Develop Reference

Parallel for loop with BigINtegers

So i am working on a program to factor a given number. you write a number in the command line and it gives you all the factors. now, doing this sequentially is VERY slow. because it uses only one thread, if i'm not mistaking. now, i thought about doing it with Parallel.For and it worked, only with integers, so wanted to try it with BigIntegers
heres the normal code:
public static void Factor(BigInteger f)
{
for(BigInteger i = 1; i <= f; i++)
{
if (f % i == 0)
{
Console.WriteLine("{0} is a factor of {1}", i, f);
}
}
}
the above code should be pretty easy to understand. but as i said, this Very slow for big numbers (above one billion it starts to get impractical) and heres my parallel code:
public static void ParallelFacotr(BigInteger d)
{
Parallel.For<BigInteger>(0, d, new ParallelOptions() { MaxDegreeOfParallelism = Environment.ProcessorCount }, i =>
{
try
{
if (d % i == 0)
{
Console.WriteLine("{0} is a factor of {1}", i, d);
}
}
catch (DivideByZeroException)
{
Console.WriteLine("Done!");
Console.ReadKey();
Launcher.Main();
}
});
}
now, the above code (parallel one) works just fine with integers (int) but its VERY fast. it factored 1000.000.000 in just 2 seconds. so i thought why not try it with bigger integers. and also, i thought that putting <Biginteger> after the parallel.for would do it. but it doesn't. so, how do you work with bigintegers in parallel for loop? and i already tried just a regular parallel loop with a bigInteger as the argument, but then it gives an error saying that it cannot convert from BigInteger to int. so how do you

Improve your algorithm efficiency first.
While it is possible to use BigInteger you will not have CPU ALU arithmetical logic to resolve arbitrarily big numbers logic in hardware, so it will be noticeably slower. So unless you need bigger numbers than 9 quintillion or exactly 9,223,372,036,854,775,807 then you can use type long.
A second thing to not is that you do not need to loop over all elements as it needs to be multiple of something, so you can reduce
for(BigInteger i = 1; i <= f; i++)
for(long i = 1; i <= Math.Sqrt(f); i++)
That would mean that instead of needing to iterate over 1000000000 items you iterate over 31623.
Additionally, if you still plan on using BigInt then check the parameters:
It should be something in the lines of
Parallel.For(
0,
(int)d,
() => BigInteger.Zero,
(x, state, subTotal) => subTotal + BigInteger.One,
Just for trivia. Some programming languages are more efficient in solving problems than others and in this case, there is a languages Wolfram (previously Mathematica) when solving problems is simpler, granted that you know what you are doing.
However they do have google alternative that answers you directly and it has a decent AI that processes your natural language to give you an exact answer as best as it could.
So finding factors of numbers is easy as:
Factor[9223372036854775809]
or use web api https://www.wolframalpha.com/input/?i=factor+9223372036854775809
You can also call Wolfram kernel from C#, but terms and conditions apply.

What is the best way to use Parallel.For in a recursive algorithm?

I am building software to evaluate many possible solutions and am trying to introduce parallel processing to speed up the calculations. My first attempt was to build a datatable with each row being a solution to evaluate but building the datatable takes quite some time and I am running into memory issues when the number of possible solutions goes into the millions.
The problem which warrants these solutions is structured as follows:
There is a range dates for x number of events which must be done in order. The solutions to evaluate could look as follows with each solution being a row, the events being the columns and the day number being the values.
Given 3 days (0 to 2) and three events:
0 0 0
0 0 1
0 0 2
0 1 1
0 1 2
0 2 2
1 1 1
1 1 2
1 2 2
2 2 2
My new plan was to use recursion and evaluate the solutions as I go rather than build a solution set to then evaluate.
for(int day = 0; day < maxdays; day++)
{
List<int> mydays = new List<int>();
mydays.Add(day);
EvalEvent(0,day,mydays);
}
private void EvalEvent(int eventnum,
int day, List<int> mydays)
{
Parallel.For(day,maxdays, day2 =>
// events must be on same day or after previous events
{
List<int> mydays2 = new List<int>();
for(int a = 0; a <mydays.Count;a++)
{
mydays2.Add(mydays[a]);
}
mydays2.Add(day2);
if(eventnum< eventcount - 1) // proceed to next event
{
EvalEvent(eventnum+1, day2,mydays2);
}
else
{
EvalSolution(mydays2);
}
});
}
My question is if this is actually an efficient use of parallel processing or will too many threads be spawned and slow it down? Should the parallel loop only be done on the last or maybe last few values of eventnum or is there a better way to approach the problem?
Requested old code pretty much is as follows:
private int daterange;
private int events;
private void ScheduleIt()
{
daterange = 10;
events = 6;
CreateSolutions();
int best = GetBest();
}
private DataTable Options();
private bool CreateSolutions()
{
Options= new DataTable();
Options.Columns.Add();
for (int day1=0;day1<=daterange ;day1++)
{
Options.Rows.Add(day1);
}
for (int event =1; event<events; event++)
{
Options.Columns.Add();
foreach(DataRow dr in Options.Rows)
{dr[Options.Columns.Count-1] = dr[Options.Columns.Count-2] ;}
int rows = Options.Rows.Count;
for (int day1=1;day1<=daterange ;day1++)
{
for(int i = 0; i <rows; i++)
{
if(day1 > Convert.ToInt32(Options.Rows[i][Options.Columns.Count-2]))
{
try{
Options.Rows.Add();
for (int col=0;col<Options.Columns.Count-1;col++)
{
Options.Rows[Options.Rows.Count-1][col] =Options.Rows[i][col];
}
Options.Rows[Options.Rows.Count-1][Options.Columns.Count-1] = day1;
}
catch(Exception ex)
{
return false;
}
}
}
}
}
return true;
}
private intGetBest()
{
int bestopt = 0;
double bestscore =999999999;
Parallel.For( 0, Options.Rows.Count,opt =>
{
double score = 0;
for(int i = 0; i <Options.Columns.Count;i++)
{score += Options.Rows[opt][i]}// just a stand in calc for a score
if (score < bestscore)
{bestscore = score;
bestopt = opt;
}
});
return bestopt;
}

Even if done without errors it can not significantly speed up your solution.
It looks like each level of recursion tries to start multiple (let say up to "k") next level calls for let's "n" level. This essentially mean code is O(k ^ n) which grows very fast. Non-algorithmic speedup of such O(k^n) solution is essentially useless (unless both k and n are very small). In particular, running code in parallel will only give you constant factor of speed up (roughly number of threads supported by your CPUs) which really does not change complexity at all.
Indeed creation of exponentially large number of requests for new threads will likely cause more problems by itself for just managing threads.
In addition to not significantly improving performance parallel code is harder to write as it needs proper synchronization or cleaver data partitioning - neither seem to be present in your case.

Parallelization works best when the workload is bulky and balanced. Ideally you would like your work splited to as many independent partitions as the logical processors of your machine, ensuring that all partitions have approximately the same size. This way all available processors will work with the maximum efficiency for approximately the same duration, and you'll get the results after the shortest time possible.
Of course you should start with a working and bug-free serial implementation, and then think about ways to partition your work. The easiest way is usually not optimal. For example an easy path is to convert your work to a LINQ query, and then parallelize it with AsParallel() (making it PLINQ). This usually results to a too granular partitioning, that introduces too much overhead. If you can't find ways to improve it you then can go the way of Parallel.For or Parallel.ForEach, which is a bit more complex.
A LINQ implementation should probably start by creating an iterator that produces all your units of work (Events or Solutions, it's not very clear to me).
public static IEnumerable<Solution> GetAllSolutions()
{
for (int day = 0; day < 3; day++)
{
for (int ev = 0; ev < 3; ev++)
{
yield return new Solution(); // ???
}
}
}
It will certainly be helpful if you have created concrete classes to represent the entities you are dealing with.

Should try-catch be avoided for known cases

I have a case which I know will happen but very scarce. For example in every 10 thousand times the code runs, this might happen once.
I can check for this case by a simple if but this if will run many times with no use.
On the other hand I can place the code in try-catch block and when that special case happens I do what is needed to recover.
The question is which one is better? I know that generally speaking try-catch should not be used for known cases because of the overhead issue and also the application logic should not rely on catch code, but running an if multiple times will have more performance issue. I have tested this using this small test code:
static void Main(string[] args)
{
Stopwatch sc = new Stopwatch();
var list = new List<int>();
var rnd = new Random();
for (int i = 0; i < 100000000; i++)
{
list.Add(rnd.Next());
}
sc.Start();
DoWithIf(list);
sc.Stop();
Console.WriteLine($"Done with IFs in {sc.ElapsedMilliseconds} milliseconds");
sc.Restart();
DoWithTryCatch(list);
sc.Stop();
Console.WriteLine($"Done with TRY-CATCH in {sc.ElapsedMilliseconds} milliseconds");
Console.ReadKey();
}
private static int[] DoWithTryCatch(List<int> list)
{
var res = new int[list.Count ];
try
{
for (int i = 0; i < list.Count; i++)
{
res[i] = list[i];
}
return res;
}
catch
{
return res;
}
}
private static int[] DoWithIf(List<int> list)
{
var res = new int[list.Count - 1];
for (int i = 0; i < list.Count; i++)
{
if (i < res.Length)
res[i] = list[i];
}
return res;
}
This code simply copies a lot of numbers to an array with not enough size. In my machine checking array bounds each time takes around 210 milliseconds to run while using try-catch that will hit catch once runs in around 190 milliseconds.
Also if you think it depends on the case my case is that I get push notifications in an app and will check if I have the topic of the message. If not I will get and store the topic information for next messages. There are many messages in few topics.

So, it would be accurate to say that in your test, the if option was slower than the try...catch option by 20 milliseconds, for a loop of 100000000 times.
That translates to 20 / 100,000,000 - that's 0.0000002 milliseconds for each iteration.
Do you really think that kind of nano-optimization is worth writing code that goes goes against proper design standards?
Exceptions are for exceptional cases, the things that you can't control or can't test in advance - for instance, when you are reading data from a database and the connection terminates in the middle - stuff like that.
Using exceptions for things that can be easily tested with simple code - well, that's just plain wrong.
If, for instance, you would have demonstrated a meaningful performance difference between these two options then perhaps you could justify using try...catch instead of if - but that's clearly not the case here.
So, to summarize - use if, not try...catch.
You should design your code for clarity, not for performance.
Write code that conveys the algorithm it is implementing in the clearest way possible.
Set performance goals and measure your code's performance against them.
If your code doesn't measure to your performance goals, Find the bottle necks and treat them.
Don't go wasting your time on nano-optimizations when you design the code.

In your case, you have somehow missed the obvious optimization: if you worry that calling an if 100.000 times is too much... don't?
private static int[] DoWithIf(List<int> list)
{
var res = new int[list.Count - 1];
var bounds = Math.Min(res.Length, list.Count)
for (int i = 0; i < bounds; i++)
{
res[i] = list[i];
}
return res;
}
So I know this is only a test case, but the answer is: optimize if you need it and for what you need it. If you have something in a loop that's supposedly costly, then try to move it out of the loop. Optimize based on logic, not based on compiler constructs. If you are down to optimizing compiler constructs, you should not be coding in a managed and/or high level language anyway.

Deleting from array, mirrored (strange) behavior

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.

How expensive are exceptions in C#?

How expensive are exceptions in C#? It seems like they are not incredibly expensive as long as the stack is not deep; however I have read conflicting reports.
Is there definitive report that hasn't been rebutted?

Having read that exceptions are costly in terms of performance I threw together a simple measurement program, very similar to the one Jon Skeet published years ago. I mention this here mainly to provide updated numbers.
It took the program below 29914 milliseconds to process one million exceptions, which amounts to 33 exceptions per millisecond. That is fast enough to make exceptions a viable alternative to return codes for most situations.
Please note, though, that with return codes instead of exceptions the same program runs less than one millisecond, which means exceptions are at least 30,000 times slower than return codes. As stressed by Rico Mariani these numbers are also minimum numbers. In practice, throwing and catching an exception will take more time.
Measured on a laptop with Intel Core2 Duo T8100 # 2,1 GHz with .NET 4.0 in release build not run under debugger (which would make it way slower).
This is my test code:
static void Main(string[] args)
{
int iterations = 1000000;
Console.WriteLine("Starting " + iterations.ToString() + " iterations...\n");
var stopwatch = new Stopwatch();
// Test exceptions
stopwatch.Reset();
stopwatch.Start();
for (int i = 1; i <= iterations; i++)
{
try
{
TestExceptions();
}
catch (Exception)
{
// Do nothing
}
}
stopwatch.Stop();
Console.WriteLine("Exceptions: " + stopwatch.ElapsedMilliseconds.ToString() + " ms");
// Test return codes
stopwatch.Reset();
stopwatch.Start();
int retcode;
for (int i = 1; i <= iterations; i++)
{
retcode = TestReturnCodes();
if (retcode == 1)
{
// Do nothing
}
}
stopwatch.Stop();
Console.WriteLine("Return codes: " + stopwatch.ElapsedMilliseconds.ToString() + " ms");
Console.WriteLine("\nFinished.");
Console.ReadKey();
}
static void TestExceptions()
{
throw new Exception("Failed");
}
static int TestReturnCodes()
{
return 1;
}

I guess I'm in the camp that if performance of exceptions impacts your application then you're throwing WAY too many of them. Exceptions should be for exceptional conditions, not as routine error handling.
That said, my recollection of how exceptions are handled is essentially walking up the stack finding a catch statement that matches the type of the exception thrown. So performance will be impacted most by how deep you are from the catch and how many catch statements you have.

In my case, exceptions were very expensive. I rewrote this:
public BlockTemplate this[int x,int y, int z]
{
get
{
try
{
return Data.BlockTemplate[World[Center.X + x, Center.Y + y, Center.Z + z]];
}
catch(IndexOutOfRangeException e)
{
return Data.BlockTemplate[BlockType.Air];
}
}
}
Into this:
public BlockTemplate this[int x,int y, int z]
{
get
{
int ix = Center.X + x;
int iy = Center.Y + y;
int iz = Center.Z + z;
if (ix < 0 || ix >= World.GetLength(0)
|| iy < 0 || iy >= World.GetLength(1)
|| iz < 0 || iz >= World.GetLength(2))
return Data.BlockTemplate[BlockType.Air];
return Data.BlockTemplate[World[ix, iy, iz]];
}
}
And I noticed a good speed increase of about 30 seconds. This function gets called at least 32,000 times at startup. The code isn't as clear as to what the intention is, but the cost savings were huge.

I did my own measurements to find out how serious the exceptions implication were. I didn't try to measure the absolute time for throwing/catching exception. I was mostly interested in how much slower a loop will become if an exception is thrown in each pass. The measuring code looks like this:
for(; ; ) {
iValue = Level1(iValue);
lCounter += 1;
if(DateTime.Now >= sFinish)
break;
}
vs.
for(; ; ) {
try {
iValue = Level3Throw(iValue);
}
catch(InvalidOperationException) {
iValue += 3;
}
lCounter += 1;
if(DateTime.Now >= sFinish)
break;
}
The difference is 20 times. The second snippet is 20 times slower.

Barebones exception objects in C# are fairly lightweight; it's usually the ability to encapsulate an InnerException that makes it heavy when the object tree becomes too deep.
As for a definitive report, I'm not aware of any, although a cursory dotTrace profile (or any other profiler) for memory consumption and speed will be fairly easy to do.

Just to give my personal experience:
I'm working on a program that parses JSON files and extracts data from them, with Newtonsoft (Json.NET).
I rewrote this:
Option 1, with exceptions
try
{
name = rawPropWithChildren.Value["title"].ToString();
}
catch(System.NullReferenceException)
{
name = rawPropWithChildren.Name;
}
To this:
Option 2, without exceptions
if(rawPropWithChildren.Value["title"] == null)
{
name = rawPropWithChildren.Name;
}
else
{
name = rawPropWithChildren.Value["title"].ToString();
}
Of course, you don't really have context to judge about it, but here are my results (in debug mode):
Option 1, with exceptions.
38.50 seconds
Option 2, without exceptions.
06.48 seconds
To give a little bit of context, I'm working with thousands of JSON properties that can be null. Exceptions were thrown way too much, like maybe during 15% of the execution time. Well, not really precise, but they were thrown too many times.
I wanted to fix this, so I changed my code, and I did not understand why the execution time was so much faster. That was because of my poor exception handling.
So, what I've learned from this: I need to use exceptions only in particular cases and for things that can't be tested with a simple conditional statement. They also must be thrown the less often possible.
This is a kind of a random story for you, but I guess I would definitely think twice before using exceptions in my code from now on!

The performance hit with exceptions seems to be at the point of generating the exception object (albeit too small to cause any concerns 90% of the time). The recommendation therefore is to profile your code - if exceptions are causing a performance hit, you write a new high-perf method that does not use exceptions. (An example that comes to mind would be (TryParse introduced to overcome perf issues with Parse which uses exceptions)
THat said, exceptions in most cases do not cause significant performance hits in most situations - so the MS Design Guideline is to report failures by throwing exceptions

TLDR;
The answer should always start by answering "expensive compared to what?"
Exceptions are likely orders of magnitude faster than any connected service or data call so it's unlikely that avoiding their use is providing a tangible benefit over the improved information and control flow that they provide.
Throwing an exception can be measured in MICROseconds (but it depends on stack depth):
Image from this article and he posts the testing code: .Net exceptions performance
Do you generally get what you pay for? Most of the time yes.
Longer Explanation:
I'm quite interested in the origins of this question. As far as I can tell it is residual distaste for marginally useful c++ exceptions. .Net exceptions have a wealth of info in them and allow for neat and tidy code without excessive checks for success and logging. I explain much of the benefit of exceptions in another answer.
In 20 years of programming, I've never removed a throw or a catch to make something faster (not to say that I couldn't, just to say that there was lower hanging fruit and after that, nobody complained).
There is a separate question with competing answers, one catching an exception (no "Try" method was provided by the built-in method) and one that avoided exceptions.
I decided to do a head to head performance comparison of the two, and for a smaller number of columns the non-exception version was faster, but the exception version scaled better and eventually outperformed the exception-avoiding version:
The linqpad code for that test is below (including the graph rendering).
The point here though, is that this idea of "exceptions are slow" begs the question of "slower than what?" If a deep-stack exception costs 500 microseconds, does it matter if it occurs in response to a unique constraint that took a database 3000 microseconds to create? In any case, this demonstrates a generalized avoiding of exceptions for performance reasons will not necessarily yield more performant code.
Code for performance test:
void Main()
{
var loopResults = new List<Results>();
var exceptionResults = new List<Results>();
var totalRuns = 10000;
for (var colCount = 1; colCount < 20; colCount++)
{
using (var conn = new SqlConnection(#"Data Source=(localdb)\MSSQLLocalDb;Initial Catalog=master;Integrated Security=True;"))
{
conn.Open();
//create a dummy table where we can control the total columns
var columns = String.Join(",",
(new int[colCount]).Select((item, i) => $"'{i}' as col{i}")
);
var sql = $"select {columns} into #dummyTable";
var cmd = new SqlCommand(sql,conn);
cmd.ExecuteNonQuery();
var cmd2 = new SqlCommand("select * from #dummyTable", conn);
var reader = cmd2.ExecuteReader();
reader.Read();
Func<Func<IDataRecord, String, Boolean>, List<Results>> test = funcToTest =>
{
var results = new List<Results>();
Random r = new Random();
for (var faultRate = 0.1; faultRate <= 0.5; faultRate += 0.1)
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
var faultCount=0;
for (var testRun = 0; testRun < totalRuns; testRun++)
{
if (r.NextDouble() <= faultRate)
{
faultCount++;
if(funcToTest(reader, "colDNE"))
throw new ApplicationException("Should have thrown false");
}
else
{
for (var col = 0; col < colCount; col++)
{
if(!funcToTest(reader, $"col{col}"))
throw new ApplicationException("Should have thrown true");
}
}
}
stopwatch.Stop();
results.Add(new UserQuery.Results{
ColumnCount = colCount,
TargetNotFoundRate = faultRate,
NotFoundRate = faultCount * 1.0f / totalRuns,
TotalTime=stopwatch.Elapsed
});
}
return results;
};
loopResults.AddRange(test(HasColumnLoop));
exceptionResults.AddRange(test(HasColumnException));
}
}
"Loop".Dump();
loopResults.Dump();
"Exception".Dump();
exceptionResults.Dump();
var combinedResults = loopResults.Join(exceptionResults,l => l.ResultKey, e=> e.ResultKey, (l, e) => new{ResultKey = l.ResultKey, LoopResult=l.TotalTime, ExceptionResult=e.TotalTime});
combinedResults.Dump();
combinedResults
.Chart(r => r.ResultKey, r => r.LoopResult.Milliseconds * 1.0 / totalRuns, LINQPad.Util.SeriesType.Line)
.AddYSeries(r => r.ExceptionResult.Milliseconds * 1.0 / totalRuns, LINQPad.Util.SeriesType.Line)
.Dump();
}
public static bool HasColumnLoop(IDataRecord dr, string columnName)
{
for (int i = 0; i < dr.FieldCount; i++)
{
if (dr.GetName(i).Equals(columnName, StringComparison.InvariantCultureIgnoreCase))
return true;
}
return false;
}
public static bool HasColumnException(IDataRecord r, string columnName)
{
try
{
return r.GetOrdinal(columnName) >= 0;
}
catch (IndexOutOfRangeException)
{
return false;
}
}
public class Results
{
public double NotFoundRate { get; set; }
public double TargetNotFoundRate { get; set; }
public int ColumnCount { get; set; }
public double ResultKey {get => ColumnCount + TargetNotFoundRate;}
public TimeSpan TotalTime { get; set; }
}

I recently measured C# exceptions (throw and catch) in a summation loop that threw an arithmetic overflow on every addition. Throw and catch of arithmetic overflow was around 8.5 microseconds = 117 KiloExceptions/second, on a quad-core laptop.

Exceptions are expensive, but there is more to it when you want to choose between exception and return codes.
Historically speaking the argument was: exceptions ensure that code is forced to handle the situation whereas return codes can be ignored. I never favoured these arguments as no programmer will want to ignore and break their codes on purpose - especially a good test team / or a well written test case will definitely not ignore return codes.
From a modern programming practices point of view, managing exceptions need to be looked at not only for their cost, but also for their viability.
First
Since most front ends will be disconnected from the API that is throwing exception. For example, a mobile app using a REST API. The same API can also be used for an Angular-based web frontend.
Either scenario will prefer return codes instead of exceptions.
Second
Nowadays, hackers randomly attempt to break all web utilities. In such a scenario, if they are constantly attacking your app's login API and if the app is constantly throwing exceptions, then you will end up dealing with thousands of exceptions a day. Of course, many will say the firewall will take care of such attacks. However, not all are spending money to manage a dedicated firewall or an expensive anti-spam service. It is better that your code is prepared for these scenarios.