I have a function like this:
fun randomWalk(numSteps: Int): Int {
var n = 0
repeat(numSteps) { n += (-1 + 2 * Random.nextInt(2)) }
return n.absoluteValue
}
This works fine, except that it uses a mutable variable, and I would like to make everything immutable when possible, for better safety and readability. So I came up with an equivalent version that doesn't use any mutable variables:
fun randomWalk_seq(numSteps: Int): Int =
generateSequence(0) { it + (-1 + 2 * Random.nextInt(2)) }
.elementAt(numSteps)
.absoluteValue
This also works fine and produces the same results, but it takes 3 times longer.
I used the following way to measure it:
#OptIn(ExperimentalTime::class)
fun main() {
val numSamples = 100000
val numSteps = 15708
repeat(5) {
val randomWalkSamples: IntArray
val duration = measureTime {
randomWalkSamples = IntArray(numSamples) { randomWalk(numSteps) }
}
println(duration)
}
}
I know it's a bit hacky (I could have used JMH but this is just a quick test - at least I know that measureTime uses a monotonic clock). The results for the iterative (mutable) version:
2.965358406s
2.560777033s
2.554363661s
2.564279403s
2.608323586s
As expected, the first line shows it took a bit longer on the first run due to the warming up of the JIT, but the next 4 lines have fairly small variation.
After replacing randomWalk with randomWalk_seq:
6.636866719s
6.980840906s
6.993998111s
6.994038706s
7.018054467s
Somewhat surprisingly, I don't see any warmup time - the first line is always lesser duration than the following 4 lines, every time I run this. And also, every time I run it, the duration keeps increasing, with line 5 always being the greatest duration.
Can someone explain the findings, and also is there any way of making this function not use any mutable variables but still have performance that is close to the mutable version?
Your solution is slower for two main reasons: boxing and the complexity of the iterator used by generateSequence()'s Sequence implementation.
Boxing happens because a Sequence uses its types generically, so it cannot use primitive 32-bit Ints directly, but must wrap them in classes and unwrap them when retrieving the items.
You can see the complexity of the iterator by Ctrl+clicking the generateSequence function to view the source code.
#Михаил Нафталь's suggestion is faster because it avoids the complex iterator of the sequence, but it still has boxing.
I tried writing an overload of sumOf that uses IntProgression directly instead of Iterable<T>, so it won't use boxing, and that resulted in equivalent performance to your imperative code with the var. As you can see, it's inline and when put together with the { -1 + 2 * Random.nextInt(2) } lambda suggested by #Михаил Нафталь, then the resulting compiled code will be equivalent to your imperative code.
inline fun IntProgression.sumOf(selector: (Int) -> Int): Int {
var sum: Int = 0.toInt()
for (element in this) {
sum += selector(element)
}
return sum
}
Ultimately, I don't think you're buying yourself much in the way of code clarity by removing a single var in such a small function. I would say the sequence code is arguably harder to read. vars may add to code complexity in complex algorithms, but I don't think they do in such simple algorithms, especially when there's only one of them and it's local to the function.
Equivalent immutable one-liner is:
fun randomWalk2(numSteps: Int) =
(1..numSteps).sumOf { -1 + 2 * Random.nextInt(2) }.absoluteValue
Probably, even more performant would be to replace
with
so that you'll have one multiplication and n additions instead of n multiplications and (2*n-1) additions:
fun randomWalk3(numSteps: Int) =
(-numSteps + 2 * (1..numSteps).sumOf { Random.nextInt(2) }).absoluteValue
Update
As #Tenfour04 noted, there is no specific stdlib implementation for IntProgression.sumOf, so it's resolved to Iterable<T>.sumOf, which will add unnecessary overhead for int boxing.
So, it's better to use IntArray here instead of IntProgression:
fun randomWalk4(numSteps: Int) =
(-numSteps + 2 * IntArray(numSteps).sumOf { Random.nextInt(2) }).absoluteValue
Still encourage you to check this all with JMH
I think:"Removing mutability without losing speed" is wrong title .because
mutability thing comes to deal with the flow that program want to achieve .
you are using var inside function.... and 100% this var will not ever change from outside this function and that is mutability concept.
if we git rid off from var everywhere why we need it in programming ?
The compilers I've been using in C or Java have dead code prevention (warning when a line won't ever be executed). My professor says that this problem can never be fully solved by compilers though. I was wondering why that is. I am not too familiar with the actual coding of compilers as this is a theory-based class. But I was wondering what they check (such as possible input strings vs acceptable inputs, etc.), and why that is insufficient.
The dead code problem is related to the Halting problem.
Alan Turing proved that it is impossible to write a general algorithm that will be given a program and be able to decide whether that program halts for all inputs. You may be able to write such an algorithm for specific types of programs, but not for all programs.
How does this relate to dead code?
The Halting problem is reducible to the problem of finding dead code. That is, if you find an algorithm that can detect dead code in any program, then you can use that algorithm to test whether any program will halt. Since that has been proven to be impossible, it follows that writing an algorithm for dead code is impossible as well.
How do you transfer an algorithm for dead code into an algorithm for the Halting problem?
Simple: you add a line of code after the end of the program you want to check for halt. If your dead-code detector detects that this line is dead, then you know that the program does not halt. If it doesn't, then you know that your program halts (gets to the last line, and then to your added line of code).
Compilers usually check for things that can be proven at compile-time to be dead. For example, blocks that are dependent on conditions that can be determined to be false at compile time. Or any statement after a return (within the same scope).
These are specific cases, and therefore it's possible to write an algorithm for them. It may be possible to write algorithms for more complicated cases (like an algorithm that checks whether a condition is syntactically a contradiction and therefore will always return false), but still, that wouldn't cover all possible cases.
Well, let's take the classical proof of the undecidability of the halting problem and change the halting-detector to a dead-code detector!
C# program
using System;
using YourVendor.Compiler;
class Program
{
static void Main(string[] args)
{
string quine_text = #"using System;
using YourVendor.Compiler;
class Program
{{
static void Main(string[] args)
{{
string quine_text = #{0}{1}{0};
quine_text = string.Format(quine_text, (char)34, quine_text);
if (YourVendor.Compiler.HasDeadCode(quine_text))
{{
System.Console.WriteLine({0}Dead code!{0});
}}
}}
}}";
quine_text = string.Format(quine_text, (char)34, quine_text);
if (YourVendor.Compiler.HasDeadCode(quine_text))
{
System.Console.WriteLine("Dead code!");
}
}
}
If YourVendor.Compiler.HasDeadCode(quine_text) returns false, then the line System.Console.WriteLn("Dead code!"); won't be ever executed, so this program actually does have dead code, and the detector was wrong.
But if it returns true, then the line System.Console.WriteLn("Dead code!"); will be executed, and since there is no more code in the program, there is no dead code at all, so again, the detector was wrong.
So there you have it, a dead-code detector that returns only "There is dead code" or "There is no dead code" must sometimes yield wrong answers.
If the halting problem is too obscure, think of it this way.
Take a mathematical problem that is believed to be true for all positive integer's n, but hasn't been proven to be true for every n. A good example would be Goldbach's conjecture, that any positive even integer greater than two can be represented by the sum of two primes. Then (with an appropriate bigint library) run this program (pseudocode follows):
for (BigInt n = 4; ; n+=2) {
if (!isGoldbachsConjectureTrueFor(n)) {
print("Conjecture is false for at least one value of n\n");
exit(0);
}
}
Implementation of isGoldbachsConjectureTrueFor() is left as an exercise for the reader but for this purpose could be a simple iteration over all primes less than n
Now, logically the above must either be the equivalent of:
for (; ;) {
}
(i.e. an infinite loop) or
print("Conjecture is false for at least one value of n\n");
as Goldbach's conjecture must either be true or not true. If a compiler could always eliminate dead code, there would definitely be dead code to eliminate here in either case. However, in doing so at the very least your compiler would need to solve arbitrarily hard problems. We could provide problems provably hard that it would have to solve (e.g. NP-complete problems) to determine which bit of code to eliminate. For instance if we take this program:
String target = "f3c5ac5a63d50099f3b5147cabbbd81e89211513a92e3dcd2565d8c7d302ba9c";
for (BigInt n = 0; n < 2**2048; n++) {
String s = n.toString();
if (sha256(s).equals(target)) {
print("Found SHA value\n");
exit(0);
}
}
print("Not found SHA value\n");
we know that the program will either print out "Found SHA value" or "Not found SHA value" (bonus points if you can tell me which one is true). However, for a compiler to be able to reasonably optimise that would take of the order of 2^2048 iterations. It would in fact be a great optimisation as I predict the above program would (or might) run until the heat death of the universe rather than printing anything without optimisation.
I don't know if C++ or Java have an Eval type function, but many languages do allow you do call methods by name. Consider the following (contrived) VBA example.
Dim methodName As String
If foo Then
methodName = "Bar"
Else
methodName = "Qux"
End If
Application.Run(methodName)
The name of the method to be called is impossible to know until runtime. Therefore, by definition, the compiler cannot know with absolute certainty that a particular method is never called.
Actually, given the example of calling a method by name, the branching logic isn't even necessary. Simply saying
Application.Run("Bar")
Is more than the compiler can determine. When the code is compiled, all the compiler knows is that a certain string value is being passed to that method. It doesn't check to see if that method exists until runtime. If the method isn't called elsewhere, through more normal methods, an attempt to find dead methods can return false positives. The same issue exists in any language that allows code to be called via reflection.
Unconditional dead code can be detected and removed by advanced compilers.
But there is also conditional dead code. That is code that cannot be known at the time of compilation and can only be detected during runtime. For example, a software may be configurable to include or exclude certain features depending on user preference, making certain sections of code seemingly dead in particular scenarios. That is not be real dead code.
There are specific tools that can do testing, resolve dependencies, remove conditional dead code and recombine the useful code at runtime for efficiency. This is called dynamic dead code elimination. But as you can see it is beyond the scope of compilers.
A simple example:
int readValueFromPort(const unsigned int portNum);
int x = readValueFromPort(0x100); // just an example, nothing meaningful
if (x < 2)
{
std::cout << "Hey! X < 2" << std::endl;
}
else
{
std::cout << "X is too big!" << std::endl;
}
Now assume that the port 0x100 is designed to return only 0 or 1. In that case the compiler cannot figure out that the else block will never be executed.
However in this basic example:
bool boolVal = /*anything boolean*/;
if (boolVal)
{
// Do A
}
else if (!boolVal)
{
// Do B
}
else
{
// Do C
}
Here the compiler can calculate out the the else block is a dead code.
So the compiler can warn about the dead code only if it has enough data to to figure out the dead code and also it should know how to apply that data in order to figure out if the given block is a dead code.
EDIT
Sometimes the data is just not available at the compilation time:
// File a.cpp
bool boolMethod();
bool boolVal = boolMethod();
if (boolVal)
{
// Do A
}
else
{
// Do B
}
//............
// File b.cpp
bool boolMethod()
{
return true;
}
While compiling a.cpp the compiler cannot know that boolMethod always returns true.
The compiler will always lack some context information. E.g. you might know, that a double value never exeeds 2, because that is a feature of the mathematical function, you use from a library. The compiler does not even see the code in the library, and it can never know all features of all mathematical functions, and detect all weired and complicated ways to implement them.
The compiler doesn't necessarily see the whole program. I could have a program that calls a shared library, which calls back into a function in my program which isn't called directly.
So a function which is dead with respect to the library it's compiled against could become alive if that library was changed at runtime.
If a compiler could eliminate all dead code accurately, it would be called an interpreter.
Consider this simple scenario:
if (my_func()) {
am_i_dead();
}
my_func() can contain arbitrary code and in order for the compiler to determine whether it returns true or false, it will either have to run the code or do something that is functionally equivalent to running the code.
The idea of a compiler is that it only performs a partial analysis of the code, thus simplifying the job of a separate running environment. If you perform a full analysis, that isn't a compiler any more.
If you consider the compiler as a function c(), where c(source)=compiled code, and the running environment as r(), where r(compiled code)=program output, then to determine the output for any source code you have to compute the value of r(c(source code)). If calculating c() requires the knowledge of the value of r(c()) for any input, there is no need for a separate r() and c(): you can just derive a function i() from c() such that i(source)=program output.
Others have commented on the halting problem and so forth. These generally apply to portions of functions. However it can be hard/impossible to know whether even an entire type (class/etc) is used or not.
In .NET/Java/JavaScript and other runtime driven environments there's nothing stopping types being loaded via reflection. This is popular with dependency injection frameworks, and is even harder to reason about in the face of deserialisation or dynamic module loading.
The compiler cannot know whether such types would be loaded. Their names could come from external config files at runtime.
You might like to search around for tree shaking which is a common term for tools that attempt to safely remove unused subgraphs of code.
Take a function
void DoSomeAction(int actnumber)
{
switch(actnumber)
{
case 1: Action1(); break;
case 2: Action2(); break;
case 3: Action3(); break;
}
}
Can you prove that actnumber will never be 2 so that Action2() is never called...?
I disagree about the halting problem. I wouldn't call such code dead even though in reality it will never be reached.
Instead, lets consider:
for (int N = 3;;N++)
for (int A = 2; A < int.MaxValue; A++)
for (int B = 2; B < int.MaxValue; B++)
{
int Square = Math.Pow(A, N) + Math.Pow(B, N);
float Test = Math.Sqrt(Square);
if (Test == Math.Trunc(Test))
FermatWasWrong();
}
private void FermatWasWrong()
{
Press.Announce("Fermat was wrong!");
Nobel.Claim();
}
(Ignore the type and overflow errors) Dead code?
Look at this example:
public boolean isEven(int i){
if(i % 2 == 0)
return true;
if(i % 2 == 1)
return false;
return false;
}
The compiler can't know that an int can only be even or odd. Therefore the compiler must be able to understand the semantics of your code. How should this be implemented? The compiler can't ensure that the lowest return will never be executed. Therefore the compiler can't detect the dead code.
Here is the program to find the factorial of a number in Go:
func factorial(x uint) uint {
if x == 0 {
return 1
}
return x * (factorial(x - 1))
}
The output for this function when called on input 5 is 120. However, if I add an else statement I get an error.
func factorial(x uint) uint {
if x == 0 {
return 1
} else {
return x * (factorial(x - 1))
}
}
Error : function ends without a return statement
I added a return at the end :
func factorial(x uint) uint {
if x == 0 {
return 1
} else {
return x * (factorial(x - 1))
}
fmt.Println("this never executes")
return 1
}
and I get back the expected output of 120.
Why would the second case cause an error? Why in the third case even though the function never reaches the last return 1, it computes the correct output?
This is a well known problem of the compiler.
There is even an issue logged : http://code.google.com/p/go/issues/detail?id=65
In the words of one of the authors of the Go language:
The compilers require either a return or a panic to be lexically last
in a function with a result. This rule is easier than requiring full
flow control analysis to determine whether a function reaches the end
without returning (which is very hard in general), and simpler than
rules to enumerate easy cases such as this one. Also, being purely
lexical, the error cannot arise spontaneously due to changes in values
such as constants used in control structures inside the function.
-rob
From another comment in golang-nuts, we can infer it's not going to be "fixed" soon :
It's not a bug, it's a deliberate design decision.
-rob
Note that other languages like Java have rules allowing this else.
March 2013 EDIT - It just got changed in Go1.1 :
Before Go 1.1, a function that returned a value needed an explicit
"return" or call to panic at the end of the function; this was a
simple way to make the programmer be explicit about the meaning of the
function. But there are many cases where a final "return" is clearly
unnecessary, such as a function with only an infinite "for" loop.
In Go 1.1, the rule about final "return" statements is more
permissive. It introduces the concept of a terminating statement, a
statement that is guaranteed to be the last one a function executes.
Examples include "for" loops with no condition and "if-else"
statements in which each half ends in a "return". If the final
statement of a function can be shown syntactically to be a terminating
statement, no final "return" statement is needed.
Note that the rule is purely syntactic: it pays no attention to the
values in the code and therefore requires no complex analysis.
Updating: The change is backward-compatible, but existing code with
superfluous "return" statements and calls to panic may be simplified
manually. Such code can be identified by go vet.
And the issue I mentioned is now closed with status "Fixed".
I'm experimenting with the frama-c value analyzer to evaluate C-Code, which is actually threaded.
I want to ignore any threading problems that might occur und just inspect the possible values for a single thread. So far this works by setting the entry point to where the thread starts.
Now to my problem: Inside one thread I read values that are written by another thread, because frama-c does not (and should not?) consider threading (currently) it assumes my variable is in some broad range, but I know that the range is in fact much smaller.
Is it possible to tell the value analyzer the value range of this variable?
Example:
volatile int x = 0;
void f() {
while(x==0)
sleep(100);
...
}
Here frama-c detects that x is volatile and thus has range [--..--], but I know what the other thread will write into x, and I want to tell the analyzer that x can only be 0 or 1.
Is this possible with frama-c, especially in the gui?
Thanks in advance
Christian
This is currently not possible automatically. The value analysis considers that volatile variables always contain the full range of values included in their underlying type. There however exists a proprietary plug-in that transforms accesses to volatile variables into calls to user-supplied function. In your case, your code would be transformed into essentially this:
int x = 0;
void f() {
while(1) {
x = f_volatile_x();
if (x == 0)
sleep(100);
...
}
By specifying f_volatile_x correctly, you can ensure it returns values between 0 and 1 only.
If the variable 'x' is not modified in the thread you are studying, you could also initialize it at the beginning of the 'main' function with :
x = Frama_C_interval (0, 1);
This is a function defined by Frama-C in ...../share/frama-c/builtin.c so you have to add this file to your inputs when you use it.
I am very new to TDD. I am reading TDD By Example and it says "never try to use the same constant to mean more than one thing" and it show an example of Plus() method.
In my opinion, there is no difference between Plus(1, 1) which uses same constant value and Plus(1, 2). I want to know what are pros and cons of using same constant value in test method?
I think you misinterprete that statement. What the author (imho) is trying to convey is that following code is a recipe for disaster.
const SomeRandomValue = 32;
...
// Plus testcase
Plus(SomeRandomValue, SomeRandomValue)
...
// Divide testcase
Divide(SomeRandomValue, SomeRandomValue)
You have two testcases reusing a none descriptive constant. There is no way to know that by changing SomeRandomValue to 0 your testsuite will fail.
A better naming would be something like
const AdditionValue = 32;
const DivisorValue = 32;
...
// Plus testcase
Plus(AdditionValue, AdditionValue)
...
// Divide testcase
Divide(DivisorValue, DivisorValue)
where it should be obvious as to what the constants are used for.You should not get to hung up on the idea of code reuse when creating testcases.
Or to put it in other words:
I don't see anything wrong with reusing the DivisorValue constant in multiple testcases > but there is definitly something wrong trying to shoehorn one value into a none descriptive variable just in the name of code reuse.
If you use the same value in your test - as in Plus(1, 1) - your code could work for the wrong reason. Here is an implementation of Plus that will pass such a test, but fail a test with different values.
public int Plus (int a, int b) {
return a + a;
}
A test that avoids this risk is a better test than one which lets errors like these slip through.