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Repeated access to properties and speed in C# [duplicate]

Please ignore code readability in this question.
In terms of performance, should the following code be written like this:
int maxResults = criteria.MaxResults;
if (maxResults > 0)
{
while (accounts.Count > maxResults)
accounts.RemoveAt(maxResults);
}
or like this:
if (criteria.MaxResults > 0)
{
while (accounts.Count > criteria.MaxResults)
accounts.RemoveAt(criteria.MaxResults);
}
?
Edit: criteria is a class, and MaxResults is a simple integer property (i.e., public int MaxResults { get { return _maxResults; } }.
Does the C# compiler treat MaxResults as a black box and evaluate it every time? Or is it smart enough to figure out that I've got 3 calls to the same property with no modification of that property between the calls? What if MaxResults was a field?
One of the laws of optimization is precalculation, so I instinctively wrote this code like the first listing, but I'm curious if this kind of thing is being done for me automatically (again, ignore code readability).
(Note: I'm not interested in hearing the 'micro-optimization' argument, which may be valid in the specific case I've posted. I'd just like some theory behind what's going on or not going on.)

First off, the only way to actually answer performance questions is to actually try it both ways and test the results in realistic conditions.
That said, the other answers which say that "the compiler" does not do this optimization because the property might have side effects are both right and wrong. The problem with the question (aside from the fundamental problem that it simply cannot be answered without actually trying it and measuring the result) is that "the compiler" is actually two compilers: the C# compiler, which compiles to MSIL, and the JIT compiler, which compiles IL to machine code.
The C# compiler never ever does this sort of optimization; as noted, doing so would require that the compiler peer into the code being called and verify that the result it computes does not change over the lifetime of the callee's code. The C# compiler does not do so.
The JIT compiler might. No reason why it couldn't. It has all the code sitting right there. It is completely free to inline the property getter, and if the jitter determines that the inlined property getter returns a value that can be cached in a register and re-used, then it is free to do so. (If you don't want it to do so because the value could be modified on another thread then you already have a race condition bug; fix the bug before you worry about performance.)
Whether the jitter actually does inline the property fetch and then enregister the value, I have no idea. I know practically nothing about the jitter. But it is allowed to do so if it sees fit. If you are curious about whether it does so or not, you can either (1) ask someone who is on the team that wrote the jitter, or (2) examine the jitted code in the debugger.
And finally, let me take this opportunity to note that computing results once, storing the result and re-using it is not always an optimization. This is a surprisingly complicated question. There are all kinds of things to optimize for:
execution time
executable code size -- this has a major effect on executable time because big code takes longer to load, increases the working set size, puts pressure on processor caches, RAM and the page file. Small slow code is often in the long run faster than big fast code in important metrics like startup time and cache locality.
register allocation -- this also has a major effect on execution time, particularly in architectures like x86 which have a small number of available registers. Enregistering a value for fast re-use can mean that there are fewer registers available for other operations that need optimization; perhaps optimizing those operations instead would be a net win.
and so on. It get real complicated real fast.
In short, you cannot possibly know whether writing the code to cache the result rather than recomputing it is actually (1) faster, or (2) better performing. Better performance does not always mean making execution of a particular routine faster. Better performance is about figuring out what resources are important to the user -- execution time, memory, working set, startup time, and so on -- and optimizing for those things. You cannot do that without (1) talking to your customers to find out what they care about, and (2) actually measuring to see if your changes are having a measurable effect in the desired direction.

If MaxResults is a property then no, it will not optimize it, because the getter may have complex logic, say:
private int _maxResults;
public int MaxReuslts {
get { return _maxResults++; }
set { _maxResults = value; }
}
See how the behavior would change if it in-lines your code?
If there's no logic...either method you wrote is fine, it's a very minute difference and all about how readable it is TO YOU (or your team)...you're the one looking at it.

Your two code samples are only guaranteed to have the same result in single-threaded environments, which .Net isn't, and if MaxResults is a field (not a property). The compiler can't assume, unless you use the synchronization features, that criteria.MaxResults won't change during the course of your loop. If it's a property, it can't assume that using the property doesn't have side effects.
Eric Lippert points out quite correctly that it depends a lot on what you mean by "the compiler". The C# -> IL compiler? Or the IL -> machine code (JIT) compiler? And he's right to point out that the JIT may well be able to optimize the property getter, since it has all of the information (whereas the C# -> IL compiler doesn't, necessarily). It won't change the situation with multiple threads, but it's a good point nonetheless.

It will be called and evaluated every time. The compiler has no way of determining if a method (or getter) is deterministic and pure (no side effects).
Note that actual evaluation of the property may be inlined by the JIT compiler, making it effectively as fast as a simple field.
It's good practise to make property evaluation an inexpensive operation. If you do some heavy calculation in the getter, consider caching the result manually, or changing it to a method.

why not test it?
just set up 2 console apps make it look 10 million times and compare the results ... remember to run them as properly released apps that have been installed properly or else you cannot gurantee that you are not just running the msil.
Really you are probably going to get about 5 answers saying 'you shouldn't worry about optimisation'. they clearly do not write routines that need to be as fast as possible before being readable (eg games).
If this piece of code is part of a loop that is executed billions of times then this optimisation could be worthwhile. For instance max results could be an overridden method and so you may need to discuss virtual method calls.
Really the ONLY way to answer any of these questions is to figure out is this is a piece of code that will benefit from optimisation. Then you need to know the kinds of things that are increasing the time to execute. Really us mere mortals cannot do this a priori and so have to simply try 2-3 different versions of the code and then test it.

If criteria is a class type, I doubt it would be optimized, because another thread could always change that value in the meantime. For structs I'm not sure, but my gut feeling is that it won't be optimized, but I think it wouldn't make much difference in performance in that case anyhow.

How do the .NET JITs optimise generated code layout?

Back in 2009 I posted this answer to a question about optimisations for nested try/catch/finally blocks.
Thinking about this again some years later, it seems the question could be extended to that other control flow, not only try/catch/finally, but also if/else.
At each of these junctions, execution will follow one path. Code must be generated for both, obviously, but the order in which they're placed in memory, and the number of jumps required to navigate through them will differ.
The order generated code is laid out in memory has implications for the miss rate on the CPU's instruction cache. Having the instruction pipeline stalled, waiting for memory reads, can really kill loop performance.
I don't think loops (for/foreach/while) are a such a good fit unless you expect the loop have zero iterations more often than it has some, as the natural generation order seems pretty optimal.
Some questions:
In what ways do the available .NET JITs optimise for generated instruction order?
How much difference can this make in practice to common code? What about perfectly suited cases?
Is there anything the developer can do to influence this layout? What about mangling with the forbidden goto?
Does the specific JIT being used make much difference to layout?
Does the method inlining heuristic come into play here too?
Basically anything interesting related to this aspect of the JIT!
Some initial thoughts:
Moving catch blocks out of line is an easy job, as they're supposed to be the exceptional case by definition. Not sure this happens.
For some loops I suspect you can increase performance non-trivially. However in general I don't think it'll make that much difference.
I don't know how the JIT decides the order of generated code. In C on Linux you have likely(cond) and unlikely(cond) which you can use to tell to the compiler which branch is the common path to optimise for. I'm not sure that all compilers respect these macros.
Instruction ordering is distinct from the problem of branch prediction, in which the CPU guesses (on its own, afaik) which branch will be taken in order to start the pipeline (oversimplied steps: decode, fetch operands, execute, write back) on instructions, before the execute step has determined the value of the condition variable.
I can't think of any way to influence this order in the C# language. Perhaps you can manipulate it a bit by gotoing to labels explicitly, but is this portable, and are there any other problems with it?
Perhaps this is what profile guided optimisation is for. Do we have that in the .NET ecosystem, now or in plan? Maybe I'll go and have a read about LLILC.

The optimization you are referring to is called the code layout optimization which is defined as follows:
Those pieces of code that are executed close in time in the same thread should be be close in the virtual address space so that they fit in a single or few consecutive cache lines. This reduces cache misses.
Those pieces of code that are executed close in time in different threads should be be close in the virtual address space so that they fit in a single or few consecutive cache lines as long as there is no self-modifying code. This gets lower priority than the previous one. This reduces cache misses.
Those pieces of code that are executed frequently (hot code) should be close in the virtual address space so that they fit in as few virtual pages as possible. This reduces page faults and working set size.
Those pieces of code that are rarely executed (cold code) should be close in the virtual address space so that they fit in as few virtual pages as possible. This reduces page faults and working set size.
Now to your questions.
In what ways do the available .NET JITs optimise for generated
instruction order?
"Instruction order" is really a very general term. Many optimizations affect instruction order. I'll assume that you're referring to code layout.
JITters by design should take the minimum amount of time to compile code while at the same time produce high-quality code. To achieve this, they only perform the most important optimizations so that it's really worth spending time doing them. Code layout optimization is not one of them because without profiling, it may not be beneficial. While a JITter can certainly perform profiling and dynamic optimization, there is a generally preferred way.
How much difference can this make in practice to common code? What
about perfectly suited cases?
Code layout optimization by itself can improve overall performance typically by -1% (negative one) to 4%, which is enough to make compiler writers happy. I would like to add that it reduces energy consumption indirectly by reducing cache misses. The reduction in miss ratio of the instruction cache can be typically up to 35%.
Is there anything the developer can do to influence this layout? What
about mangling with the forbidden goto?
Yes, there are numerous ways. I would like to mention the generally recommended one which is mpgo.exe. Please do not use goto for this purpose. It's forbidden.
Does the specific JIT being used make much difference to layout?
No.
Does the method inlining heuristic come into play here too?
Inlining can indeed improve code layout with respect to function calls. It's one of the most important optimizations and all .NET JITs perform it.
Moving catch blocks out of line is an easy job, as they're supposed to
be the exceptional case by definition. Not sure this happens.
Yes it might be "easy", but what is the potential gained benefit? catch blocks are typically small in size (containing a call to a function that handles the exception). Handling this particular case of code layout does not seem promising. If you really care, use mpgo.exe.
I don't know how the JIT decides the order of generated code. In C on
Linux you have likely(cond) and unlikely(cond) which you can use to
tell to the compiler which branch is the common path to optimise for.
Using PGO is much more preferable over using likely(cond) and unlikely(cond) for two reasons:
The programmer might inadvertently make mistakes while placing likely(cond) and unlikely(cond) in the code. It actually happens a lot. Making big mistakes while trying to manually optimize the code is very typical.
Adding likely(cond) and unlikely(cond) all over the code makes it less maintainable in the future. You'll have to make sure that these hints hold every time you change the source code. In large code bases, this could be ( or rather is) a nightmare.
Instruction ordering is distinct from the problem of branch
prediction...
Assuming you are talking about code layout, yes they are distinct. But code layout optimization is usually guided by a profile which really includes branch statistics. Hardware branch prediction is of course totally different.
Maybe I'll go and have a read about LLILC.
While using mpgo.exe is the mainstream way of performing this optimization, you can use LLILC also since LLVM support profile-guided optimization as well. But I don't think you need to go this far.

Database recommendations needed -> Columnar, Embedded (if possible)

EDIT: As result of the answers so far I like to add more focus in what I like to zero in on: A database that allows writing in-memory (could be simple C# code) with persistence to storage options in order to access the data from within R. Redis so far looks the most promising. I also consider to actually use something similar to Lockfree++ or ZeroMQ, in order to avoid writing data concurrently to the database, but rather sending all to be persisted data over a message bus/other implementation and to have one "actor" handle all write operations to an in-memory db or other solution. Any more ideas aside Redis (some mentioned SQLite and I will need to still test its performance). Any other suggestions?
I am searching for the ideal database structure/solution that meets most of my below requirements but so far I utterly failed. Can you please help?
My tasks: I run a process in .Net 4.5 (C#) and generate (generally) value types that I want to use for further analysis in other applications and therefore like to either preserve in-memory or persist on disk. More below. The data is generated within different tasks/threads and thus a row based data format does not lend itself well to match this situation (because the data generated in different threads is generated at different times and is thus not aligned). Thus I thought a columnar data structure may be suitable but please correct me if I am wrong.
Example:
Tasks/Thread #1 generates the following data at given time stamps
datetime.ticks / value of output data
1000000001 233.23
1000000002 233.34
1000000006 234.23
...
Taks/Thread #2 generates the following data at given time stamps
datetime.ticks / value of output data
1000000002 33.32
1000000005 34.34
1000000015 54.32
...
I do not need to align the time stamps at the .Net run-time, I am first and foremost after preserving the data and to process the data within R or Python at a later point.
My requirements:
Fast writes, fast writes, fast writes: It can happen that I generate 100,000- 1,000,000 data points per second and need to persist (worst case) or retain in memory the data. Its ok to run the writes on its own thread so this process can lag the data generation process but limitation is 16gb RAM (64bit code), more below.
Preference is for columnar db format as it lends itself well to how I want to query the data later but I am open to any other structure if it makes sense in regards to the examples above (document/key-value also ok if all other requirements are met, especially in terms of write speed).
API that can be referenced from within .Net. Example: HDF5 may be considered capable by some but I find their .Net port horrible.Something that supports .Net a little better would be a plus but if all other requirements are met then I can deal with something similar to the HDF5 .Net port.
Concurrent writes if possible: As described earlier I like to write data concurrently from different tasks/threads.
I am constrained by 16gb memory (run .Net process in 64bit) and thus I probably look for something that is not purely in-memory as I may sometimes generate more data than that. Something in-memory which persists at times or a pure persistence model is probably preferable.
Preference for embedded but if a server in a client/server solution can run as a windows service then no issue.
In terms of data access I have strong preference for a db solution for which interfaces from R and Python already exist because I like to use the Panda library within Python for time series alignments and other analysis and run analyses within R.
If the API/library supports in addition SQL/SQL-like/Linq/ like queries that would be terrific but generally I just need the absolute bare bones such as load columnar data in between start and end date (given the "key"/index is in such format) because I analyze and run queries within R/Python.
If it comes with a management console or data visualizer that would be a plus but not a must.
Should be open source or priced within "reach" (no, KDB does not qualify in that regards ;-)
OK, here is what I have so far, and again its all I got because most db solution simply fail already on the write performance requirement:
Infobright and Db4o. I like what I read so far but I admit I have not checked into any performance stats
Something done myself. I can easily store value types in binary format and index the data by datetime.ticks , I just would need to somehow write scripts to load/deserialize the data in Python/R. But it would be a massive tasks if I wanted to add concurrency, a query engine, and other goodies. Thus I look for something already out there.

I can't comment -- low rep (I'm new here) -- so you get a full answer instead...
First, are you sure you need a database at all? If fast write speed and portability to R is your biggest concern then have you just considered a flat file mechanism? According to your comments you're willing to batch writes out but you need persistence; if those were my requirements I'd write a straight-to-disck buffering system that was lightning fast then build a separate task that periodically took the disk files and moved them into a data store for R, and that's only if R reading the flat files wasn't sufficient in the first place.
If you can do alignment after-the-fact, then you could write the threads to separate files in your main parallel loop, cutting each file off every so often, and leave the alignment and database loading to the subprocess.
So (in crappy pseudo_code), build a thread process that you'd call with backgroundworker or some such and include a threadname string uniquely identifying each worker and thus each filestream (task/thread):
file_name = threadname + '0001.csv' // or something
open(file_name for writing)
while(generating_data) {
generate_data()
while (buffer_not_full and very_busy) {
write_data_to_buffer
generate_data()
}
flush_buffer_to_disk(file_name)
if(file is big enough or enough time has passed or we're not too busy) {
close(file_name)
move(file_name to bob's folder)
increment file_name
open(file_name for writing)
}
)
Efficient and speedy file I/O and buffering is a straightforward and common problem. Nothing is going to be faster than this. Then you can just write another process to do the database loads and not sweat the performance there:
while(file_name in list of files in bob's folder sorted by date for good measure)
{
read bob's file
load bob's file to database
align dates, make pretty
}
And I wouldn't write that part in C#, I'd batch script it and use the database's native loader which is going to be as fast as anything you can build from scratch.
You'll have to make sure the two loops don't interfere much if you're running on the same hardware. That is, run the task threads at a higher priority, or build in some mutex or performance limiters so that the database load doesn't hog resources while the threads are running. I'd definitely segregate the database server and hardware so that file I/O to the flat files isn't compromised.
FIFO queues would work if you're on Unix, but you're not. :-)
Also, hardware is going to have more of a performance impact for you than the database engine, I'd imagine. If you're on a budget I'm guessing you're on COTS hardware, so springing for a solid state drive may up performance fairly cheaply. As I said, separating the DB storage from the flat file storage would help, and the CPU/RAM for R, the Database, and your Threads should all be segregated ideally.
What I'm saying is that choice of DB vendor probably isn't your biggest issue, unless you have a lot of money to spend. You'll be hardware bound most of the time otherwise. Database tuning is an art, and while you can eek out minor performance gains at the top end, having a good database administrator will keep most databases in the same ballpark for performance. I'd look at what R and Python support well and that you're comfortable with. If you think in columnar fashion then look at R and C#'s support for Cassandra (my vote), Hana, Lucid, HBase, Infobright, Vertica and others and pick one based on price and support. For traditional databases on a single commodity machine, I haven't seen anything that MySQL can't handle.

This is not to answer my own question but to keep track of all data bases which I tested so far and why they have not met my requirements (yet): each time I attempted to write 1 million single objects (1 long, 2 floats) to the database. For ooDBs, I stuck the objects into a collection and wrote the collection itself, similar story for key/value such as Redis but also attempted to write simple ints (1mil) to columnar dbs such as InfoBright.
Db4o, awefully slow writes: 1mil objects within a collection took about 45 seconds. I later optimized the collection structure and also wrote each object individually, not much love here.
InfoBright: Same thing, very slow in terms of write speed, which surprised me quite a bit as it organizes data in columnar format but I think the "knowledge tree" only kicks in when querying data rather than when saving flat data structures/tables-like structures.
Redis (through BookSleeve): Great API for .Net: Full Redis functionality (though couple drawbacks to run the server on Windows machines vs. a Linux or Unix box). Performance was very fast...North of 1 million items per second. I serialized all objects using Protocol Buffers (protobuf-net, both written by Marc Gravell), still need to play a lot more with the library but R and Python both have full access to the Redis DB, which is a big plus. Love it so far. The Async framework that Marc wrote around the Redis base functions is awesome, really neat and it works so far. I wanna spend a little more time to experiment with the Redis Lists/Collection types as well, as I so far only serialized to byte arrays.
SqLite: I ran purely in-memory and managed to write 1 million value type elements in around 3 seconds. Not bad for a pure RDBMS, obviously the in-memory option really speeds things up. I only created one connection, one transaction, created one command, one parameter, and simply adjusted the value of the parameter within a loop and ran the ExecuteNonQuery on each iteration. The transaction commit was then run outside the loop.
HDF5: Though there is a .Net port and there also exists a library to somehow work with HDF5 files out of R, I strongly discourage anyone to do so. Its a pure nightmare. The .Net port is very badly written, heck, the whole HDF5 concept is more than questionable. Its a very old and in my opinion outgrown solution to store vectorized/columnar data. This is 2012 not 1995. If one cannot completely delete datasets and vectors out of the file in which they were stored before then I do not call that an annoyance but a major design flaw. The API in general (not just .Net) is very badly designed and written imho, there are tons of class objects that nobody, without having spent hours and hours of studying the file structure, understands how to use. I think that is somewhat evidenced by the very sparse amount of documentation and example code that is out there. Furthermore, the h5r R library is a drama, an absolute nightmare. Its badly written as well (often the file upon writing is not correctly close due to a faulty flush and it corrupts files), the library has issues to even be properly installed on 32 bit OSs...and it goes on and on. I write the most about HDF5 because I spent the most of my time on this piece of .... and ended up with the most frustration. The idea to have a fast columnar file storage system, accessible from R and .Net was enticing but it just does not deliver what it promised in terms of API integration and usability or lack thereof.
Update: I ditched testing velocityDB simply because there does not seem any adapter to access the db from within R available. I currently contemplate writing my own GUI with charting library which would access the generated data either from a written binary file or have it sent over a broker-less message bus (zeroMQ) or sent through LockFree++ to an "actor" (my gui). I could then call R from within C# and have results returned to my GUI. That would possibly allow me the most flexibility and freedom, but would obviously also be the most tedious to code. I am running into more and more limitations during my tests that with each db test I befriend this idea more and more.
RESULT: Thanks for the participation. In the end I awarded the bounty points to Chipmonkey because he suggested partly what I considered important points to the solution to my problem (though I chose my own, different solution in the end).
I ended up with a hybrid between Redis in memory storage and direct calls out of .Net to the R.dll. Redis allows access to its data stored in memory by different processes. This makes it a convenient solution to quickly store the data as key/value in Redis and to then access the same data out of R. Additionally I directly send data and invoke functions in R through its .dll and the excellent R.Net library. Passing a collection of 1 million value types to R takes about 2.3 seconds on my machine which is fast enough given that I get the convenience to just pass in the data, invoke computational functions within R out of the .Net environment and getting the results back sync or async.

Just a note: I once had a similar problem posted by a fellow in a delphi forum. I could help him with a simple ID-key-value database backend I wrote at that time (kind of a NoSQL engine). Basically, it uses a B-Tree to store triplets (32bit ObjectID, 32bit PropertyKey, 64bit Value). I could manage to save about 500k/sec Values in real time (about 5 years ago). Of course, the data was indexed on all three values (ID, property-ID and value). You could optimize this by ignoring the value index.
The source I still have is in Delphi, but I would think about implementing something like that using C#. I cannot tell you whether it will meet your needs for performance, but if all else fails, give it a try. Using a buffered write should also drastically improve performance.

I would go with way combining persistence storage (I personally prefer db4o, but you can use files as well as mentioned above) and storing objects into memory this way:
use BlockingCollection<T> to store objects in memory (I believe you will achieve better performance then 1000000/s to store objects in memory), and than have one or more processing threads which will consume the objects and store them into persistent database
// Producing thread
for (int i=0; i<1000000; i++)
blockingCollection.Add(myObject);
// Consuming threads
while (true)
{
var myObject = blockingCollection.Take();
db4oSession.Store(myObject); // or write it to the files or whathever
}
BlockingCollection pretty much solves Producer-Consumer workflow, and in case you will use multiple instance of them and use AddToAny/TakeFromAny you can reach any kind of multithreaded performance
each consuming thread could have different db4o session (file) to reach desired performance (db4o is singlethreaded).

Since you want to use ZeroMQ why not use memcache over Redis?
ZeroMQ offers no persistence as far as I know. Memcache also offers no persistence and is a bit faster than Redis.
Or perhaps the other way, if you use Redis why not use beanstalk MQ?
If you want to use Redis (for the persistence) you might want to switch from ZeroMQ to beanstalk MQ (also a fast in memory queue, but also has persistence via logging). Beanstalk also has C# libs.

RDTSC vs. StopWatch

I programmed my own string matching algorithm, and I want to measure its time accuratly,
to compare it with other algorithms to check if my implementation is better.
I tried (StopWatch), but it gives different time in each run, because of multiple processes running of the Windows OS. I heared about (RDTSC) that can get the number of
cycles consumed, but I do not know if it gives different cycles number in each excution too ?
Please help me; Can (RDTSC) give an accurate and same measurment of cycles for a C# function, or it is similar to (StopWatch) ? Which is the best way to get cycles number for a C# function alone without the other running processes ? and thanks alot for any help or hint

it gives different time in each run, because of multiple processes running of the Windows OS.
That is in the nature of all benchmarks.
Good benchmarks offset this by statistical means, i.e. measuring often enough to offset any side-effects from other running programs. This is the way to go. As far as precision goes, StopWatch is more than enough for benchmarks.
This requires several things (without getting into statistical details, which I’m not too good at either):
An individual should last long enough to offset measurement imprecisions introduced by the measuring method (even RDTSC isn’t completely precise), and to offset calling overhead. After all, you want to measure your algorithm, not the time it takes to run the testing loop and invoking your testing method.
Enough test runs to have confidence in the result: the more data, the higher the robustness of your statistic.
Minimize external influences, in particular systematic bias. That is to say, run all your tests on the same machine under same conditions, otherwise the results cannot be compared. At all.
Furthermore, if you run multiple runs of your tests (and you should!) interleave the different methods.

I think to have the most accurate info you should interop with GetThreadTimes():
http://msdn.microsoft.com/en-us/library/ms683237%28v=vs.85%29.aspx
In the link there is down the signature for use the function in C#.

Adding items to a List<T> / defensive programming

Explicitly checking/handling that you don't hit the 2^31 - 1 (?) maximum number of entries when adding to a C# List is crazyness, true of false?
(Assuming this is an app where the average List size is less than a 100.)

1. Memory limits
Well, size of System.Object without any properties is 8 bytes (2x32 bit pointers), or 16 bytes in 64-bit system. [EDIT:] Actually, I just checked in WinDbg, and the size is 12bytes on x86 (32-bit).
So in a 32-bit system, you would need 24Gb ram (which you cannot have on a 32-bit system).
2. Program design
I strongly believe that such a large list shouldn't be held in memory, but rather in some other storage medium. But in that case, you will always have the option to create a cached class wrapping a List, which would handle actual storage under the hood. So testing the size before adding is the wrong place to do the testing, your List implementation should do it itself if you find it necessary one day.
3. To be on the safe side
Why not add a re-entrance counter inside each method to prevent a Stack Overflow? :)
So, yes, it's crazy to test for that. :)

Seems excessive. Would you not hit the machine's memory limit first, depending on the size of the objects in your list ? (I assume this check is performed by the user of the List class, and is not any check in the implementation?)
Perhaps it's reassuring that colleagues are thinking ahead though ? (sarcasm!)

It would seem so, and I probably wouldn't include the check but I'm conflicted on this. Programmers once though that 2 digits were enough to represent the year in date fields on the grounds that it was fine for the expected life of their code, however we discovered that this assumption wasn't correct.
Look at the risk, look at the effort and make a judgement call (otherwise known as an educated guess! :-) ). I wouldn't say there's any hard or fast rule on this one.

As in the answer above there would more things going wrong I suspect than to worry about that. But yes if you have the time and inclination that you can polish code till it shines!

True
(well you asked true or false..)

Just tried this code:
List<int> list = new List<int>();
while (true) list.Add(1);
I got a System.OutOfMemoryException. So what would you do to check / handle this?

If you keep adding items to the list, you'll run out of memory long before you hit that limit. By "long" I really mean "a lot sooner than you think".
See this discussion on the large object heap (LOB). Once you hit around 21500 elements (half that on a 64-bit system) (assuming you're storing object references), your list will start to be a large object. Since the LOB isn't compacted in the same way the normal .NET heaps are, you'll eventually fragment it badly enough that a large enough continous memory area cannot be allocated.
So you don't have to check for that limit at all, it's not a real limit.

Yes, that is crazyness.
Consider what happens to the rest of the code when you start to reach those numbers. Is the application even usable if you would have millions of items in the list?
If it's even possible that the application would reach that amount of data, perhaps you should instead take measures to keep the list from getting that large. Perhaps you should not even keep all the data in memory at once. I can't really imagine a scenario where any code could practially make use of that much data.