the compiler a bunch of information about the code it can use to do optimization it normally could not do because of indicability. Most often the compiler turn values that are re-computed recursively and discarded from each iteration (recursion) into a mutable computation. These two things gives you the proof that if your program compile it will work. (with some assumptions)
If you look at the language Rust (not functional) just by learning about "borrow system" you will understand more about how and when things can be shared safely. it is a language that is painful to write code in unless you like to see your computer scream at you that your are an idiot. Rust is for the most part the combination of all the study made of programming language and type theory for more than 40 years. I mention Rust, because it despite the pain of writing in it, has the promise that if your program compile, there will be NO memory leaking, dead locking, dangling pointers, even in multi processing programs. This is because it uses much of the research of functional programming language that has been done.
For a more complex example of when functional programming uses less memory, I have made a lexer/parser interpreter (the same as generator but without the need to generate a code file) when computing the states of the DFA (deterministic finite automata) it uses immutable sets, because it compute new sets of already computed sets, my code allocate less memory simply because it borrow already known data points instead of copying it to a new set.
To wrap it up, yes functional programming can use more memory than imperative once. Most likely it is because you are using the wrong abstraction to mirror the problem. i.e. If you try to do it the imperative way in a functional language it will hurt you.
Try this book, it has not much on memory management but is a good book to start with if you will learn about compiler theory and yes it is legal to download. I have ask Torben, he is my old professor.
http://hjemmesider.diku.dk/~torbenm/Basics/
I'll throw my hat in the ring here. The short answer to the question is no, and this is because immutability does not mean the same thing as stored in memory. For example, let's take this toy program :
x = 2
x = x * 3
x = x * 2
print(x)
Which uses mutation to compute new values. Compare this to the same program which does not use mutation:
x = 2
y = x * 3
z = y * 2
print(z)
At first glance, it appears this requires 3x the memory of the first program! However, just because a value is immutable doesn't mean it needs to be stored in memory. In the case of the second program, after y is computed, x is no longer necessary, because it isn't used for the rest of the program, and can be garbage collected, or removed from memory. Similarly, after z is computed, y can be garbage collected. So, in principle, with a perfect garbage collector, after we execute the third line of code, I only need to have stored z in memory.
Another oft-worried about source of memory consumption in functional languages is deep recursion. For example, calculating a large Fibonacci number.
calc_fib(x):
if x > 1:
return x * calc_fib(x-1)
else:
return x
If I run calc_fib(100000), I could implement this in a way which requires storing 100000 values in memory, or I could use Tail-Call Elimination (basically storing only the most-recently computed value in memory instead of all function calls). For less straightforward recursion you can resort to trampolining. So for functional languages which support this, recursion does not need to be a source of massive memory consumption, either. However, not all nominally functional languages do (for example, JavaScript does not).
Related
I have some data that isn't stored as structure of arrays. What is the best practice for loading the data in registers?
__m128 _mm_set_ps (float e3, float e2, float e1, float e0)
// or
__m128 _mm_loadu_ps (float const* mem_addr)
With _mm_loadu_ps, I'd copy the data in a temporary stack array, vs. copying the data as values directly. Is there a difference?
It can be a tradeoff between latency and throughput, because separate stores into an array will cause a store-forwarding stall when you do a vector load. So it's high latency, but throughput could still be ok, and it doesn't compete with surrounding code for the vector shuffle execution unit. So it can be a throughput win if the surrounding code also has shuffle operations, vs. 3 shuffles to insert 3 elements into an XMM register after a scalar load of the first one. Either way it's still a lot of total uops, and that's another throughput bottleneck.
Most compilers like gcc and clang do a pretty good job with _mm_set_ps () when optimizing with -O3, whether the inputs are in memory or registers. I'd recommend it, except in some special cases.
The most common missed-optimization with _mm_set is when there's some locality between the inputs. e.g. don't do _mm_set_ps(a[i+2], a[i+3], a[i+0], a[i+1]]), because many compilers will use their regular pattern without taking advantage of the fact that 2 pairs of elements are contiguous in memory. In that case, use (the intrinsics for) movsd and movhps to load in two 64-bit chunks. (Not movlps: it merges into an existing register instead of zeroing the high elements, so it has a false dependency on the old contents while movsd zeros the high half.) Or a shufps if some reordering is needed between or within the 64-bit chunks.
The "regular pattern" that compilers use will usually be movss / insertps from memory if compiling with SSE4, or movss loads and unpcklps shuffles to combine pairs and then another unpcklps, unpcklpd, or movlhps to shuffle into one register. Or a shufps or shufpd if the compiler likes to waste code-side on immediate shuffle-control operands instead of using fixed shuffles intelligently.
See also Agner Fog's optimization guides for some handy tables of data-movement instructions to get a better idea of what the compiler has to work with, and how stuff performs. Note that Haswell and later can only do 1 shuffle per clock. Also other links in the x86 tag wiki.
There's no really cheap way for a compiler or human to do this, in the general case when you have 4 separate scalars that aren't contiguous in memory at all. Or for register inputs, where it can't optimize the way they're generated in registers in the first place to have some of them already packed together. (e.g. for function args passed in registers to a function that can't / doesn't inline.)
Anyway, it's not a big deal unless you have this inside an inner loop. In that case, definitely worry about it (and check the compiler's asm output to see if it made a mess or could do better if you program the gather yourself with intrinsics that map to single instructions like _mm_load_ss / _mm_shuffle_ps).
If possible, rearrange your data layout to make data contiguous in at least small chunks / stripes. (See https://stackoverflow.com/tags/sse/info, specifically these slides. But sometimes one part of the program needs the data one way, and the other needs another. Choose the layout that's good for the case that needs to be faster, or that runs more often, or whatever, and suck it up and do the best you can for the other part of the program. :P Possibly transpose / convert once to set up for multiple SIMD operations, but extra passes over data with no computation just suck up time and can hurt your computational intensity (how much ALU work you do for each time you load data into registers) more than they help.
And BTW, actual gather instructions (like AVX2 vgatherdps) are not very fast; even on Skylake it's probably not worth using a gather instruction for four 32-bit elements at known locations. On Broadwell / Haswell, gather is definitely not worth using for this.
Because of having performance issues when passing a code from static to dynamic allocation, I started to wander about how memory allocation is managed in a Fortran code.
Specifically, in this question, I wander if the order or syntax used for the allocate statement makes any difference. That is, does it make any difference to allocate vectors like:
allocate(x(DIM),y(DIM))
versus
allocate(x(DIM))
allocate(y(DIM))
The syntax suggests that in the first case the program would allocate all the space for the vectors at once, possibly improving the performance, while in the second case it must allocate the space for one vector at a time, in such a way that they could end up far from each other. If not, that is, if the syntax does not make any difference, I wander if there is a way to control that allocation (for instance, allocating a vector for all space and using pointers to address the space allocated as multiple variables).
Finally, I notice now that I don't even know one thing: an allocate statement guarantees that at least a single vector occupies a contiguous space in memory (or the best it can?).
From the language standard point of view both ways how to write them are possible. The compiler is free to allocate the arrays where it wants. It normally calls malloc() to allocate some piece of memory and makes the allocatable arrays from that piece.
Whether it might allocate a single piece of memory for two different arrays in a single allocate statement is up to the compiler, but I haven't heard about any compiler doing that.
I just verified that my gfortran just calls __builtin_malloc two times in this case.
Another issue is already pointed out by High Performance Mark. Even when malloc() successfully returns, the actual memory pages might still not be assigned. On Linux that happens when you first access the array.
I don't think it is too important if those arrays are close to each other in memory or not anyway. The CPU can cache arrays from different regions of address space if it needs them.
Is there a way how to control the allocation? Yes, you can overload the malloc by your own allocator which does some clever things. It may be used to have always memory aligned to 32-bytes or similar purposes (example). Whether you will improve performance of your code by allocating things somehow close to each other is questionable, but you can have a try. (Of course this is completely compiler-dependent thing, a compiler doesn't have to use malloc() at all, but mostly they do.) Unfortunately, this will only works when the calls to malloc are not inlined.
There are (at least) two issues here, firstly the time taken to allocate the memory and secondly the locality of memory in the arrays and the impact of this on performance. I don't know much about the actual allocation process, although the links suggested by High Performance Mark and the answer by Vadimir F cover this.
From your question, it seems you are more interested in cache hits and memory locality given by arrays being next to each other. I would guess there is no guarantee either allocate statement ensures both arrays next to each other in memory. This is based on allocating arrays in a type, which in the fortran 2003 MAY 2004 WORKING DRAFT J3/04-007 standard
NOTE 4.20
Unless the structure includes a SEQUENCE statement, the use of this terminology in no way implies that these components are stored in this, or any other, order. Nor is there any requirement that contiguous storage be used.
From the discussion with Vadimir F, if you put allocatable arrays in a type and use the sequence keyword, e.g.
type botharrays
SEQUENCE
double precision, dimension(:), allocatable :: x, y
end type
this DOES NOT ensure they are allocated as adjacent in memory. For static arrays or lots of variables, a sequential type sounds like it may work like your idea of "allocating a vector for all space and using pointers to address the space allocated as multiple variables". I think common blocks (Fortran 77) allowed you to specify the relationship between memory location of arrays and variables in memory, but don't work with allocatable arrays either.
In short, I think this means you cannot ensure two allocated arrays are adjacent in memory. Even if you could, I don't see how this will result in a reduction in cache misses or improved performance. Even if you typically use the two together, unless the arrays are small enough that the cache will include multiple arrays in one read (assuming reads are allowed to go beyond array bounds) you won't benefit from the memory locality.
It is nice to have a wrapper for every primitive value, so that there is no way to misuse it. I suspect this convenience comes at a price. Is there a performance drop? Should I rather use bare primitive values instead if the performance is a concern?
Yes, there's going to be a performance drop when using single-case union types to wrap primitive values. Union cases are compiled into classes, so you'll pay the price of allocating (and later, collecting) the class and you'll also have an additional indirection each time you fetch the value held inside the union case.
Depending on the specifics of your application, and how often you'll incur these additional overheads, it may still be worth doing if it makes your code safer and more modular.
I've written a lot of performance-sensitive code in F#, and my personal preference is to use F# unit-of-measure types whenever possible to "tag" primitive types (e.g., ints). This keeps them from being misused (thanks to the F# compiler's type checker) but also avoids any additional run-time overhead, since the measure types are erased when the code is compiled. If you want some examples of this, I've used this design pattern extensively in my fsharp-tools projects.
Jack has much more experience with writing high-performance F# code than I do, so I think his answer is absolutely right (I also think the idea to use units of measure is pretty interesting).
Just to put things in context, I wrote a really basic test (using just F# Interactive - so things may differ in Release mode) to compare the performance. It allocates an array of wrapped (vs. non-wrapped) int values. This is probably the scenario where non-wrapped types are really a good choice, because the array will be just a continuous block of memory.
#time
// Using a simple wrapped `int` type
type Foo = Foo of int
let foos = Array.init 1000000 Foo
// Add all 'foos' 1k times and ignore the results
for i in 0 .. 1000 do
let mutable total = 0
for Foo f in foos do total <- total + f
On my machine, the for loop takes on average something around 1050ms. Now, the unwrapped version:
let bars = Array.init 1000000 id
for i in 0 .. 1000 do
let mutable total = 0
for b in bars do total <- total + b
On my machine, this takes about 700ms.
So, there is certainly some performance penalty, but perhaps smaller than one would expect (some 33%). And this is looking at a test that does virtually nothing else than unwrap the values in a loop. In code that does something useful, the overhead would be a lot smaller.
This may be an issue if you're writing high-performance code, something that will process lots of data or something that takes some time and the users will run it frequently (like compiler & tools). On the other hand, if you application is not performance critical, then this is not likely to be a problem.
From F# 4.1 onwards adding the [<Struct>] attribute to suitable single case discriminated unions will increase the performance and reduce the number of memory allocations performed.
I see that Erlang Efficiency User's Guide Section 5.3 recommends leaving the non-flat list as it is when being used as an iolist because the penalty of non-flattening is smaller than flattening. Is there any quantitative example of the speed difference?
When a deep list contains n elements, then performing lists:flatten on it will require Θ(n) time, and worse, Θ(n) memory allocations. How slow that is on your machine is a function of many variables; measure and ye shall know.
I've been looking into how programming languages work, and some of them have a so-called virtual machines. I understand that this is some form of emulation of the programming language within another programming language, and that it works like how a compiled language would be executed, with a stack. Did I get that right?
With the proviso that I did, what bamboozles me is that many non-compiled languages allow variables with "liberal" type systems. In Python for example, I can write this:
x = "Hello world!"
x = 2**1000
Strings and big integers are completely unrelated and occupy different amounts of space in memory, so how can this code even be represented in a stack-based environment? What exactly happens here? Is x pointed to a new place on the stack and the old string data left unreferenced? Do these languages not use a stack? If not, how do they represent variables internally?
Probably, your question should be titled as "How do dynamic languages work?."
That's simple, they store the variable type information along with it in memory. And this is not only done in interpreted or JIT compiled languages but also natively-compiled languages such as Objective-C.
In most VM languages, variables can be conceptualized as pointers (or references) to memory in the heap, even if the variable itself is on the stack. For languages that have primitive types (int and bool in Java, for example) those may be stored on the stack as well, but they can not be assigned new types dynamically.
Ignoring primitive types, all variables that exist on the stack have their actual values stored in the heap. Thus, if you dynamically reassign a value to them, the original value is abandoned (and the memory cleaned up via some garbage collection algorithm), and the new value is allocated in a new bit of memory.
The VM has nothing to do with the language. Any language can run on top of a VM (the Java VM has hundreds of languages already).
A VM enables a different kind of "assembly language" to be run, one that is more fit to adapting a compiler to. Everything done in a VM could be done in a CPU, so think of the VM like a CPU. (Some actually are implemented in hardware).
It's extremely low level, and in many cases heavily stack based--instead of registers, machine-level math is all relative to locations relative to the current stack pointer.
With normal compiled languages, many instructions are required for a single step. a + might look like "Grab the item from a point relative to the stack pointer into reg a, grab another into reg b. add reg a and b. put reg a into a place relative to the stack pointer.
The VM does all this with a single, short instruction, possibly one or two bytes instead of 4 or 8 bytes PER INSTRUCTION in machine language (depending on 32 or 64 bit architecture) which (guessing) should mean around 16 or 32 bytes of x86 for 1-2 bytes of machine code. (I could be wrong, my last x86 coding was in the 80286 era.)
Microsoft used (probably still uses) VMs in their office products to reduce the amount of code.
The procedure for creating the VM code is the same as creating machine language, just a different processor type essentially.
VMs can also implement their own security, error recovery and memory mechanisms that are very tightly related to the language.
Some of my description here is summary and from memory. If you want to explore the bytecode definition yourself, it's kinda fun:
http://java.sun.com/docs/books/jvms/second_edition/html/Instructions2.doc.html
The key to many of the 'how do VMs handle variables like this or that' really comes down to metadata... The meta information stored and then updated gives the VM a much better handle on how to allocate and then do the right thing with variables.
In many cases this is the type of overhead that can really get in the way of performance. However, modern day implementations, etc have come a long way in doing the right thing.
As for your specific questions - treating variables as vanilla objects / etc ... comes down to reassigning / reevaluating meta information on new assignments - that's why x can look one way and then the next.
To answer a part of your questions, I'd recommend a google tech talk about python, where some of your questions concerning dynamic languages are answered; for example what a variable is (it is not a pointer, nor a reference, but in case of python a label).