book | The Rust Programming Language | Learning library

You can use the std::nextafter function, which, despite its name, can retrieve the next representable value that is arithmetically before a given starting point, by using an appropriate to argument. (Often -Infinity, 0, or +Infinity).

This works portably by definition of nextafter, regardless of what floating-point format your C++ implementation uses. (Binary vs. decimal, or width of mantissa aka significand, or anything else.)

Example: Retrieving the closest value less than 1 for the double type (on Windows, using the clang-cl compiler in Visual Studio 2019), the answer is different from the result of the 1 - ε calculation (which as discussed in comments, is incorrect for IEEE754 numbers; below any power of 2, representable numbers are twice as close together as above it):

Simply use Room 2.4.0-alpha03

Code readability, short-circuiting and it is not guaranteed that Ord will always outperform a || operand. Computer systems are more complicated than expected, even though they are man-made.

There was a case where a for loop with a much more complicated condition ran faster on an IBM. The CPU didn't cool and thus instructions were executed faster, that was a possible reason. What I am trying to say, focus on other areas to improve code than fighting small-cases which will differ depending on the CPU and the boolean evaluation (compiler optimizations).

Since you're already using Docker, I'd suggest using a multi-stage build. Using a standard docker image like golang one can build an executable asset which is guaranteed to work with other docker linux images:

This is a good question... and I would have hoped there would be a practical guide somewhere. One could question if SO would be a good place to ask this question, but regardless, here's my attempt to summarize the various scale_color_*() and scale_fill_*() functions built into ggplot2. Here, we'll describe the range of functions using scale_color_*(); however, the same general rules will apply for scale_fill_*() functions.

There are 22 functions in all, but happily we can group them intelligently based on practical usage scenarios. There are three key criteria that can be used to define practically how to use each of the scale_color_*() functions:

1. Nature of the mapping data. Is the data mapped to the color aesthetic discrete or continuous? CONTINUOUS data is something that can be explained via real numbers: time, temperature, lengths - these are all continuous because even if your observations are 1 and 2, there can exist something that would have a theoretical value of 1.5. DISCRETE data is just the opposite: you cannot express this data via real numbers. Take, for example, if your observations were: "Model A" and "Model B". There is no obvious way to express something in-between those two. As such, you can only represent these as single colors or numbers.

2. The Colorspace. The color palette used to draw onto the plot. By default, ggplot2 uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.

3. The level of Specification. Generally, once you have defined if the scale function is continuous and in what colorspace, you have variation on the level of control or specification the user will need or can specify. A good example of this is the functions: *_continuous(), *_gradient(), *_gradient2(), and *_gradientn().

We can start off with continuous scales. These functions are all used when applied to observations that are continuous variables (see above). The functions here can further be defined if they are either binned or not binned. "Binning" is just a way of grouping ranges of a continuous variable to all be assigned to a particular color. You'll notice the effect of "binning" is to change the legend keys from a "colorbar" to a "steps" legend.

The continuous example (colorbar legend):

(This answer is a summary of comments posted above by Antti Haapala, klutt and Peter Cordes.)

GCC allocates more space than "necessary" in order to ensure that the stack is properly aligned for the call to proc: the stack pointer must be adjusted by a multiple of 16, plus 8 (i.e. by an odd multiple of 8). Why does the x86-64 / AMD64 System V ABI mandate a 16 byte stack alignment?

What's strange is that the code in the book doesn't do that; the code as shown would violate the ABI and, if proc actually relies on proper stack alignment (e.g. using aligned SSE2 instructions), it may crash.

So it appears that either the code in the book was incorrectly copied from compiler output, or else the authors of the book are using some unusual compiler flags which alter the ABI.

Modern GCC 11.2 emits nearly identical asm (Godbolt) using -Og -mpreferred-stack-boundary=3 -maccumulate-outgoing-args, the former of which changes the ABI to maintain only 2^3 byte stack alignment, down from the default 2^4. (Code compiled this way can't safely call anything compiled normally, even standard library functions.) -maccumulate-outgoing-args used to be the default in older GCC, but modern CPUs have a "stack engine" that makes push/pop single-uop so that option isn't the default anymore; push for stack args saves a bit of code size.

One difference from the book's asm is a movl $0, %eax before the call, because there's no prototype so the caller has to assume it might be variadic and pass AL = the number of FP args in XMM registers. (A prototype that matches the args passed would prevent that.) The other instructions are all the same, and in the same order as whatever older GCC version the book used, except for choice of registers after call proc returns: it ends up using movslq %edx, %rdx instead of cltq (sign-extend with RAX).

CS:APP 3e global edition is notorious for errors in practice problems introduced by the publisher (not the authors), but apparently this code is present in the North American edition, too. So this may be the author's mistake / choice to use actual compiler output with weird options. Unlike some of the bad global edition practice problems, this code could have come unmodified from some GCC version, but only with non-standard options.

Related: Why does GCC allocate more space than necessary on the stack, beyond what's needed for alignment? - GCC has a missed-optimization bug where it sometimes reserves an additional 16 bytes that it truly didn't need to. That's not what's happening here, though.

Reading a memory address does not clear the cache line.

Writing does.

So in a modern computer, there is RAM, and there are multiple layers of cache "around" the CPU (they are called L1, L2 and L3 cache, but the important part is that they are layers, and the CPU is at the middle). In a multi-core system, often the outer layers are shared; the innermost layer is usually not, and is specific to a given CPU.

Clearing the cache line means informing every other cache holding this memory "the data you own may be stale, throw it out".

Test and set writes true and atomically returns the old value. It clears the cache line, because it writes.

Test does not write. If you have another thread unsynchronized with this one, it reading the cache of this memory doesn't have to be poked.

The outer loop writes true, and exits if it replaced false. The inner loop waits until there is a false visible, then falls to outer loop. The inner loop need not clear every other cpu's cache status of the value of the atomic flag, but the outer has to (as it could change the false to true). As spinning could go on for a while, avoiding continuous cache clearing seems like a good idea.

Essentially, skipping over some stuff not relevant in this case, overload resolution is done to choose the user-defined conversion function to initialize the variable and (because there are no other differences between the conversion operators) the best viable one is chosen based on the rank of the standard conversion sequence required to convert the return value of to the variable's type.

The conversion int -> double is a floating-integral conversion, which has rank conversion.

The conversion float -> double is a floating-point promotion, which has rank promotion.

The rank promotion is better than the rank conversion, and so overload resolution will choose operator float as the best viable overload.

The conversion int -> long double is also a floating-integral conversion.

The conversion float -> long double is not a floating-point promotion (which only applies for conversion float -> double). It is instead a floating-point conversion which has rank conversion.

Both sequences now have the same standard conversion sequence rank and also none of the tie-breakers (which I won't go through) applies, so overload resolution is ambigious.

The conversion int -> bool is a boolean conversion which has rank conversion.

The conversion float -> bool is also a boolean conversion.

Therefore the same situation as above arises.

See https://en.cppreference.com/w/cpp/language/overload_resolution#Ranking_of_implicit_conversion_sequences and https://en.cppreference.com/w/cpp/language/implicit_conversion for a full list of the conversion categories and ranks.

Although it might seem that a conversion between floating-point types should be considered "better" than a conversion from integral to floating-point type, this is generally not the case.

Zero Cost Abstractions means adding higher-level programming concepts, like generics, collections and so on do not come with a run-time cost, only compiler time cost (the code will be slower to compile). Any operation on zero-cost abstractions is as fast as you would write out matching functionality by hand using lower-level programming concepts (for loops, counters, ifs and so on).