What’s the difference? sizeof and Marshal.SizeOf

I often see StackOverflow answers that confuse the sizeof operator with the Marshal.SizeOf method. These two operators do different things and can return different results, so it is important to know which is which.

In a nutshell, the difference is: the sizeof operator takes a type name and tells you how many bytes of managed memory need to be allocated for an instance of that struct.[1. I don’t have to tell long-time readers of this blog that of course this is not necessarily stack memory; structs are allocated off the heap when they are array elements, fields of a class, and so on.] By contrast, Marshal.SizeOf takes either a type object or an instance of the type, and tells you how many bytes of unmanaged memory need to be allocated. These can be different for a variety of reasons. The name of the type gives you a clue: Marshal.SizeOf is intended to be used when marshaling a structure to unmanaged memory.

Another difference between the two is that the sizeof operator can only take the name of an unmanaged type; that is, a struct type whose fields are only integral types, Booleans, pointers and so on. (See the specification for an exact definition.) Marshal.SizeOf by contrast can take any class or struct type.

Nullable micro-optimizations, part one

Which is faster, Nullable<T>.Value or Nullable<T>.GetValueOrDefault()?

Before I answer that question, my standard response to “which horse is faster?” questions applies. Read that first.


Welcome back. But again, before I answer the question I need to point out that the potential performance difference between these two mechanisms for obtaining the non-nullable value of a nullable value type is a consequence of the fact that these two mechanisms are not semantically equivalent. The former may legally only be called if you are sure that the nullable value is non-null; put another way, calling Value without knowing that HasValue is true is a boneheaded exception. The latter may be called on any nullable value. A glance at a simplified version of the source code illustrates the difference.

struct Nullable<T> where T : struct
  private bool hasValue;
  private T value;
  public Nullable(T value)
    this.hasValue = true;
    this.value = value;
  public bool HasValue { get { return this.hasValue; } }
  public T Value
      if (!this.HasValue) throw something;
      return this.value;
  public T GetValueOrDefault() 
    return this.value; 
  ... and then all the other conversion gear and so on ...

The first thing to notice is that a nullable value type’s ability to represent a “null” integer or decimal or whatever is not magical. (Nullable value types are magical in other ways; for example, there’s no way to write your own struct that has the strange boxing behaviour of a nullable value type; an int? boxes to either an int or null, never to a boxed int?. But let’s not worry about these magical features today.) A nullable value type is nothing more than an instance of the value type plus a bool saying whether it’s null or not.

If a variable of nullable value type is initialized with the default constructor then the hasValue field will be its default value, false, and the value field will be default(T). If it is initialized with the declared constructor then of course the hasValue field is true and the value field is any legal value, including possibly T‘s default value. Thus, the implementation of GetValueOrDefault() need not check the flag; if the flag is true then the value field is set correctly, and if it is false, then it is set to the default value of T.

Looking at the code it should be clear that Value is almost certainly not faster than GetValueOrDefault() because obviously the former does exactly the same work as the latter in the success case, plus the additional work of the flag check. Moreover, because GetValueOrDefault() is so brain-dead simple, the jitter is highly likely to perform an inlining optimization.

An inlining optimization is where the jitter eliminates an unnecessary “call” and “return” instruction by simply generating the code of the method body “inline” in the caller. This is a great optimization because doing so can make code both smaller and faster in some cases, though it does make it harder to debug because the debugger has no good way to generate breakpoints inside the inlined method.

How the jitter chooses to inline or not is an implementation detail, but it is reasonable to assume that it is less likely to perform an inlining optimization on code that contains more than one “basic block” and has a throw in it.

A “basic block” is a region of code where you know that the code will execute from the top of the block to the bottom without any “normal” branches in or out of the middle of the block. (A basic block may of course have exceptions thrown out of it.) Many optimizing compilers use “basic blocks” as an abstraction because it abstracts away the unnecessary details of what the block actually does, and treats it solely as a node in a flow control graph.

It should also be clear that though the relative performance difference might be large, the absolute difference is small. A call, field fetch, conditional jump and return in the typical case makes up the difference, and those things are each only nanoseconds.

Now, this is of course not to say that you should willy-nilly change all your calls to Value to GetValueOrDefault() for performance reasons. Read my rant again if you have the urge to do that! Don’t go changing working, debugged, tested code in order to obtain a performance benefit that is (1) highly unlikely to be a real bottleneck, and (2) highly unlikely to be your worst performance problem.

And besides, using Value has the nice property that if you have made a mistake and fetched the value of a null, you’ll get an exception that informs you of where your bug is! Code that draws attention to its faults is a good thing.

Finally, I note that here we have one of those rare cases where the frameworks design guidelines have been deliberately bent. We have a “Get” method is actually faster than a property getter, and the property getter throws! Normally you expect the opposite: the “Get” method is usually the one that is slow and can throw, and the property is the one that is fast and never throws. Though this is somewhat unfortunate, remember, the design guidelines are our servants, not our masters, and they are guidelines, not rules.

Next time on FAIC: How does the C# compiler use its knowledge of the facts discussed today to your advantage? Have a great Christmas everyone; we’ll pick up this subject again in a week.

Atomicity, volatility and immutability are different, part three

So what does the keyword “volatile” mean, anyway? Misinformation abounds on this subject.

First off, so as to not bury the lead: in C# the rules have been carefully designed so that every volatile field read and write is also atomic. (Of course the converse does not follow; it is perfectly legal for an operation to be atomic without it being volatile, whatever that means.)

The way this is achieved is simple; the rules of C# only permit you to annotate fields with volatile if the field has a type that is guaranteed to have atomic reads and writes.

There is no logical requirement for this property; logically, volatility and atomicity are orthogonal. One could have a volatile-but-not-atomic read, for example. The very idea of doing so gives me the heebie-jeebies! Getting an up-to-date value that has been possibly splinched through the middle seems like a horrid prospect. I am very glad that C# has ensured that any volatile read or write is also an atomic read or write.

Volatility and immutability are essentially opposites; as we’ll see the whole point of volatility is to impose some kind of safety upon certain dangerous kinds of mutability.

But what does volatile mean anyway? To understand this we must first go back in time to the early days of the C language. Suppose you are writing a device driver in C for a temperature-recording device at a weather station:

int* currentBuf = bufferStart;
while(currentBuf < bufferEnd)
  int temperature = deviceRegister[0];
  *currentBuf = temperature;

It is entirely possible that the optimizer reasons as follows: we know that bufferStart, bufferEnd and deviceRegister are initialized at the beginning of the program and never changed afterwards; they can be treated as constants. We know that the memory addresses mapped to deviceRegister do not overlap the buffer; there is no aliasing going on here. We see that there are never any writes whatsoever in this program to deviceRegister[0]. Therefore the optimizer can pretend that you wrote:

int* currentBuf = bufferStart;
int temperature = deviceRegister[0];
while(currentBuf < bufferEnd)
  *currentBuf = temperature;

which obviously is completely different in our scenario. The optimizer makes the seemingly-reasonable assumption that if it can prove that a variable is never written to again then it need only read from it once. But if the variable in question is marked as volatile — meaning it changes on its own, outside of the control of the program — then the compiler cannot safely make this optimization.

That’s what volatile is for in C. Any additional meanings of volatile are not required by the C standard, and are compiler-specific extensions.[1. And there are a few other standardized valid usages as well; it also prevents optimizations that would screw up non-local gotos and some other relatively obscure scenarios.]

So now let’s make an analogy.

Imagine that there is a three-ring binder with a thousand pages in it, called The Big Book of Memory.  Each page has a thousand numbers on it, written in pencil so that they can be changed. You also have a “register page” which only has a dozen numbers on it, each with special meaning. When you need to do some operation on a number, first you flip to the right page, then you look up the right number on that page, then you copy it into the register page. You do your calculations only on the register page. When you are done a calculation you might write a number back somewhere into the book, or you might do another read from the book to get another value to operate on.

Suppose you are doing some operation in a loop — as with our temperature example above. You might decide as an optimization that you are pretty sure that a particular number location is never going to change. So instead of reading it out of the book every time you need it, you copy it onto your register page, and never read it again. That’s the optimization we proposed above. If one of those numbers is changing constantly based on factors outside your control then making this optimization is not valid. You need to go back to the book every time.

You’ll note that nothing in our C-style volatile story so far said anything at all about multithreading. C-style volatile is about telling the compiler to turn off optimizations because the compiler cannot make reasonable assumptions about whether a given variable is changing or not. It is not about making things threadsafe. Let’s see why![1. Of course we already know one reason: volatile operations are not guaranteed to be atomic, and thread safety requires atomicity. But there is a deeper reason why C-style volatility does not create thread safety.]

Suppose you have one thread that is writing a variable and another thread that is reading the same variable. You might think that this is exactly the same as our “C-style volatile” scenario. Imagine, for example, that our deviceRegister[0] expression above was not reading from some hardware register changing based on outside factors, but rather was simply reading from a memory address that was potentially changing based on the operation of another thread that is feeding it temperature data from some other source. Does “C-style volatile” solve our threading problem?

Kinda, sorta… well, no. The assumption we’ve just made is that a memory address being changed by the temperature outside is logically the same as a memory address being changed by another threadThat assumption is not warranted in general. Let’s continue with our analogy to see why that is by first considering a model in which it is warranted.

Suppose instead of a number being updated by magic because it is somehow reflecting the temperature, suppose instead we have two people taking turns using the book. One is the Reader and one is the Writer. They only have one book and one register page between them, so they have to cooperate.

The Reader again might be reading the same book number slot over and over again. The Reader might decide that they can read the value just once, copy it to the register page, and then keep on using the copy. The Reader does this for a while. When it is time to give the Writer a turn, the Reader writes all of the current register page values to a special page in the book that is reserved for the Reader’s use. The Reader then hands the book and the register page to the Writer.

The Writer fills in the register page from their personal special location in the book, and keeps on doing what they were doing, which is writing new values into the book. When the Writer wants to take a break, again, they write the contents of the register page into the book and hand the register page back to the Reader.

The problem should be obvious. If the Writer is writing to the location that the Reader previously cached to their register page then the Reader is making decisions based on an out-of-date value.

If this analogy is a good description of the memory model of the processor then marking the shared location as “C-style volatile” does the right thing. The Reader knows that they should not be caching the value; to get the up-to-date value they have to go back to the book every time because they don’t know whether the Writer changed the value when the Writer last had control.[1. This is particularly true in non-cooperative multithreading; perhaps the Reader and the Writer do not choose for themselves the schedule for taking turns!]

Unfortunately, that is not the actual memory model of many modern multi-processor machines. The actual memory model goes more like this:

Suppose there are two people sharing one book — again, the Reader and the Writer. Each has their own register page,plus a blank book page. The Reader goes to read a value from the book. But the book isn’t there! Instead, there is the Librarian. The Reader asks the Librarian for the book and the Librarian says “you can’t have the book itself; it is far too valuable to let you use it. But give me your blank page, and I’ll copy the stuff from the book onto it“. The Reader figures that is better than nothing. In fact, it’s really good, now that we consider it! The Librarian hands the Reader back a copy of the entire page, not just the single number the Reader wanted. Now the Reader can now go to town and really efficiently do calculations involving any number in that page without talking to the Librarian again. Only when the Reader wants something outside of the bounds of the copied page do they have to go back to the Librarian. The Reader’s performance just went way up.

Similarly, the Writer goes to write a value in the book at the same time. (Remember, the Reader and Writer no longer have to take turns using the register page because they both have their own register page.) But the Librarian does not allow this. The Librarian says “here, let me make you a copy of the page you want to write to. You make your changes to the copy, and when you are done, let me know and I will update the entire page.” The Writer thinks this is great! The Writer can write all kinds of crazy things and never talk to the Librarian again until the Writer needs to write to (or read from) a different page. When that happens the Writer hands the modified copy page to the Librarian, the Librarian copies the Writer’s page back into the book, and gives the Writer a copy of the new page that the Writer wants.

Clearly this is awesome if the Reader and the Writer are not both reading and writing the same page of the book. But what if they are? C-style volatile does not help at all in this situation! Suppose the Reader decides, oh, this memory location is marked as volatile, so I will not cache the read of the value onto my register page. Does that help? Not a bit! Even if the reader always goes back to the page, they are going back to their copy of the page, the copy made for them by the Librarian. Suppose the Reader then says, “OK, this thing is volatile, so when I read it, heck, I’ll just go back to the Librarian again and have the Librarian make me a new copy of this page”. Does that help? No, because the Writer might not have submitted the changes to the Librarian yet! The Writer has been making changes to their local copy.

In order to solve this problem the Reader could have a way to tell the Librarian “Hey, Librarian! I need to read the most up-to-date version of this location from the Book”. The Librarian then has to go find the Writer and ask the Writer to stop what they are doing and submit the changes right now. Both the Reader and the Writer come to a screeching halt and the Librarian then does the laborious work of ensuring that the Book is consistent.[1. And of course we haven’t even considered situations where there are multiple readers and multiple writers all partying on the same page.] Alternatively, the Writer could tell the Librarian “hey, I’m about to update this value; go find anyone who is about to read it and tell them that they need to fetch a new copy of this page when I’m done”. The exact strategy chosen doesn’t really matter for the purposes of this analogy; the point is that everyone has to somehow cooperate to make sure that a consistent view of all the edits is achieved.

This strategy gives a massive performance increases in the common scenario where multiple readers and multiple writers are each working on data that is highly contiguous — that is, each reader and each writer does almost all of their work on the one page they have copied locally, so that they don’t have to go back to the Librarian. It gives massive performance penalties in scenarios where readers and writers are working on the same page and cannot tolerate inconsistencies or out-of-date values; the readers and writers are constantly going back to the Librarian, stopping everybody from doing work, and spending all their time copying stuff back into and out of the Book of Memory to ensure that the local caches are consistent.

Clearly we have a problem here. If C-style volatile doesn’t solve this problem, what does solve this problem? C#-style volatile, that’s what.

Sorta. Kinda. In a pretty bogus way, actually.

In C#, volatile means not only “make sure that the compiler and the jitter do not perform any code reordering or register caching optimizations on this variable“. It also means “tell the processors to do whatever it is they need to do to ensure that I am reading the latest value, even if that means halting other processors and making them synchronize main memory with their caches“.

Actually, that last bit is a lie. The true semantics of volatile reads and writes are considerably more complex than I’ve outlined here; in fact they do not actually guarantee that every processor stops what it is doing and updates caches to/from main memory. Rather, they provide weaker guarantees about how memory accesses before and after reads and writes may be observed to be ordered with respect to each other. Certain operations such as creating a new thread, entering a lock, or using one of the Interlocked family of methods introduce stronger guarantees about observation of ordering. If you want more details, read sections 3.10 and 10.5.3 of the C# 4.0 specification.

Frankly, I discourage you from ever making a volatile field. Volatile fields are a sign that you are doing something downright crazy: you’re attempting to read and write the same value on two different threads without putting a lock in place. Locks guarantee that memory read or modified inside the lock is observed to be consistent, locks guarantee that only one thread accesses a given hunk of memory at a time, and so on. The number of situations in which a lock is too slow is very small, and the probability that you are going to get the code wrong because you don’t understand the exact memory model is very large. I don’t attempt to write any low-lock code except for the most trivial usages of Interlocked operations. I leave the usage of volatile to real experts.

For more information on this incredibly complex topic, see:

Why C-style volatile is almost useless for multi-threaded programming

Joe Duffy on why attempting to ‘fix’ volatile in C# is a waste of time

Vance Morrison on incoherent caches and other aspects of modern memory models

Atomicity, volatility and immutability are different, part one

I get a fair number of questions about atomicity, volatility, thread safety, immutability and the like; the questions illustrate a lot of confusion on these topics. Let’s take a step back and examine each of these ideas to see what the differences are between them.

First off, what do we mean by “atomic”? From the Greek ἄτομος, meaning “not divisible into smaller parts”, an “atomic” operation is one which is always observed to be done or not done, but never halfway done. The C# specification clearly defines what operations are atomic in section 5.5. The atomic operations are: reads and writes of variables of any reference type, or, effectively, any built-in value type that takes up four bytes or less, like int, short and so on. Reads and writes of variables of value types that take more than four bytes, like double, long and decimal, are not guaranteed to be atomic by the C# language. [1. There is no guarantee that they are not atomic! They might in practice be atomic on some hardware. Or they might not.]

What does it mean for a read and write of an int to be atomic?  Suppose you have static variables of type int. X is 2, Y is 1, Z is 0. Then on one thread we say:

Z = X;

and on another thread:

X = Y

Each thread does one read and one write. Each read and write is itself atomic. What is the value of Z? Without any synchronization, the threads will race. If the first thread wins then Z will be 2. If the second thread wins then Z will be 1. But Z will definitely be one of those two values, you just can’t say which.

Now consider an immutable struct:

struct MyLong
  public readonly int low;
  public readonly int high;
  public MyLong(low, high)
    this.low = low;
    this.high = high;

Ignore for the moment the evil that is public fields. Suppose we have a fields Q, R and S of type MyLong initialized to (0x01234567, 0x0BADF00D), (0x0DEDBEEF, 0x0B0B0B0B) and (0, 0),  respectively. On two threads we say:

S = Q;


Q = R;

We have two threads. Each thread does one read and one write, but the reads and writes are not atomic. They can be divided! This program is actually the same as if the two threads were:

S.low = Q.low;
S.high = Q.high;


Q.low = R.low;
Q.high = R.high;

Now, you can’t do this because that’s writing to a readonly field outside a constructor. But the CLR is the one enforcing that rule; it can break it! (We’ll come back to this in the next episode; things are even weirder than you might think.) Value types are copied by value; that’s why they’re called value types. When copying a value type, the CLR doesn’t call a constructor, it just moves the bytes over one atomic chunk at a time. In practice, maybe the jitter has special registers available that allow it to move bigger chunks around, but that’s not a guarantee of the C# language. The C# language only goes so far as to guarantee that the chunks are not smaller than four bytes.

Now the threads can race such that perhaps first S.low = Q.low runs, then Q.low = R.low runs, then Q.high = R.high runs, and then S.high = Q.high runs, and hey, S is now (0x0DEDBEEF, 0x0BADF00D), even though that was neither of the original values. The values have been splinched, as Hermione Granger would say.[1. were she a computer programmer.]

And of course, the ordering above is not guaranteed either. The CLR is permitted to copy the chunks over in any order it chooses; it could be copying high before low, for example.

The name “MyLong” was of course no accident; in effect, a two-int readonly struct is how longs are implemented on 32 bit chips. Each operation on the long is done in two parts, on each 32 bit chunk. The same goes for doubles, the same goes for anything larger than 32 bits. If you try reading and writing longs or doubles in multiple threads on 32 bit operating systems without adding some sort of locking around it to make the operation atomic, your data are highly likely to get splinched.

The only operations that are guaranteed by the C# language to be atomic without some sort of help from a lock or other synchronization magic are those listed above: individual reads and writes of variables of the right size. In particular, operations like “increment” and “decrement” are not atomic. When you say


that’s a syntactic sugar for “read i, increment the read value, write the incremented value back to i“. The read and write operations are guaranteed to be atomic, but the entire operation is not; it consists of multiple atomic operations and therefore is not itself atomic. Two attempts to increment i on two different threads could interleave such that one of the increments is “lost”.

There are many techniques for making non-atomic operations into atomic operations; the easiest is simply to wrap every access to the variable in question with a lock, so that it is never the case that two threads are messing with the variable at the same time. You can also use the Interlocked family of helper methods which provide atomic increment, atomic compare-and-exchange, and so on.

Have a lovely Memorial Day weekend, American readers. I’m spending my Memorial Day weekend marrying a close personal friend.[1. Actually, I am marrying two close personal friends. To each other, even!] Should be fun!

Next time on FAIC: readonly inside a struct is the moral equivalent of cheque kiting, plus ways you can make the atomicity guarantees stronger or weaker.

What’s the difference between conditional compilation and the Conditional attribute?

User: Why does this program not compile correctly in the release build?

class Program 
#if DEBUG 
    static int testCounter = 0; 
    static void Main(string[] args) 
    static void SomeTestMethod(int t) { } 

Eric: This fails to compile in the release build because testCounter cannot be found in the call to SomeTestMethod.

User: But that call site is going to be omitted anyway, so why does it matter? Clearly there’s some difference here between removing code with the conditional compilation directive versus using the conditional attribute, but what’s the difference?

Eric: You already know the answer to your question, you just don’t know it yet. Let’s get Socratic; let me turn this around and ask you how this works. How does the compiler know to remove the method call site?

User: Because the method called has the Conditional attribute on it.

Eric: You know that. But how does the compiler know that the method called has the Conditional attribute on it?

User: Because overload resolution chose that method. If this were a method from an assembly, the metadata associated with that method has the attribute. If it is a method in source code, the compiler knows that the attribute is there because the compiler can analyze the source code and figure out the meaning of the attribute.

Eric: I see. So fundamentally, overload resolution does the heavy lifting. How does overload resolution know to choose that method? Suppose hypothetically there were another method of the same name with different parameters.

User: Overload resolution works by examining the arguments to the call and comparing them to the parameter types of each candidate method and then choosing the unique best match of all the candidates.

Eric: And there you go. Therefore the arguments must be well-defined at the point of the call, even if the call is going to be removed. In fact, the call cannot be removed unless the arguments are extant! But in the release build, the type of the argument cannot be determined because its declaration has been removed.

So now you see that the real difference between these two techniques for removing unwanted code is what the compiler is doing when the removal happens. At a high level, the compiler processes a text file like this. First it “lexes” the file. That is, it breaks the string down into “tokens” — sequences of letters, numbers and symbols that are meaningful to the compiler. Then those tokens are “parsed” to make sure that the program conforms to the grammar of C#. Then the parsed state is analyzed to determine semantic information about it; what all the types are of all the expressions and so on. And finally, the compiler spits out code that implements those semantics.

The effect of a conditional compilation directive happens at lex time; anything that is inside a removed #if block is treated by the lexer as a comment. It’s like you simply deleted the whole contents of the block and replaced it with whitespace. But removal of call sites depending on conditional attributes happens at semantic analysis time; everything necessary to perform that semantic analysis must be present. 

User: Fascinating. Which parts of the C# specification define this behavior?

Eric: The specification begins with a handy “table of contents”, which is very useful for answering such questions. The table of contents states that section 2.5.1 describes “Conditional compilation symbols” and section 17.4.2 describes “The Conditional attribute”.

User: Awesome!

What’s the difference? fixed versus fixed

I got an email the other day that began:

I have a question about fixed sized buffers in C#:

unsafe struct FixedBuffer 
  public fixed int buffer[100];

Now by declaring buffer as fixed it is not movable…

And my heart sank. This is one of those deeply unfortunate times when subtle choices made in the details of language design encourage misunderstandings.

When doing pointer arithmetic in unsafe code on a managed object, you need to make sure that the garbage collector does not move the memory you’re looking at. If a collection on another thread happens while you’re doing pointer arithmetic on an object, the pointer can get all messed up. Therefore, C# classifies all variables as “fixed” or “movable”. If you want to do pointer arithmetic on a movable object, you can use the fixed keyword to say “this local variable contains data which the garbage collector should not move.” When a collection happens, the garbage collector needs to look at all the local variables for in-flight calls (because of course, stuff that is in local variables needs to stay alive); if it sees a “fixed” local variable then it makes a note to itself to not move that memory, even if that fragments the managed heap. (This is why it is important to keep stuff fixed for as little time as possible.) So typically, we use “fixed” to mean “fixed in place”.

But that’s not what “fixed” means in this context; this means “the buffer in question is fixed in size to be one hundred ints” — basically, it’s the same as generating one hundred int fields in this structure.

Obviously we often use the same keyword to mean conceptually the same thing. For example, we use the keyword internal in many ways in C#, but all of them are conceptually the same. It is only ever used to mean “accessibility to some entity is restricted to only code in this assembly”.

Sometimes we use the same keyword to mean two completely different things, and rely upon context for the user to figure out which meaning is intended. For example:

var results = from c in customers where c.City == "London" select c;


class C<T> where T : IComparable<T>

It should be clear that where is being used in two completely different ways: to build a filter clause in a query, and to declare a type constraint on a generic type parameter.

We cause people to run into trouble when one keyword is used in two different ways but the difference is subtle, like our example above. The user’s email went on to ask a whole bunch of questions which were predicated on the incorrect assumption that a fixed-in-size buffer is automatically fixed in place in memory.

Now, one could say that this is just an unfortunate confluence of terms; that “fixed in size” and “fixed in place” just happen to both use the word “fixed” in two different ways, how vexing. But the connection is deeper than that: you cannot safely access the data stored in a fixed-in-size buffer unless the container of the buffer has been fixed in place. The two concepts are actually quite strongly related in this case, but not at allthe same.

On the one hand it might have been less confusing to use two keywords, say pinned and fixed. But on the other hand, both usages of fixed are only valid in unsafe code. A key assumption of all unsafe code features is that if you are willing to use unsafe code in C#, then you are already an expert programmer who fully understands memory management in the CLR. That’s why we make you write unsafe on the code; it indicates that you’re turning off the safety system and you know what you’re doing.

A considerable fraction of the keywords of C# are used in two or more ways: fixed, into, partial, out, in, new, delegate, where, using, class, struct, true, false, base, this, event, return and void all have at least two different meanings. Most of those are clear from the context, but at least the first three — fixed, into and partial — have caused enough confusion that I’ve gotten questions about the differences from perplexed users. I’ll take a look at into and partial next.