C# Performance Pitfall – Interop Scenarios Change the Rules
- by Reed
C# and .NET, overall, really do have fantastic performance in my opinion. That being said, the performance characteristics dramatically differ from native programming, and take some relearning if you’re used to doing performance optimization in most other languages, especially C, C++, and similar. However, there are times when revisiting tricks learned in native code play a critical role in performance optimization in C#.
I recently ran across a nasty scenario that illustrated to me how dangerous following any fixed rules for optimization can be…
The rules in C# when optimizing code are very different than C or C++. Often, they’re exactly backwards. For example, in C and C++, lifting a variable out of loops in order to avoid memory allocations often can have huge advantages. If some function within a call graph is allocating memory dynamically, and that gets called in a loop, it can dramatically slow down a routine.
This can be a tricky bottleneck to track down, even with a profiler. Looking at the memory allocation graph is usually the key for spotting this routine, as it’s often “hidden” deep in call graph. For example, while optimizing some of my scientific routines, I ran into a situation where I had a loop similar to:
for (i=0; i<numberToProcess; ++i)
{
// Do some work
ProcessElement(element[i]);
}
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This loop was at a fairly high level in the call graph, and often could take many hours to complete, depending on the input data. As such, any performance optimization we could achieve would be greatly appreciated by our users.
After a fair bit of profiling, I noticed that a couple of function calls down the call graph (inside of ProcessElement), there was some code that effectively was doing:
// Allocate some data required
DataStructure* data = new DataStructure(num);
// Call into a subroutine that passed around and manipulated this data highly
CallSubroutine(data);
// Read and use some values from here
double values = data->Foo;
// Cleanup
delete data;
// ...
return bar;
Normally, if “DataStructure” was a simple data type, I could just allocate it on the stack. However, it’s constructor, internally, allocated it’s own memory using new, so this wouldn’t eliminate the problem. In this case, however, I could change the call signatures to allow the pointer to the data structure to be passed into ProcessElement and through the call graph, allowing the inner routine to reuse the same “data” memory instead of allocating. At the highest level, my code effectively changed to something like:
DataStructure* data = new DataStructure(numberToProcess);
for (i=0; i<numberToProcess; ++i)
{
// Do some work
ProcessElement(element[i], data);
}
delete data;
Granted, this dramatically reduced the maintainability of the code, so it wasn’t something I wanted to do unless there was a significant benefit. In this case, after profiling the new version, I found that it increased the overall performance dramatically – my main test case went from 35 minutes runtime down to 21 minutes. This was such a significant improvement, I felt it was worth the reduction in maintainability.
In C and C++, it’s generally a good idea (for performance) to:
Reduce the number of memory allocations as much as possible,
Use fewer, larger memory allocations instead of many smaller ones, and
Allocate as high up the call stack as possible, and reuse memory
I’ve seen many people try to make similar optimizations in C# code. For good or bad, this is typically not a good idea. The garbage collector in .NET completely changes the rules here.
In C#, reallocating memory in a loop is not always a bad idea. In this scenario, for example, I may have been much better off leaving the original code alone. The reason for this is the garbage collector. The GC in .NET is incredibly effective, and leaving the allocation deep inside the call stack has some huge advantages. First and foremost, it tends to make the code more maintainable – passing around object references tends to couple the methods together more than necessary, and overall increase the complexity of the code. This is something that should be avoided unless there is a significant reason. Second, (unlike C and C++) memory allocation of a single object in C# is normally cheap and fast. Finally, and most critically, there is a large advantage to having short lived objects. If you lift a variable out of the loop and reuse the memory, its much more likely that object will get promoted to Gen1 (or worse, Gen2). This can cause expensive compaction operations to be required, and also lead to (at least temporary) memory fragmentation as well as more costly collections later.
As such, I’ve found that it’s often (though not always) faster to leave memory allocations where you’d naturally place them – deep inside of the call graph, inside of the loops. This causes the objects to stay very short lived, which in turn increases the efficiency of the garbage collector, and can dramatically improve the overall performance of the routine as a whole.
In C#, I tend to:
Keep variable declarations in the tightest scope possible
Declare and allocate objects at usage
While this tends to cause some of the same goals (reducing unnecessary allocations, etc), the goal here is a bit different – it’s about keeping the objects rooted for as little time as possible in order to (attempt) to keep them completely in Gen0, or worst case, Gen1. It also has the huge advantage of keeping the code very maintainable – objects are used and “released” as soon as possible, which keeps the code very clean. It does, however, often have the side effect of causing more allocations to occur, but keeping the objects rooted for a much shorter time.
Now – nowhere here am I suggesting that these rules are hard, fast rules that are always true. That being said, my time spent optimizing over the years encourages me to naturally write code that follows the above guidelines, then profile and adjust as necessary. In my current project, however, I ran across one of those nasty little pitfalls that’s something to keep in mind – interop changes the rules.
In this case, I was dealing with an API that, internally, used some COM objects. In this case, these COM objects were leading to native allocations (most likely C++) occurring in a loop deep in my call graph. Even though I was writing nice, clean managed code, the normal managed code rules for performance no longer apply.
After profiling to find the bottleneck in my code, I realized that my inner loop, a innocuous looking block of C# code, was effectively causing a set of native memory allocations in every iteration. This required going back to a “native programming” mindset for optimization. Lifting these variables and reusing them took a 1:10 routine down to 0:20 – again, a very worthwhile improvement.
Overall, the lessons here are:
Always profile if you suspect a performance problem – don’t assume any rule is correct, or any code is efficient just because it looks like it should be
Remember to check memory allocations when profiling, not just CPU cycles
Interop scenarios often cause managed code to act very differently than “normal” managed code.
Native code can be hidden very cleverly inside of managed wrappers
