Can anyone explain, why this program is returning the correct value for sqrt_min?
int n = 1000000;
double[] myArr = new double[n];
for(int i = n-1 ; i>= 0; i--){ myArr[i] = (double)i;}
// sqrt_min contains minimal sqrt-value
double sqrt_min = double.MaxValue;
Parallel.ForEach(myArr, num =>
{
double sqrt = Math.Sqrt(num); // some time consuming calculation that should be parallized
if(sqrt < sqrt_min){ sqrt_min = sqrt;}
});
Console.WriteLine("minimum: "+sqrt_min);
It works by sheer luck. Sometimes when you run it you are lucky that the non-atomic reads and writes to the double are not resulting in "torn" values. Sometimes you are lucky that the non-atomic tests and sets just happen to be setting the correct value when that race happens. There is no guarantee that this program produces any particular result.
Your code is not safe; it only works by coincidence.
If two threads run the if simultaneously, one of the minimums will be overwritten:
sqrt_min = 6
Thread A: sqrt = 5
Thread B: sqrt = 4
Thread A enters the if
Thread B enters the if
Thread B assigns sqrt_min = 4
Thread A assigns sqrt_min = 5
On 32-bit systems, you're also vulnerable to read/write tearing.
It would be possible to make this safe using Interlocked.CompareExchange in a loop.
For why your original code is broken check the other answers, I won't repeat that.
Multithreading is easiest when there is no write access to shared state. Luckily your code can be written that way. Parallel linq can be nice in such situations, but sometimes the the overhead is too large.
You can rewrite your code to:
double sqrt_min = myArr.AsParallel().Select(x=>Math.Sqrt(x)).Min();
In your specific problem it's faster to swap around the Min and the Sqrt operation, which is possible because Sqrt is monotonically increasing.
double sqrt_min = Math.Sqrt(myArr.AsParallel().Min())
Your code does not really work: I ran it in a loop 100,000 times, and it failed once on my 8-core computer, producing this output:
minimum: 1
I shortened the runs to make the error appear faster.
Here are my modifications:
static void Run() {
int n = 10;
double[] myArr = new double[n];
for (int i = n - 1; i >= 0; i--) { myArr[i] = (double)i*i; }
// sqrt_min contains minimal sqrt-value
double sqrt_min = double.MaxValue;
Parallel.ForEach(myArr, num => {
double sqrt = Math.Sqrt(num); // some time consuming calculation that should be parallized
if (sqrt < sqrt_min) { sqrt_min = sqrt; }
});
if (sqrt_min > 0) {
Console.WriteLine("minimum: " + sqrt_min);
}
}
static void Main() {
for (int i = 0; i != 100000; i++ ) {
Run();
}
}
This is not a coincidence, considering the lack of synchronization around the reading and writing of a shared variable.
As others have said, this only works based on shear luck. Both the OP and other posters have had trouble actually creating the race condition though. That is fairly easily explained. The code generates lots of race conditions, but the vast majority of them (99.9999% to be exact) are irrelevant. All that matters at the end of the day is the fact that 0 should be the min result. If your code thinks that root 5 is greater than root 6, or that root 234 is greater than root 235 it still won't break. There needs to be a race condition specifically with the iteration generating 0. The odds that one of the iterations has a race condition with another is very, very high. The odds that the iteration processing the last item has a race condition is really quite low.
Related
This is my first attempt at parallel programming.
I'm writing a test console app before using this in my real app and I can't seem to get it right. When I run this, the parallel search is always faster than the sequential one, but the parallel search never finds the correct value. What am I doing wrong?
I tried it without using a partitioner (just Parallel.For); it was slower than the sequential loop and gave the wrong number. I saw a Microsoft doc that said for simple computations, using Partitioner.Create can speed things up. So I tried that but still got the wrong values. Then I saw Interlocked, but I think I'm using it wrong.
Any help would be greatly appreciated
Random r = new Random();
Stopwatch timer = new Stopwatch();
do {
// Make and populate a list
List<short> test = new List<short>();
for (int x = 0; x <= 10000000; x++)
{
test.Add((short)(r.Next(short.MaxValue) * r.NextDouble()));
}
// Initialize result variables
short rMin = short.MaxValue;
short rMax = 0;
// Do min/max normal search
timer.Start();
foreach (var amp in test)
{
rMin = Math.Min(rMin, amp);
rMax = Math.Max(rMax, amp);
}
timer.Stop();
// Display results
Console.WriteLine($"rMin: {rMin} rMax: {rMax} Time: {timer.ElapsedMilliseconds}");
// Initialize parallel result variables
short pMin = short.MaxValue;
short pMax = 0;
// Create list partioner
var rangePortioner = Partitioner.Create(0, test.Count);
// Do min/max parallel search
timer.Restart();
Parallel.ForEach(rangePortioner, (range, loop) =>
{
short min = short.MaxValue;
short max = 0;
for (int i = range.Item1; i < range.Item2; i++)
{
min = Math.Min(min, test[i]);
max = Math.Max(max, test[i]);
}
_ = Interlocked.Exchange(ref Unsafe.As<short, int>(ref pMin), Math.Min(pMin, min));
_ = Interlocked.Exchange(ref Unsafe.As<short, int>(ref pMax), Math.Max(pMax, max));
});
timer.Stop();
// Display results
Console.WriteLine($"pMin: {pMin} pMax: {pMax} Time: {timer.ElapsedMilliseconds}");
Console.WriteLine("Press enter to run again; any other key to quit");
} while (Console.ReadKey().Key == ConsoleKey.Enter);
Sample output:
rMin: 0 rMax: 32746 Time: 106
pMin: 0 pMax: 32679 Time: 66
Press enter to run again; any other key to quit
The correct way to do a parallel search like this is to compute local values for each thread used, and then merge the values at the end. This ensures that synchronization is only needed at the final phase:
var items = Enumerable.Range(0, 10000).ToList();
int globalMin = int.MaxValue;
int globalMax = int.MinValue;
Parallel.ForEach<int, (int Min, int Max)>(
items,
() => (int.MaxValue, int.MinValue), // Create new min/max values for each thread used
(item, state, localMinMax) =>
{
var localMin = Math.Min(item, localMinMax.Min);
var localMax = Math.Max(item, localMinMax.Max);
return (localMin, localMax); // return the new min/max values for this thread
},
localMinMax => // called one last time for each thread used
{
lock(items) // Since this may run concurrently, synchronization is needed
{
globalMin = Math.Min(globalMin, localMinMax.Min);
globalMax = Math.Max(globalMax, localMinMax.Max);
}
});
As you can see this is quite a bit more complex than a regular loop, and this is not even doing anything fancy like partitioning. An optimized solution would work over larger blocks to reduce overhead, but this is omitted for simplicity, and it looks like the OP is aware such issues already.
Be aware that multi threaded programming is difficult. While it is a great idea to try out such techniques in a playground rather than a real program, I would still suggest that you should start by studying the potential dangers of thread safety, there is fairly easy to find good resources about this.
Not all problems will be as obviously wrong like this, and it is quite easy to cause issues that breaks once in a million, or only when the cpu load is high, or only on single CPU systems, or issues that are only detected long after the code is put into production. It is a good practice to be paranoid whenever multiple threads may read and write the same memory concurrently.
I would also recommend learning about immutable data types, and pure functions, since these are much safer and easier to reason about once multiple threads are involved.
Interlocked.Exchange is thread safe only for Exchange, every Math.Min and Math.Max can be with race condition. You should compute min/max for every batch separately and then join results.
Using low-lock techniques like the Interlocked class is tricky and advanced. Taking into consideration that your experience in multithreading is not excessive, I would say go with a simple and trusty lock:
object locker = new object();
//...
lock (locker)
{
pMin = Math.Min(pMin, min);
pMax = Math.Max(pMax, max);
}
I have a piece of code in my C# Windows Form Application that looks like below:
List<string> RESULT_LIST = new List<string>();
int[] arr = My_LIST.ToArray();
string s = "";
Stopwatch sw = new Stopwatch();
sw.Start();
for (int i = 0; i < arr.Length; i++)
{
int counter = i;
for (int j = 1; j <= arr.Length; j++)
{
counter++;
if (counter == arr.Length)
{
counter = 0;
}
s += arr[counter].ToString();
RESULT_LIST.Add(s);
}
s = "";
}
sw.Stop();
TimeSpan ts = sw.Elapsed;
string elapsedTime = String.Format("{0:00}", ts.TotalMilliseconds * 1000);
MessageBox.Show(elapsedTime);
I use this code to get any combination of the numbers of My list. I have behaved with My_LIST like a recursive one. The image below demonstrates my purpose very clearly:
All I need to do is:
Making a formula to calculate the approximate run time of these two
nested for loops to guess the run time for any length and help the
user know the approximate time that he/she must wait.
I have used a C# Stopwatch like this: Stopwatch sw = new Stopwatch(); to show the run time and below are the results(Note that in order to reduce the chance of error I've repeated the calculation three times for each length and the numbers show the time in nano seconds for the first, second and third attempt respectively.):
arr.Length = 400; 127838 - 107251 - 100898
arr.Length = 800; 751282 - 750574 - 739869
arr.Length = 1200; 2320517 - 2136107 - 2146099
arr.Length = 2000; 8502631 - 7554743 - 7635173
Note that there are only one-digit numbers in My_LIST to make the time
of adding numbers to the list approximately equal.
How can I find out the relation between arr.Length and run time?
First, let's suppose you have examined the algorithm and noticed that it appears to be quadratic in the array length. This suggests to us that the time taken to run should be a function of the form
t = A + B n + C n2
You've gathered some observations by running the code multiple times with different values for n and measuring t. That's a good approach.
The question now is: what are the best values for A, B and C such that they match your observations closely?
This problem can be solved in a variety of ways; I would suggest to you that the least-squares method of regression would be the place to start, and see if you get good results. There's a page on it here:
www.efunda.com/math/leastsquares/lstsqr2dcurve.cfm
UPDATE: I just looked at your algorithm again and realized it is cubic because you have a quadratic string concat in the inner loop. So this technique might not work so well. I suggest you use StringBuilder to make your algorithm quadratic.
Now, suppose you did not know ahead of time that the problem was quadratic. How would you determine the formula then? A good start would be to graph your points on log scale paper; if they roughly form a straight line then the slope of the line gives you a clue as to the power of the polynomial. If they don't form a straight line -- well, cross that bridge when you come to it.
Well you gonna do some math here.
Since the total number of runs is exactly n^2, not O(n^2) but exactly n^2 times.
Then what you could do is to keep a counter variable for the number of items processed and use math to find out an estimate
int numItemProcessed;
int timeElapsed;//read from stop watch
int totalItems = n * n;
int remainingEstimate = ((float) totalItems - numItemProcessed) / numItemProcessed) * timeElapsed
Don't assume the algorithm is necessarily N^2 in time complexity.
Take the averages of your numbers, and plot the best fit on a log-log plot, then measure the gradient. This will give you an idea as to the largest term in the polynomial. (see wikipedia log-log plot)
Once you have that, you can do a least-squares regression to work out the coefficients of the polynomial of the correct order. This will allow an estimate from the data, of the time taken for an unseen problem.
Note: As Eric Lippert said, it depends on what you want to measure - averaging may not be appropriate depending on your use case - the first run time might be more correct.
This method will work for any polynomial algorithm. It will also tell you if the algorithm is polynomial (non-polynomial running times will not give straight lines on the log-log plot).
This question already has answers here:
Parallel.For(): Update variable outside of loop
(7 answers)
Closed 6 years ago.
this is allmost my first attempt at parallel code, (first attempt worked fine and speeded up some code) but this below is causing strange issues and I cant see why. Both for loops below give the same result most of the time but not allways, i.e. res != res1. The function IdealGasEnthalpy is just calculating a number and not changing anything else, i cant figure out what the problem is or even where to begin to look, has anyone any suggestions?
double res = 0;
object lockObject = new object();
for (int I = 0; I < cc.Count; I++)
{
res += IdealGasEnthalpy(T, cc[I], enumMassOrMolar.Molar) * x[I];
}
double res1 = 0;
Parallel.For(0, cc.Count, I =>
{
res1 += IdealGasEnthalpy(T, cc[I], enumMassOrMolar.Molar) * x[I];
});
I tried the following code, but its very slow and doubled the execution time for the whole program compared to serial code.
double res = 0.0d;
Parallel.For(0, cc.Count,
() => 0.0d,
(x, loopState, partialResult) =>
{
return partialResult += IdealGasEnthalpy(T, cc[x], enumMassOrMolar.Molar) * X[x];
},
(localPartialSum) =>
{
lock (lockObject)
{
res += localPartialSum;
}
});
Also tried this below, going to stick to non-parallel for this routine as the parallel versions are all a lot slower...
double res = 0.0d;
double[] partialresult = new double[cc.Count];
Parallel.For(0, cc.Count, i =>
{
partialresult[i] = IdealGasEnthalpy(T, cc[i], enumMassOrMolar.Molar) * X[i];
});
for (int i = 0; i < cc.Count; i++)
{
res += partialresult[i];
}*
Your second operation needs to do an interlocked add, because += is not atomic. Remember this is shorthand for read the variable, add to it, and store the result. There is a race condition where two reads of the same old value could occur before either has stored the new result. You need to synchronize access.
Note that, depending on how computationally expensive your function is, interlocking with the Parallel.For approach might be slower than just doing a serial approach. It comes down to how much time is spent calculating the value versus how much time is spent synchronizing and doing the summation.
Alternately you could store the results in an array which you allocate in advance, then do the summation after all parallel operations are done. That way no two operations modify the same variable. The array trades memory for speed, since you eliminate overhead from synchronization.
The title may seem a little odd, because I have no idea how to describe this in one sentence.
For the course Algorithms we have to micro-optimize some stuff, one is finding out how deleting from an array works. The assignment is delete something from an array and re-align the contents so that there are no gaps, I think it is quite similar to how std::vector::erase works from c++.
Because I like the idea of understanding everything low-level, I went a little further and tried to bench my solutions. This presented some weird results.
At first, here is a little code that I used:
class Test {
Stopwatch sw;
Obj[] objs;
public Test() {
this.sw = new Stopwatch();
this.objs = new Obj[1000000];
// Fill objs
for (int i = 0; i < objs.Length; i++) {
objs[i] = new Obj(i);
}
}
public void test() {
// Time deletion
sw.Restart();
deleteValue(400000, objs);
sw.Stop();
// Show timings
Console.WriteLine(sw.Elapsed);
}
// Delete function
// value is the to-search-for item in the list of objects
private static void deleteValue(int value, Obj[] list) {
for (int i = 0; i < list.Length; i++) {
if (list[i].Value == value) {
for (int j = i; j < list.Length - 1; j++) {
list[j] = list[j + 1];
//if (list[j + 1] == null) {
// break;
//}
}
list[list.Length - 1] = null;
break;
}
}
}
}
I would just create this class and call the test() method. I did this in a loop for 25 times.
My findings:
The first round it takes a lot longer than the other 24, I think this is because of caching, but I am not sure.
When I use a value that is in the start of the list, it has to move more items in memory than when I use a value at the end, though it still seems to take less time.
Benchtimes differ quite a bit.
When I enable the commented if, performance goes up (10-20%) even if the value I search for is almost at the end of the list (which means the if goes off a lot of times without actually being useful).
I have no idea why these things happen, is there someone who can explain (some of) them? And maybe if someone sees this who is a pro at this, where can I find more info to do this the most efficient way?
Edit after testing:
I did some testing and found some interesting results. I run the test on an array with a size of a million items, filled with a million objects. I run that 25 times and report the cumulative time in milliseconds. I do that 10 times and take the average of that as a final value.
When I run the test with my function described just above here I get a score of:
362,1
When I run it with the answer of dbc I get a score of:
846,4
So mine was faster, but then I started to experiment with a half empty empty array and things started to get weird. To get rid of the inevitable nullPointerExceptions I added an extra check to the if (thinking it would ruin a bit more of the performance) like so:
if (fromItem != null && fromItem.Value != value)
list[to++] = fromItem;
This seemed to not only work, but improve performance dramatically! Now I get a score of:
247,9
The weird thing is, the scores seem to low to be true, but sometimes spike, this is the set I took the avg from:
94, 26, 966, 36, 632, 95, 47, 35, 109, 439
So the extra evaluation seems to improve my performance, despite of doing an extra check. How is this possible?
You are using Stopwatch to time your method. This calculates the total clock time taken during your method call, which could include the time required for .Net to initially JIT your method, interruptions for garbage collection, or slowdowns caused by system loads from other processes. Noise from these sources will likely dominate noise due to cache misses.
This answer gives some suggestions as to how you can minimize some of the noise from garbage collection or other processes. To eliminate JIT noise, you should call your method once without timing it -- or show the time taken by the first call in a separate column in your results table since it will be so different. You might also consider using a proper profiler which will report exactly how much time your code used exclusive of "noise" from other threads or processes.
Finally, I'll note that your algorithm to remove matching items from an array and shift everything else down uses a nested loop, which is not necessary and will access items in the array after the matching index twice. The standard algorithm looks like this:
public static void RemoveFromArray(this Obj[] array, int value)
{
int to = 0;
for (int from = 0; from < array.Length; from++)
{
var fromItem = array[from];
if (fromItem.Value != value)
array[to++] = fromItem;
}
for (; to < array.Length; to++)
{
array[to] = default(Obj);
}
}
However, instead of using the standard algorithm you might experiment by using Array.RemoveAt() with your version, since (I believe) internally it does the removal in unmanaged code.
I have a local class with a method used to build a list of strings and I'm finding that when I hit this method (in a for loop of 1000 times) often it's not returning the amount I request.
I have a global variable:
string[] cachedKeys
A parameter passed to the method:
int requestedNumberToGet
The method looks similar to this:
List<string> keysToReturn = new List<string>();
int numberPossibleToGet = (cachedKeys.Length <= requestedNumberToGet) ?
cachedKeys.Length : requestedNumberToGet;
Random rand = new Random();
DateTime breakoutTime = DateTime.Now.AddMilliseconds(5);
//Do we have enough to fill the request within the time? otherwise give
//however many we currently have
while (DateTime.Now < breakoutTime
&& keysToReturn.Count < numberPossibleToGet
&& cachedKeys.Length >= numberPossibleToGet)
{
string randomKey = cachedKeys[rand.Next(0, cachedKeys.Length)];
if (!keysToReturn.Contains(randomKey))
keysToReturn.Add(randomKey);
}
if (keysToReturn.Count != numberPossibleToGet)
Debugger.Break();
I have approximately 40 strings in cachedKeys none exceeding 15 characters in length.
I'm no expert with threading so I'm literally just calling this method 1000 times in a loop and consistently hitting that debug there.
The machine this is running on is a fairly beefy desktop so I would expect the breakout time to be realistic, in fact it randomly breaks at any point of the loop (I've seen 20s, 100s, 200s, 300s).
Any one have any ideas where I'm going wrong with this?
Edit: Limited to .NET 2.0
Edit: The purpose of the breakout is so that if the method is taking too long to execute, the client (several web servers using the data for XML feeds) won't have to wait while the other project dependencies initialise, they'll just be given 0 results.
Edit: Thought I'd post the performance stats
Original
'0.0042477465711424217323710136' - 10
'0.0479597267250446634977350473' - 100
'0.4721072091564710039963179678' - 1000
Skeet
'0.0007076318358897569383818334' - 10
'0.007256508857969378789762386' - 100
'0.0749829936486341141122684587' - 1000
Freddy Rios
'0.0003765841748043396576939248' - 10
'0.0046003053460705201359390649' - 100
'0.0417058592642360970458535931' - 1000
Why not just take a copy of the list - O(n) - shuffle it, also O(n) - and then return the number of keys that have been requested. In fact, the shuffle only needs to be O(nRequested). Keep swapping a random member of the unshuffled bit of the list with the very start of the unshuffled bit, then expand the shuffled bit by 1 (just a notional counter).
EDIT: Here's some code which yields the results as an IEnumerable<T>. Note that it uses deferred execution, so if you change the source that's passed in before you first start iterating through the results, you'll see those changes. After the first result is fetched, the elements will have been cached.
static IEnumerable<T> TakeRandom<T>(IEnumerable<T> source,
int sizeRequired,
Random rng)
{
List<T> list = new List<T>(source);
sizeRequired = Math.Min(sizeRequired, list.Count);
for (int i=0; i < sizeRequired; i++)
{
int index = rng.Next(list.Count-i);
T selected = list[i + index];
list[i + index] = list[i];
list[i] = selected;
yield return selected;
}
}
The idea is that at any point after you've fetched n elements, the first n elements of the list will be those elements - so we make sure that we don't pick those again. When then pick a random element from "the rest", swap it to the right position and yield it.
Hope this helps. If you're using C# 3 you might want to make this an extension method by putting "this" in front of the first parameter.
The main issue are the using retries in a random scenario to ensure you get unique values. This quickly gets out of control, specially if the amount of items requested is near to the amount of items to get i.e. if you increase the amount of keys, you will see the issue less often but that can be avoided.
The following method does it by keeping a list of the keys remaining.
List<string> GetSomeKeys(string[] cachedKeys, int requestedNumberToGet)
{
int numberPossibleToGet = Math.Min(cachedKeys.Length, requestedNumberToGet);
List<string> keysRemaining = new List<string>(cachedKeys);
List<string> keysToReturn = new List<string>(numberPossibleToGet);
Random rand = new Random();
for (int i = 0; i < numberPossibleToGet; i++)
{
int randomIndex = rand.Next(keysRemaining.Count);
keysToReturn.Add(keysRemaining[randomIndex]);
keysRemaining.RemoveAt(randomIndex);
}
return keysToReturn;
}
The timeout was necessary on your version as you could potentially keep retrying to get a value for a long time. Specially when you wanted to retrieve the whole list, in which case you would almost certainly get a fail with the version that relies on retries.
Update: The above performs better than these variations:
List<string> GetSomeKeysSwapping(string[] cachedKeys, int requestedNumberToGet)
{
int numberPossibleToGet = Math.Min(cachedKeys.Length, requestedNumberToGet);
List<string> keys = new List<string>(cachedKeys);
List<string> keysToReturn = new List<string>(numberPossibleToGet);
Random rand = new Random();
for (int i = 0; i < numberPossibleToGet; i++)
{
int index = rand.Next(numberPossibleToGet - i) + i;
keysToReturn.Add(keys[index]);
keys[index] = keys[i];
}
return keysToReturn;
}
List<string> GetSomeKeysEnumerable(string[] cachedKeys, int requestedNumberToGet)
{
Random rand = new Random();
return TakeRandom(cachedKeys, requestedNumberToGet, rand).ToList();
}
Some numbers with 10.000 iterations:
Function Name Elapsed Inclusive Time Number of Calls
GetSomeKeys 6,190.66 10,000
GetSomeKeysEnumerable 15,617.04 10,000
GetSomeKeysSwapping 8,293.64 10,000
A few thoughts.
First, your keysToReturn list is potentially being added to each time through the loop, right? You're creating an empty list and then adding each new key to the list. Since the list was not pre-sized, each add becomes an O(n) operation (see MSDN documentation). To fix this, try pre-sizing your list like this.
int numberPossibleToGet = (cachedKeys.Length <= requestedNumberToGet) ? cachedKeys.Length : requestedNumberToGet;
List<string> keysToReturn = new List<string>(numberPossibleToGet);
Second, your breakout time is unrealistic (ok, ok, impossible) on Windows. All of the information I've ever read on Windows timing suggests that the best you can possibly hope for is 10 millisecond resolution, but in practice it's more like 15-18 milliseconds. In fact, try this code:
for (int iv = 0; iv < 10000; iv++) {
Console.WriteLine( DateTime.Now.Millisecond.ToString() );
}
What you'll see in the output are discrete jumps. Here is a sample output that I just ran on my machine.
13
...
13
28
...
28
44
...
44
59
...
59
75
...
The millisecond value jumps from 13 to 28 to 44 to 59 to 75. That's roughly a 15-16 millisecond resolution in the DateTime.Now function for my machine. This behavior is consistent with what you'd see in the C runtime ftime() call. In other words, it's a systemic trait of the Windows timing mechanism. The point is, you should not rely on a consistent 5 millisecond breakout time because you won't get it.
Third, am I right to assume that the breakout time is prevent the main thread from locking up? If so, then it'd be pretty easy to spawn off your function to a ThreadPool thread and let it run to completion regardless of how long it takes. Your main thread can then operate on the data.
Use HashSet instead, HashSet is much faster for lookup than List
HashSet<string> keysToReturn = new HashSet<string>();
int numberPossibleToGet = (cachedKeys.Length <= requestedNumberToGet) ? cachedKeys.Length : requestedNumberToGet;
Random rand = new Random();
DateTime breakoutTime = DateTime.Now.AddMilliseconds(5);
int length = cachedKeys.Length;
while (DateTime.Now < breakoutTime && keysToReturn.Count < numberPossibleToGet) {
int i = rand.Next(0, length);
while (!keysToReturn.Add(cachedKeys[i])) {
i++;
if (i == length)
i = 0;
}
}
Consider using Stopwatch instead of DateTime.Now. It may simply be down to the inaccuracy of DateTime.Now when you're talking about milliseconds.
The problem could quite possibly be here:
if (!keysToReturn.Contains(randomKey))
keysToReturn.Add(randomKey);
This will require iterating over the list to determine if the key is in the return list. However, to be sure, you should try profiling this using a tool. Also, 5ms is pretty fast at .005 seconds, you may want to increase that.