puzzles | Raccolta personale di giochi ed enigmi matematici | Dependency Injection library

It appears you forgot to tell rustc it was allowed to use AVX2 instructions everywhere, so it couldn't inline those functions. Instead, you get a total disaster where only the wrapper functions are compiled as AVX2-using functions, or something like that.

Works fine for me with -O -C target-cpu=skylake-avx512 (https://godbolt.org/z/csY5or43T) so it can inline even the AVX512VL load you used, _mm256_load_epi32¹, and then optimize it into a memory source operand for vpaddd ymm0, ymm0, ymmword ptr [rdi + 4*rax] (AVX2) inside a tight loop.

In GCC / clang, you get an error like "inlining failed in call to always_inline foobar" in this case, instead of working but slow asm. (See this for details). This is something Rust should probably sort out before this is ready for prime time, either be like MSVC and actually inline the instruction into a function using the intrinsic, or refuse to compile like GCC/clang.

Footnote 1: See How to emulate _mm256_loadu_epi32 with gcc or clang? if you didn't mean to use AVX512.

With -O -C target-cpu=skylake (just AVX2), it inlines everything else, including vpaddd ymm, but still calls out to a function that copies 32 bytes from memory to memory with AVX vmovaps. It requires AVX512VL to inline the intrinsic, but later in the optimization process it realizes that with no masking, it's just a 256-bit load it should do without a bloated AVX-512 instruction. It's kinda dumb that Intel even provided a no-masking version of _mm256_mask[z]_loadu_epi32 that requires AVX-512. Or dumb that gcc/clang/rustc consider it an AVX512 intrinsic.

Since counting is enough, let's just do that, and then it takes a split second for large cases as well.

Basically your question already included all the building blocks. I only updated the report template to include the code to plot the radar chart. As a parameter I decided to pass the filtered dataset. In the server I only adjusted the specs for the params:

First, it's good to note the difference between equivalence and equisatisfiability. In general, converting an arbitrary boolean formula (say, something in DNF) to CNF can result in a exponential blow-up in size.

This blow-up is the issue with your from_dnf approach: whenever you handle another product term, each of the literals in that product demands a new copy of the current cnf clause set (to which it will add itself in every clause). If you have n product terms of size k, the growth is O(k^n).

In your case n is actually a function of k!. What's kept as a product term is filtered to those satisfying the view constraint, but overall the runtime of your program is roughly in the region of O(k^f(k!)). Even if f grows logarithmically, this is still O(k^(k lg k)) and not quite ideal!

Because you're asking "is this satisfiable?", you don't need an equivalent formula but merely an equisatisfiable one. This is some new formula that is satisfiable if and only if the original is, but which might not be satisfied by the same assignments.

For example, (a ∨ b) and (a ∨ c) ∧ (¬b) are each obviously satisfiable, so they are equisatisfiable. But setting b true satisfies the first and falsifies the second, so they are not equivalent. Furthermore the first doesn't even have c as a variable, again making it not equivalent to the second.

This relaxation is enough to replace this exponential blow-up with a linear-sized translation instead.

The critical idea is the use of extension variables. These are fresh variables (i.e., not already present in the formula) that allow us to abbreviate expressions, so we don't end up making multiple copies of them in the translation. Since the new variable is not present in the original, we'll no longer have an equivalent formula; but because the variable will be true if and only if the expression is, it will be equisatisfiable.

If we wanted to use x as an abbreviation of y, we'd state x ≡ y. This is the same as x → y and y → x, which is the same as (¬x ∨ y) ∧ (¬y ∨ x), which is already in CNF.

Consider the abbreviation for a product term: x ≡ (a ∧ b). This is x → (a ∧ b) and (a ∧ b) → x, which works out to be three clauses: (¬x ∨ a) ∧ (¬x ∨ b) ∧ (¬a ∨ ¬b ∨ x). In general, abbreviating a product term of k literals with x will produce k binary clauses expressing that x implies each of them, and one (k+1)-clause expressing that all together they imply x. This is linear in k.

To really see why this helps, try converting (a ∧ b ∧ c) ∨ (d ∧ e ∧ f) ∨ (g ∧ h ∧ i) to an equivalent CNF with and without an extension variable for the first product term. Of course, we won't just stop with one term: if we abbreviate each term then the result is precisely a single CNF clause: (x ∨ y ∨ z) where these each abbreviate a single product term. This is a lot smaller!

This approach can be used to turn any circuit into an equisatisfiable formula, linear in size and in CNF. This is called a Tseitin transformation. Your DNF formula is simply a circuit composed of a bunch of arbitrary fan-in AND gates, all feeding into a single arbitrary fan-in OR gate.

Best of all, although this formula is not equivalent due to additional variables, we can recover an assignment for the original formula by simply dropping the extension variables. It is sort of a 'best case' equisatisfiable formula, being a strict superset of the original.

To patch this into your code, I added:

You can do this:

Here's my test program in Compiler Explorer using clang.
And here is the same program in OnlineGDB using gcc.

The sizeof(_16Bytes) is 16 as expected, however offsetof(Test, test) is 12 because the compiler decided to pack it right after _16Bytes::_4bytes.

This actually makes perfect sense to me, based on standard packing rules, assuming that struct Test : _16Bytes turns into the equivalent of this:

You need to use SimpleDateFormat or DateTimeFormatter classes to convert Date from one representation to another.

Refer

You ask:

So why does Message1 not appear right after step 1 as coded?

Because you are blocking the main thread with your repeat-while loop. Your code is a perfect demonstration of why you should never block the main thread, as the UI cannot be updated until you free up the main thread. Any system events will be prevented, too. If you block the main thread for long enough, you even risk having your app unceremoniously killed by the watchdog process (designated with the termination code 0x8badf00d, pronounced “ate bad food”).

Your code says:

I looked into the Stream source code. The result of a map operation is just fed into the next operation. So there is almost no difference between one big map() call or several small map() calls.

And for the map() operation a parallel Stream makes no difference at all. Meaning each input object will be processed until the end by the same Thread in any case.

Also note: A parallel Stream only splits up the work if the operation chain allows it and there is enough data to process. So for a small Collection or a Collection that allows no random access, a parallel Stream behaves like a sequential Stream.