A Design Study for a Macro-Free Testing Library
https://jonastoth.github.io/posts/rtest_library/

https://redd.it/1v8qw4e
@r_cpp

ODB C++ ORM version 2.6.0 released
https://codesynthesis.com/pipermail/odb-announcements/2026/000047.html

https://redd.it/1v8ycqt
@r_cpp

Constraints of mdspan policy layout_stride

Wtith great support of Mark Hoemmen and Christian Robert Trott (two authors of std::mdspan), I just finished the mdspan chapter of "C++23 - The Complete Guide" (https://www.cppstd23.com/). I learned something I was not aware of and what is not obvious:

If you specify a layout\_stride mapping, there are several constraints you have to take into account. Otherwise, the resulting code has undefined behavior.

The constaintes are:

* Each layout mapping must be ***unique***. This means that each underlying element may only be reached by one combination of indices. In other words: subsets of this layout may not overlap and iterating over all elements may not visit any element twice.
* Only ***positive offsets*** from one stride to the other are allowed. This, for example, means that you cannot use this layout for direct reverse iterations.
* There must be one way to perform a multi-dimensional iteration so that the ***resulting offsets*** to the underlying memory are **ascending.**

For example:

* [https://www.cppstd23.com/code/mdspan/mdspanstride2.cpp.html](https://www.cppstd23.com/code/mdspan/mdspanstride2.cpp.html) is valid.
* [https://www.cppstd23.com/code/mdspan/mdspanstrideUB4.cpp.html](https://www.cppstd23.com/code/mdspan/mdspanstrideUB4.cpp.html) has undefined behavior.

Hope this helps.

https://redd.it/1v8zynw
@r_cpp

Latest News From Upcoming C++ Conferences (2026-07-28)

**TICKETS AVAILABLE TO PURCHASE**

The following conferences currently have tickets available to purchase

* **CppCon (12th – 18th September)** – You can buy standard tickets until August 29th at [https://cppcon.org/registration/](https://cppcon.org/registration/)
* **C++ Under The Sea** **(14th – 16th October)** – You can buy early bird tickets at [https://sales.ticketing.cm.com/cppunderthesea2026/](https://sales.ticketing.cm.com/cppunderthesea2026/)
* **ADC – (9th – 11th November)** – Tickets for ADC can now be purchased at [https://ti.to/audio-developer-conference/adc-bristol-2026](https://ti.to/audio-developer-conference/adc-bristol-2026)
* **Meeting C++ (26th – 28th November)** – You can buy early bird tickets at [https://meetingcpp.com/2026/](https://meetingcpp.com/2026/)

**OPEN CALL FOR SPEAKERS**

**OTHER OPEN CALLS**

* **(Last Chance) CppCon Call For Volunteers Now Open** – Interested volunteers have until August 1st to apply at the CppCon main conference which is scheduled to take place from 14th – 18th September. For more information including how to apply visit [https://cppcon.org/cfv2026/](https://cppcon.org/cfv2026/)

**TRAINING COURSES AVAILABLE FOR PURCHASE**

Conferences are offering the following training courses:

**CppCon Online Workshops**

**9th – 11th September**

1. **Modern C++: When Efficiency Matters** – Andreas Fertig – 3 day online workshop available on 9th – 11th September 09.00 – 15.00 MDT – [https://cppcon.org/class-2026-when-efficiency-matters/](https://cppcon.org/class-2026-when-efficiency-matters/)
2. **System Architecture And Design Using Modern C++** – Charley Bay – 3 day online workshop available on 9th – 11th September 09.00 – 15.00 MDT – [https://cppcon.org/class-2026-system-architecture-and-design-using-modern-cpp/](https://cppcon.org/class-2026-system-architecture-and-design-using-modern-cpp/)

**21st – 23rd September**

1. **C++ Fundamentals You Wish You Had Known Earlier** – Mateusz Pusz – 3 day online workshop available on 21st– 23rd September 09.00 – 15.00 MDT – [https://cppcon.org/class-2026-cpp-fundamentals/](https://cppcon.org/class-2026-cpp-fundamentals/)
2. **C++23 in Practice: A Complete Introduction** – Nicolai Josuttis – 3 day online workshop available on 21st– 23rd September 09.00 – 15.00 MDT – [https://cppcon.org/class-2026-cpp23-in-practice/](https://cppcon.org/class-2026-cpp23-in-practice/)
3. **Programming with C++20** – Andreas Fertig – 3 day online workshop available on 21st– 23rd September 09.00 – 15.00 MDT – [https://cppcon.org/class-2026-programming-with-cpp20/](https://cppcon.org/class-2026-programming-with-cpp20/)

**26th – 27th September**

1. **Using C++ for Low-Latency Systems** – Patrice Roy – 2 day online workshop available on 26th– 27th September 09.00 – 17.00 MDT – [https://cppcon.org/class-2026-low-latency/](https://cppcon.org/class-2026-low-latency/)

This is the latest news from upcoming C++ Conferences. You can review all of the news at [https://programmingarchive.com/upcoming-conference-news/](https://programmingarchive.com/upcoming-conference-news/)

**CppCon Onsite Workshops**

All onsite workshops will take place in the Gaylord Rockies in Aurora, Colorado

**12th & 13th September**

1. **Advanced and Modern C++ Programming: The Tricky Parts** – Nicolai Josuttis – 2 day in-person workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-tricky-parts/](https://cppcon.org/class-2026-tricky-parts/)
2. **C++ Best Practices** – Jason Turner – 2 day in-person workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-best-practices/](https://cppcon.org/class-2026-best-practices/)
3. **How Hardware Gets Hacked: Breaking and Defending Embedded Systems** – Nathan Jones – 2 day in-person workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-hardware-hack/](https://cppcon.org/class-2026-hardware-hack/)
4. **Mastering \`std::execution\`: A Hands-On Workshop** – Mateusz Pusz – 2 day in-person

workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-execution/](https://cppcon.org/class-2026-execution/)
5. **Performance and Efficiency in C++ for Experts, Future Experts, and Everyone Else** – Fedor Pikus – 2 day in-person workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-performance-and-efficiency/](https://cppcon.org/class-2026-performance-and-efficiency/)
6. **Talking Tech** – Sherry Sontag – 2 day in-person workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-talking-tech/](https://cppcon.org/class-2026-talking-tech/)

 **13th September**

1. **AI++ 101 : Build a C++ Coding Agent from Scratch** – Jody Hagins – 2 day in-person workshop available on 12th & 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-AI101/](https://cppcon.org/class-2026-AI101/)
2. **Essential GDB and Linux System Tools** – Mike Shah – 1 day in-person workshop available on 13th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-essential-gdb/](https://cppcon.org/class-2026-essential-gdb/)

**19th & 20th September**

1. **AI++ 201: Building High Quality C++ Infrastructure with AI** – Jody Hagins – 2 day in-person workshop available on 19th & 20th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-ai201/](https://cppcon.org/class-2026-ai201/)
2. **Function and Class Design with C++2x** – Jeff Garland – 2 day in-person workshop available on 19th & 20th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-function-class-design/](https://cppcon.org/class-2026-function-class-design/)
3. **High-performance Concurrency in C++** – Fedor Pikus – 2 day in-person workshop available on 19th & 20th September – 09:00 – 17:00 – [https://cppcon.org/class-2026-high-perf-concurrency/](https://cppcon.org/class-2026-high-perf-concurrency/)

**OTHER NEWS**

* **Dates for ACCU on Sea 2027 Announced –** ACCU on Sea 2027 will take place in Folkestone from June 30th – July 3rd with pre-conference workshops taking place from June 28th – 29th
* **Boost Documentary screening at CppCon 2026** – Boost Libraries have announced that they will be screening a documentary on the history of Boost at CppCon 2026. Watch the trailer here [https://www.youtube.com/watch?v=87jvuDbnwqQ](https://www.youtube.com/watch?v=87jvuDbnwqQ)
* **C++Now 2026 Videos Now Being Released on YouTube** – Subscribe to the C++Now YouTube channel to stay up to date when each video is published – [https://www.youtube.com/@CppNow](https://www.youtube.com/@CppNow)

https://redd.it/1v93gvk
@r_cpp

amask = f(amask) on NEON. faster than the obvious blend

## Problem

Apply an operation to elements that satisfy a condition:

for (sizet i = 0; i < n; ++i)
if (mask(a[i])) a[i] = f(a[i]);

### Notes

- `a[i] ∈ (0, 1)`, `thd ∈ (0, 1)`, `mask = a[i] < thd`; uniform distribution (except at the end of the article)
- `f` is one of `sqrt`, `frfrexp` (mantissa), `sin` 3.5 ULP, `sin` 1 ULP, `pow` 1 ULP (from SLEEF)
- `f` and `mask` are passed as runtime values, so they are wrapped in a lambda with `alwaysinline, otherwise they may
not be inlined
- The array size n is a multiple of every unroll, tile etc. The tail is trivial to handle(BSL/scalar)
- In tables ` = best, units = GiB/s
- Don't compare numbers across tables. Different conditions, values fluctuate
- All benchmarks: Apple M5; clang++ -O3 -std=c++23 -march=native; GiB/s = (n 4 bytes) / time, min of 720 runs
(During bench, functions run in a changing order, data is restored ofc); n=1e7 + 2432;

## BSL blend

If the problem is memory bound (cheap function or high density), the standard algorithm is optimal:

template <bool Skip>
void bsl(float dst, const size_t n, auto f, auto mask) {
for (size_t i = 0; i < n; i += 16) {
std::array<float32x4_t, 4> v;
for (size_t j = 0; j < 4; ++j) v[j] = vld1q_f32(dst + i + 4 j);
std::array<uint32x4t, 4> m;
for (sizet j = 0; j < 4; ++j) mj = mask(vj);
if constexpr (Skip) {
if (vmaxvqu32(vaddqu32(vaddqu32(m[0], m[1]), vaddqu32(m2, m3))) == 0) continue;
}
for (sizet j = 0; j < 4; ++j) vst1qf32(dst + i + 4 j, vbslq_f32(m[j], f(v[j]), v[j]));
}
}

It computes `f` on every element, but stores only the selected ones.
Skip helps on sparse masks, but otherwise mispredictions will kill performance. We'll need it later.

But for expensive `f` this algo does too much extra work

## Detour

To avoid unnecessary work, we compress selected elements, apply only to them, and expand back.

avx512 does this in two instructions. NEON doesn't, so we'll emulate and optimize.

constexpr size_t tile = 4096;
constexpr std::array<uint32_t, 4> weights{1 + 16, 2 + 16, 4 + 16, 8 + 16};
constexpr auto cps_tbl = compress_table();
constexpr auto exp_tbl = expand_table();
std::array<float, tile + 16> tmp;
std::array<uint8_t, tile / 4 + 3> s;
std::array<uint16_t, tile / 4 + 3> idx; // idx, D and B come in later
constexpr double D = 0.845;
constexpr double B = 0.3;

template <bool Skip>
size_t detour(float dst, const sizet n, const auto w, auto f, auto mask) {
float* ptr = tmp.data();
for (sizet i = 0; i < n; i += 16) {
std::array<float32x4t, 4> v;
for (sizet j = 0; j < 4; ++j) vj = vld1qf32(dst + i + 4 * j);
std::array<uint32x4t, 4> m;
for (sizet j = 0; j < 4; ++j) m[j] = mask(v[j]);

if constexpr (Skip)
if (vmaxvqu32(vaddqu32(vaddqu32(m0, m1), vaddqu32(m[2], m[3]))) == 0) {
s[i / 4] = s[i / 4 + 1] = s[i / 4 + 2] = s[i / 4 + 3] = 0;
continue;
}

std::array<uint32t, 4> sk;
for (sizet j = 0; j < 4; ++j) {
sk[j] = vaddvqu32(vandqu32(m[j], w));
s[i / 4 + j] = sk[j];
}
std::array<sizet, 4> off; off0 = 0;
for (sizet j = 1; j < 4; ++j) off[j] = off[j - 1] + (sk[j - 1] >> 4);
std::array<uint8x16t, 4> index;
for (sizet j = 0; j < 4; ++j) index[j] = vld1qu8(cpstbl[sk[j] & 15].data());

for (sizet j = 0; j < 4; ++j) vst1qf32(ptr + off[j], vreinterpretqf32u8(vqtbl1qu8(vreinterpretqu8f32(vj), indexj)));
ptr += off3 + (sk3 >> 4);
}
const sizet size = ptr - tmp.data();

if (size == 0) return size;

ptr = tmp.data();
for (sizet i = 0; i < size; i += 16) {

a[mask] = f(a[mask]) on NEON. faster than the obvious blend

## Problem

Apply an operation to elements that satisfy a condition:

for (size_t i = 0; i < n; ++i)
if (mask(a[i])) a[i] = f(a[i]);

### Notes

- `a[i] ∈ (0, 1)`, `thd ∈ (0, 1)`, `mask = a[i] < thd`; uniform distribution (except at the end of the article)
- `f` is one of `sqrt`, `frfrexp` (mantissa), `sin` 3.5 ULP, `sin` 1 ULP, `pow` 1 ULP (from SLEEF)
- `f` and `mask` are passed as runtime values, so they are wrapped in a lambda with `always_inline`, otherwise they may
not be inlined
- The array size `n` is a multiple of every unroll, tile etc. The tail is trivial to handle(BSL/scalar)
- In tables `*` = best, units = GiB/s
- Don't compare numbers across tables. Different conditions, values fluctuate
- All benchmarks: Apple M5; clang++ -O3 -std=c++23 -march=native; GiB/s = (n * 4 bytes) / time, min of 720 runs
(During bench, functions run in a changing order, data is restored ofc); n=1e7 + 2432;

## BSL blend

If the problem is memory bound (cheap function or high density), the standard algorithm is optimal:

template <bool Skip>
void bsl(float* dst, const size_t n, auto f, auto mask) {
for (size_t i = 0; i < n; i += 16) {
std::array<float32x4_t, 4> v;
for (size_t j = 0; j < 4; ++j) v[j] = vld1q_f32(dst + i + 4 * j);
std::array<uint32x4_t, 4> m;
for (size_t j = 0; j < 4; ++j) m[j] = mask(v[j]);
if constexpr (Skip) {
if (vmaxvq_u32(vaddq_u32(vaddq_u32(m[0], m[1]), vaddq_u32(m[2], m[3]))) == 0) continue;
}
for (size_t j = 0; j < 4; ++j) vst1q_f32(dst + i + 4 * j, vbslq_f32(m[j], f(v[j]), v[j]));
}
}

It computes `f` on every element, but stores only the selected ones.
Skip helps on sparse masks, but otherwise mispredictions will kill performance. We'll need it later.

But for expensive `f` this algo does too much extra work

## Detour

To avoid unnecessary work, we compress selected elements, apply only to them, and expand back.

avx512 does this in two instructions. NEON doesn't, so we'll emulate and optimize.

constexpr size_t tile = 4096;
constexpr std::array<uint32_t, 4> weights{1 + 16, 2 + 16, 4 + 16, 8 + 16};
constexpr auto cps_tbl = compress_table();
constexpr auto exp_tbl = expand_table();
std::array<float, tile + 16> tmp;
std::array<uint8_t, tile / 4 + 3> s;
std::array<uint16_t, tile / 4 + 3> idx; // idx, D and B come in later
constexpr double D = 0.845;
constexpr double B = 0.3;

template <bool Skip>
size_t detour(float* dst, const size_t n, const auto w, auto f, auto mask) {
float* ptr = tmp.data();
for (size_t i = 0; i < n; i += 16) {
std::array<float32x4_t, 4> v;
for (size_t j = 0; j < 4; ++j) v[j] = vld1q_f32(dst + i + 4 * j);
std::array<uint32x4_t, 4> m;
for (size_t j = 0; j < 4; ++j) m[j] = mask(v[j]);

if constexpr (Skip)
if (vmaxvq_u32(vaddq_u32(vaddq_u32(m[0], m[1]), vaddq_u32(m[2], m[3]))) == 0) {
s[i / 4] = s[i / 4 + 1] = s[i / 4 + 2] = s[i / 4 + 3] = 0;
continue;
}

std::array<uint32_t, 4> sk;
for (size_t j = 0; j < 4; ++j) {
sk[j] = vaddvq_u32(vandq_u32(m[j], w));
s[i / 4 + j] = sk[j];
}
std::array<size_t, 4> off; off[0] = 0;
for (size_t j = 1; j < 4; ++j) off[j] = off[j - 1] + (sk[j - 1] >> 4);
std::array<uint8x16_t, 4> index;
for (size_t j = 0; j < 4; ++j) index[j] = vld1q_u8(cps_tbl[sk[j] & 15].data());

for (size_t j = 0; j < 4; ++j) vst1q_f32(ptr + off[j], vreinterpretq_f32_u8(vqtbl1q_u8(vreinterpretq_u8_f32(v[j]), index[j])));
ptr += off[3] + (sk[3] >> 4);
}
const size_t size = ptr - tmp.data();

if (size == 0) return size;

ptr = tmp.data();
for (size_t i = 0; i < size; i += 16) {

std::array<float32x4_t, 4> v;
for (size_t j = 0; j < 4; ++j) v[j] = vld1q_f32(ptr + i + 4 * j);
for (size_t j = 0; j < 4; ++j) vst1q_f32(ptr + i + 4 * j, f(v[j]));
}

for (size_t i = 0; i < n; i += 16) {
std::array<uint32_t, 4> sk;
for (size_t j = 0; j < 4; ++j) sk[j] = s[i / 4 + j];
std::array<size_t, 4> off{};
for (size_t j = 1; j < 4; ++j) off[j] = off[j - 1] + (sk[j - 1] >> 4);
std::array<float32x4_t, 4> v;
for (size_t j = 0; j < 4; ++j) v[j] = vld1q_f32(ptr + off[j]);
std::array<float32x4_t, 4> a;
for (size_t j = 0; j < 4; ++j) a[j] = vld1q_f32(dst + i + 4 * j);
std::array<uint8x16_t, 4> index;
for (size_t j = 0; j < 4; ++j) index[j] = vld1q_u8(exp_tbl[sk[j] & 15].data());
std::array<uint8x16x2_t, 4> tbl;
for (size_t j = 0; j < 4; ++j) tbl[j] = {{vreinterpretq_u8_f32(v[j]), vreinterpretq_u8_f32(a[j])}};
for (size_t j = 0; j < 4; ++j) vst1q_f32(dst + i + 4 * j, vreinterpretq_f32_u8(vqtbl2q_u8(tbl[j], index[j])));
ptr += off[3] + (sk[3] >> 4);
}
return size;
}

void tiled_detour(float* dst, const size_t n, auto f, auto mask) {
const auto w = vld1q_u32(weights.data());
for (size_t i = 0; i < n; i += tile)
detour<false>(dst + i, tile, w, f, mask);
}

compress is the same as
in [my previous post](https://www.reddit.com/r/cpp/comments/1uqscnr/stream_compaction_on_neon_vectorizing_copy_if_by/).

expand_table: for true lanes it selects the next element from the compressed register (bytes from `[0, 15]`),
and for false lanes, selects the same bytes + 16.
Then tbl2 on {processed, original}, the same trick as in compress basically

Also:

- expand is skipped for free on empty tiles
- `s` is saved for free to avoid recomputing addv
- cache-sized tiling.
- Empirically `tile=4096` is optimal.

BSL speed doesn't depend on density: `sqrt 32.1`, `sin35 - 9.7`, `pow10 1.02`.

| thd | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
|:-------------|--------:|--------:|--------:|--------:|--------:|--------:|-------:|-------:|-------:|-------:|------:|
| detour sqrt | 38.03\* | 18.81 | 17.78 | 16.82 | 15.98 | 15.21 | 14.51 | 13.85 | 13.24 | 12.71 | 12.30 |
| detour sin35 | 38.16\* | 16.70\* | 14.46\* | 12.71\* | 11.36\* | 10.25\* | 9.35 | 8.59 | 7.95 | 7.39 | 6.95 |
| detour pow10 | 38.09\* | 6.83\* | 4.13\* | 2.96\* | 2.31\* | 1.89\* | 1.60\* | 1.39\* | 1.22\* | 1.10\* | 0.99 |

For cheap sqrt BSL is always better (except thd = 0, obviously).
And for very expensive pow, detour is better (except thd = 1, of course).

## When detour wins

Define:

- `B` = BSL(`vld + vbsl + vst`) overhead per register.
- `D` = detour(compress + expand) overhead per register.
- `T` = cost of `f` per register (we assume `cost of f >> cost of mask`, affects only calibration accuracy).
- `d` = fraction of selected elements

BSL applies `f` to every register. detour applies it only to `d` of them, so it saves `T * (1 - d)`.
Detour wins when the saving outweighs `D - B`.

`T * (1 - d) > D - B`

So detour pays off when `d < d_max = 1 - (D - B) / T`.
`B` and `D` depend only on hardware, so let's premeasure them (I have `B = 0.3`, `D = 0.845` ns/register)

`T` we measure over the first few tiles, timing BSL. And from it, we also find `d_max`:

template<size_t tile>
double bsl_calibrate(float* dst, const size_t len, auto f, auto mask) {
double ns = 1e18;
for (size_t i = 0; i < len; i += tile) {
const auto st = std::chrono::high_resolution_clock::now();
bsl<false>(dst, tile, f, mask);
const auto ed = std::chrono::high_resolution_clock::now();
ns = std::min(ns, std::chrono::duration<double, std::nano>(ed - st).count());
dst += tile;
}
const double t = std::max(1e-9, ns / (tile / 4.0) - B);

return 1.0 - (D - B) / t;
}

Here:

- empirically 4 tiles of 2048 are enough
- min over measurements is less noisy than mean
- We'll run the first few tiles with calibration
- measure bsl, because it always computes `f`, so `T = t_bsl - B`
- std::max here protects against divide-by-zero and against `t < 0` when `T` is very cheap

### pilot v1

When `d_max < 0` bsl is always faster:

void pilot_v1(float* dst, const size_t n, auto f, auto mask) {
const auto w = vld1q_u32(weights.data());
const float d_max = bsl_calibrate<tile / 2>(dst, 2 * tile, f, mask);

dst += 2 * tile;
for (size_t i = 2 * tile; i < n; i += tile) {
if (d_max < 0)
bsl<false>(dst, tile, f, mask);
else
detour<false>(dst, tile, w, f, mask);
dst += tile;
}
}

| thd | tiled detour sqrt | pilot v1 sqrt | BSL sqrt | tiled detour sin35 | pilot v1 sin35 | BSL sin35 |
|----:|------------------:|--------------:|---------:|-------------------:|---------------:|----------:|
| 0 | 38.37\* | 32.19 | 32.24 | 38.26\* | 38.16 | 9.75 |
| 0.3 | 16.89 | 32.18 | 32.24\* | 12.72\* | 12.71 | 9.75 |
| 0.6 | 14.54 | 32.18\* | 32.18\* | 9.25 | 9.36 | 9.75\* |
| 1 | 12.29 | 32.21 | 32.25\* | 6.87 | 6.86 | 9.75\* |

The algorithm got sqrt right.
But for sin35 at high thd, detour is selected, and we lose 30%:
v1 switches to bsl only when it's faster at every density.

### pilot v2

It's expensive to calculate the density of the whole tile, so we'll use the first 256
(It reads 6% of the tile, which is noise on pow, but noticeable on sqrt)

For a more or less uniform distribution this is enough:

size_t density(float* dst, const size_t n, auto mask) {
std::array<uint32x4_t, 4> acc;
acc.fill(vdupq_n_u32(0));
for (size_t i = 0; i < n; i += 16) {
for (size_t j = 0; j < 4; ++j)
acc[j] = vsubq_u32(acc[j], mask(vld1q_f32(dst + i + 4 * j)));
}
return vaddvq_u32(vaddq_u32(vaddq_u32(acc[0], acc[1]), vaddq_u32(acc[2], acc[3])));
}

Trick here: true lane of bitmask = 0xFFFFFFFF = -1, subtracting the lane actually adds.

Skip wins when the predictor rarely mispredicts, i.e. an 80% chance that all 4 registers are empty.
The density is approximately `0.014` (`(1 - x)^16 = 0.8`)

void pilot_v2(float* dst, const size_t n, auto f, auto mask) {
const auto w = vld1q_u32(weights.data());

const float d_max = bsl_calibrate<tile / 2>(dst, 2 * tile, f, mask);

constexpr size_t probe = 256;
constexpr size_t xlo = 0.014 * probe;
const long long hi = d_max * probe;
dst += 2 * tile;
for (size_t i = 2 * tile; i < n; i += tile) {
const long long cnt = density(dst, probe, mask);
if (cnt > hi) {
if (cnt < xlo)
bsl<true>(dst, tile, f, mask);
else
bsl<false>(dst, tile, f, mask);
} else if (cnt < xlo)
detour<true>(dst, tile, w, f, mask);
else
detour<false>(dst, tile, w, f, mask);
dst += tile;
}
}

`hi` and `xlo` here are `d_max` and `0.014` cutoffs, but multiplied by the probe length (256).

| thd | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
|:---------------|--------:|--------:|--------:|--------:|--------:|--------:|-------:|-------:|-------:|-------:|-------:|
| pilot_v1 sin35 | 38.25 | 16.72\* | 14.48\* | 12.77\* | 11.41\* | 10.33\* | 9.40 | 8.62 | 7.93 | 7.36 | 6.91 |
| pilot_v2 sin35 | 77.29\* | 16.55 | 14.34 | 12.65 | 11.33 | 10.14 | 9.71 | 9.73 | 9.74 | 9.73 | 9.73 |
| BSL sin35 | 9.78 | 9.77 | 9.77 | 9.76 | 9.76 | 9.78 | 9.78\* | 9.77\* | 9.78\* | 9.77\* | 9.78\* |
|

pilot_v1 sin10 | 38.28 | 14.30\* | 11.18\* | 9.18\* | 7.75\* | 6.73\* | 5.94\* | 5.31\* | 4.78 | 4.36 | 4.02 |
| pilot_v2 sin10 | 76.60\* | 14.16 | 11.09 | 9.11 | 7.73 | 6.70 | 5.92 | 5.31\* | 4.82 | 4.84 | 4.84 |
| BSL sin10 | 4.85 | 4.85 | 4.84 | 4.85 | 4.84 | 4.84 | 4.85 | 4.85 | 4.85\* | 4.85\* | 4.85\* |

At thd = 0, Skip gives a huge win. For high thd, bsl is selected correctly.
But now on sparse masks, expand for empty registers is wasted.

### pilot v3

New detour version: during compress, we'll store only the indices of non-empty registers (into the `idx` buffer).
And expand will iterate over them:

template <bool Skip>
size_t detour_compact(float* dst, const size_t n, const auto w, auto f, auto mask) {
float* ptr = tmp.data();
size_t k = 0;
for (size_t i = 0; i < n; i += 16) {
// ... same as detour

if constexpr (Skip)
if (vmaxvq_u32(vaddq_u32(vaddq_u32(m[0], m[1]), vaddq_u32(m[2], m[3]))) == 0) continue;

std::array<uint32_t, 4> sk;
for (size_t j = 0; j < 4; ++j) sk[j] = vaddvq_u32(vandq_u32(m[j], w));
for (size_t j = 0; j < 4; ++j) {
s[k] = sk[j];
idx[k] = i + 4 * j;
k += bool(sk[j]);
}
// ... same as detour
}
// ... same as detour

for (size_t j = 0; j < 3; ++j) s[k + j] = 0, idx[k + j] = 0;
// ... same as detour
k = (k + 3) & ~size_t(3);
for (size_t i = 0; i < k; i += 4) {
std::array<uint32_t, 4> sk;
for (size_t j = 0; j < 4; ++j) sk[j] = s[i + j];
std::array<float32x4_t, 4> a;
for (size_t j = 0; j < 4; ++j) a[j] = vld1q_f32(dst + idx[i + j]);
// ... same as detour
for (size_t j = 0; j < 4; ++j)
vst1q_f32(dst + idx[i + j], vreinterpretq_f32_u8(vqtbl2q_u8(tbl[j], index[j])));
ptr += off[3] + (sk[3] >> 4);
}

return size;
}

k (number of non-empty registers) is rounded up to a multiple of 4 before expand,
so the unrolled loop has no tail left.

No branches in the hot loops: they'd kill speed, so compress runs on all four registers.
Instead of branches, the position of the current element is advanced by `bool(sk)`.

detour_compact wins when at least 50% of all registers are empty. The density is approximately 0.16 (`(1 - x) ^ 4 = 0.5`).

void pilot_v3(float* dst, const size_t n, auto f, auto mask) {
// ... same as v2
constexpr size_t lo = 0.16 * probe;
for (size_t i = 2 * tile; i < n; i += tile) {
const long long cnt = density(dst, probe, mask);
if (cnt > hi) {
if (cnt < xlo)
bsl<true>(dst, tile, f, mask);
else
bsl<false>(dst, tile, f, mask);
} else if (cnt < xlo)
detour_compact<true>(dst, tile, w, f, mask);
else if (cnt < lo)
detour_compact<false>(dst, tile, w, f, mask);
else
detour<false>(dst, tile, w, f, mask);
dst += tile;
}
}

And v3 is noticeably faster at low density:

| thd | 0 | 0.02 | 0.04 | 0.06 | 0.08 | 0.1 | 0.12 | 0.14 | 0.16 | 0.18 | 0.2 |
|:---------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| pilot_v2 sin35 | 77.29\* | 18.67 | 18.40 | 17.70 | 17.18 | 16.66 | 16.15 | 15.68 | 15.24\* | 14.82\* | 14.43\* |
| pilot_v3 sin35 | 77.26 | 23.71\* | 22.19\* | 20.48\* | 19.03\* | 17.82\* | 16.73\* | 15.76\* | 15.16 | 14.74 | 14.38 |
| pilot_v2 pow10 | 72.88 | 13.93 | 11.32 | 9.38 | 8.00 | 6.97 | 6.18 | 5.54 | 5.02\* | 4.59\* | 4.24\* |
| pilot_v3 pow10 | 73.71\* | 16.54\* | 12.63\* | 10.07\* | 8.39\* | 7.17\* | 6.26\* | 5.55\* | 5.02\* | 4.59\* | 4.24\* |

Now the algo is fast,

but there's one big problem we've overlooked: we're assuming the data is uniform.
So it's easy to build a test where v3 will fail:

for (size_t i = 0; i < n; i++)
dst[i] = i % 4096 >= 256;

In this case, v3 always prefers BSL, even though detour wins on 3840 elements of the tile.

### pilot v3.5

According to the first table, in the worst case detour is under 3x slower (it happens on sqrt thd = 1), but on pow,
thd = 0, detour is 37x faster.
So a wrong BSL costs much more than a wrong detour.

For bsl, we'll play it safe by running it in tile/8 blocks and checking the density. If it drops well below `d_max`,
we'll switch to detour.
It costs 1 instruction per register. `acc = vsubq_u32(acc, m)` works because the mask is 0/-1. And unlike the probe, the
density here is exact.

size_t bsl_verified(float* dst, const size_t n, float d_max, auto f, auto mask) {
for (size_t i0 = 0; i0 < 8; ++i0) {
std::array<uint32x4_t, 4> acc;
acc.fill(vdupq_n_u32(0));

for (size_t i = 0; i < n / 8; i += 16) {
std::array<float32x4_t, 4> v;
for (size_t j = 0; j < 4; ++j) v[j] = vld1q_f32(dst + 4 * j);
std::array<uint32x4_t, 4> m;
for (size_t j = 0; j < 4; ++j) m[j] = mask(v[j]);
for (size_t j = 0; j < 4; ++j) acc[j] = vsubq_u32(acc[j], m[j]);
for (size_t j = 0; j < 4; ++j)
vst1q_f32(dst + 4 * j, vbslq_f32(m[j], f(v[j]), v[j]));

dst += 16;
}
const size_t cur = vaddvq_u32(vaddq_u32(vaddq_u32(acc[0], acc[1]), vaddq_u32(acc[2], acc[3])));
if (static_cast<double>(cur) / (n / 8) < 0.75 * d_max) return (i0 + 1) * n / 8;
}
return n;
}

BSL bails out when the density is less than `0.75 * d_max`. I have no math behind the 0.75, it just won on average.

And pilot v3.5 will use bsl_verified if the density > hi:

void pilot_v3_5(float* dst, const size_t n, auto f, auto mask) {
// ... same as v3

for (size_t i = 2 * tile; i < n; i += tile) {
const long long cnt = density(dst, probe, mask);
if (cnt > hi) {
if (hi < 0) {
if (cnt < xlo)
bsl<true>(dst, tile, f, mask);
else
bsl<false>(dst, tile, f, mask);
} else {
auto done = bsl_verified(dst, tile, d_max, f, mask);
if (done < tile)
detour<false>(dst + done, tile - done, w, f, mask);
}
}
// ... same as v3
}
}

But here too it's easy to build a countertest:

for (size_t i = 0; i < n; ++i)
dst[i] = i % 4096 >= 390;

The first block will pass the check (> 75% zeros in it), but the second won't. BSL runs on it for nothing, and for pow
that's expensive. v3.5's problem: it has no memory.
After a miss the algo keeps trusting the first 256 and misses on every tile.

### pilot v4

v4 will fix this: if bsl bails out, we stop trusting the probe for the next 16 tiles, and instead we take the density of
the previous tile:

void pilot_v4(float* dst, const size_t n, auto f, auto mask) {
// ... same as v3

size_t distrust = 0;
size_t prev = 0;
for (size_t i = 2 * tile; i < n; i += tile) {
if (distrust) --distrust;

const long long cnt = distrust ? prev : density(dst, probe, mask);
if (cnt > hi) {
if (hi < 0) {
if (cnt < xlo) {
bsl<true>(dst, tile, f, mask);
} else {
bsl<false>(dst, tile, f, mask);
}
} else {
if (distrust == 0) {
auto done = bsl_verified(dst, tile, d_max, f, mask);
if (done < tile) {
distrust = 16;
const size_t rem =

detour<false>(dst + done, tile - done, w, f, mask);
prev = rem * probe / (tile - done);
}
} else {
prev = detour<false>(dst, tile, w, f, mask) * probe / tile;
}
}
} else if (cnt < xlo) {
prev = detour_compact<true>(dst, tile, w, f, mask) * probe / tile;
} else if (cnt < lo) {
prev = detour_compact<false>(dst, tile, w, f, mask) * probe / tile;
} else {
prev = detour<false>(dst, tile, w, f, mask) * probe / tile;
}
dst += tile;
}
}

And v4 easily passes that test. pow10:

| thd | 0 | 1 |
|--------------|--------:|-------:|
| tiled detour | 38.18 | 6.94 |
| pilot v3 | 68.11 | 1.04 |
| pilot v3.5 | 68.67 | 3.84 |
| pilot v4 | 69.51\* | 7.05\* |
| BSL | 1.04 | 1.04 |

btw here thd no longer matches the density. For thd = 0, density = 0, and for thd = 1, density = 9.5%

It beats v3.5 by over 80%. It's also faster than plain tiled detour, because v4 picks detour_compact. This is the final
version.

## Results

v4 vs BSL:

| thd | 0 | 0.05 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 |
|:-----------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| pilot_v4 sqrt | 69.81\* | 31.99\* | 31.84 | 31.73 | 31.83 | 32.07\* | 31.98 | 31.96\* | 31.96 | 31.89\* | 31.91 | 31.77 |
| BSL sqrt | 31.88 | 31.93 | 31.95\* | 31.80\* | 31.89\* | 31.99 | 32.06\* | 31.92 | 31.98\* | 31.75 | 31.93\* | 31.91\* |
| pilot_v4 frfrexp | 69.41\* | 17.21\* | 16.57 | 15.79 | 15.99 | 16.06 | 15.93 | 15.99 | 16.39 | 15.84 | 15.87 | 15.80 |
| BSL frfrexp | 16.79 | 16.87 | 16.82\* | 16.71\* | 16.78\* | 16.88\* | 16.89\* | 16.83\* | 16.89\* | 16.79\* | 16.75\* | 16.75\* |
| pilot_v4 sin10 | 68.19\* | 18.82\* | 14.84\* | 10.84\* | 8.93\* | 7.55\* | 6.56\* | 5.81\* | 5.19\* | 4.69 | 4.68 | 4.68 |
| BSL sin10 | 4.78 | 4.75 | 4.78 | 4.72 | 4.75 | 4.74 | 4.75 | 4.77 | 4.75 | 4.76\* | 4.77\* | 4.77\* |
| pilot_v4 pow10 | 64.17\* | 10.76\* | 6.85\* | 4.04\* | 2.89\* | 2.25\* | 1.85\* | 1.57\* | 1.36\* | 1.20\* | 1.08\* | 1.00 |
| BSL pow10 | 1.02 | 1.02 | 1.02 | 1.01 | 1.01 | 1.01 | 1.02 | 1.02 | 1.02 | 1.02 | 1.02 | 1.01\* |

Against BSL, it loses at worst 6%, but wins big much more often.
To reduce the loss, dispatch can be sped up: use every 4th register in density and bsl_verified. But that helps only if
the density is uniform.

The worst v4 miss I found: 512 dense, 512 empty, then everything is dense until the end of the cycle (17 * 4096).

const size_t cycle = 17 * 4096;
for (size_t i = 0; i < n; ++i) {
dst[i] = i % cycle >= 512 && i % cycle < 1024;
}

This hurts most with the cheapest `f` (with `d_max > 0`, of course):

const auto a = vdupq_n_f32(0.5f);
#pragma unroll
for (int i = 0; i < 13; ++i) x = vfmaq_f32(a, x, a);
return x;

| thd | 0 | 1 |
|--------------|--------:|--------:|
| tiled detour | 38.29 | 9.44 |
| pilot v3 | 74.72 | 15.81\* |
| pilot v3.5 | 74.84 | 15.04 |
| pilot v4 | 74.89\* | 9.84 |
| BSL | 15.61 | 15.60 |

v3.5's biggest loss is limited by BSL, while v4 is limited by detour. And a wrong detour is the cheaper mistake.
So v4 isn't always better, but its misses are less severe.

This problem has no perfect solution. Any dispatch algo can be countertested.

Full code: [godbolt](https://godbolt.org/z/nKhPjEn6v).


https://redd.it/1v94b7w
@r_cpp

Implementing the Tuple API easily using custom annotations and reflections

Hey, so I've been working on something interesting lately. It's a solution to a problem that you may have encountered in one form or anothing but it boils down to this: you have a struct:

struct mystruct
{
    long a;
    int b;
    char c;
};

And you want this to work:

auto [a, b, c] = instance;

Well it doesn't, aggregates don't implement the tuple API required for structured bindings by default and implementing it manually is a pain. You need to write your own specialization of std::tuple\size, std::tuple_element and implement a visible get<> method. It might not be that much work at first glance but it becomes very tiresome if you have multiple such structs or if you try to deviate from the general default indexing and not to mention a paint to maintain and update.

Well, why write repeated code when you can only write code once that writes that code for you? Reflections are mature enough in gcc's implementation to solve this. Simply put I have compiling code that with only the application of an annotation gives me all of this:

struct [=tuple_like] simpletuple
{
    long a;
    int b;
    char c;
};

Through which I get:

staticassert(std::tuplesizev<simpletuple> == 3);


constexpr simpletuple simple { 1, 2, 3 };


staticassert(1 == get<0>(simple));
staticassert(2 == get<1>(simple));
staticassert(3 == get<2>(simple));


staticassert(std::sameas<std::tupleelementt<0, simpletuple>, long>);
staticassert(std::sameas<std::tupleelementt<1, simpletuple>, int>);
staticassert(std::sameas<std::tupleelementt<2, simpletuple>, char>);

And of couse:

auto a, b, c = simple;

It doesn't stop here though, since the case I described above is common but inflexible. Customization is available and an opt-in feature:

struct [=tuple_like] customizabletuple
{
    constexpr customizabletuple(long a, const char b, float c, char d, double e)
        : a(a)
        , b(b)
        , c(c)
        , d(d)
        , e(e)
    {}


    [[=tuple_element(0)]]
    long a;
    [[=read_only_tuple_element(3)]]
    const char b;
    [=tuple_element(2)]
    float c;
    char d;


private:
    [=read_only_tuple_element(1)]
    double e;
};

It supports:

tuple element index reordering
implicit data member exclusion from the API
explicit read-only modifier
explicit opt-in for private fields
correct semantics for members which are reference types

&#8203;

constexpr customizable_tuple custom( 1, "2", 3, 4, 5 );

static_assert(std::tuple_size_v<customizable_tuple> == 4);

static_assert(1 == get<0>(custom));
static_assert(std::string_view{"2"} == get<3>(custom));
static_assert(3 == get<2>(custom));
static_assert(5 == get<1>(custom));

static_assert(std::same_as<std::tuple_element_t<0, customizable_tuple>, long>);
static_assert(std::same_as<std::tuple_element_t<1, customizable_tuple>, const double>);
static_assert(std::same_as<std::tuple_element_t<2, customizable_tuple>, float>);
static_assert(std::same_as<std::tuple_element_t<3, customizable_tuple>, const char const>);

But it doesn't stop there, no it doesn't. The part that took the most effort to implement were the error messages. No, we have better static_assert's in C++26 now, we abuse them:

Example 1:

staticassert(3 == get<3>(simple));

/app/myreflectutils.hpp:819:17:

error:
static assertion failed: reflectutils: get<3> called for tuple-like type 'simpletuple', but its reflected tuple size is 3.

Example 2:

[[=tupleelement(1)]]
long a;
[=tuple_element(2)]
int b;
[=tuple_element(3)]
char c;

/app/myreflectutils.hpp:632:21: error: static assertion failed: reflectutils: invalid tuple layout for 'simpletuple': tuple index 0 is missing. There are 3

explicitly annotated members, so their indices must be exactly 0, 3). Member 'simple_tuple::c' uses index 3.

Example 3:

[[=tuple_element(0)]
[=tuple_element(1)]
long a;

/app/myreflectutils.hpp:632:21: error: static assertion failed: reflectutils: invalid tuple layout for 'simpletuple': member 'simpletuple::a' has 2 tuple annotations; use exactly one of [[=tupleelement(i)]] and [=read_only_tuple_element(i)].

Full code: https://godbolt.org/z/exjW6YGEr

https://redd.it/1v9u2d5
@r_cpp

C++26: Reducing undefined behaviour
https://www.sandordargo.com/blog/2026/07/29/cpp26-reduces-undefined-behaviour

https://redd.it/1v9x7nt
@r_cpp

Building a compiler that works at compile-time so you can compile your program while you compile your program.

In short, I wanted to build a compiler of some C subset that would work at compile-time. It compiles into a custom byte-code for a runtime VM.

I've once tried to write a compile-time C compiler, but I abandoned that project, because I made it overly complex (one-pass compiler right into x86). No clear separation between parser, lexer, etc.

Why would I even want this? Idk. But how can it be useful?
- The code of the compiler doesn't go to the resulting binary,
- No need to waste time for compilation at runtime too,
- Guaranteed type-safety. There can't be such thing as "oh, I changed the function signature, but forgot to update the bindings and it crashed at runtime"

And I shouldn't forget about cons:
- No optimisations. Real compilers spent decades on them and I'm definitely not going to implement LLVM at runtime. Although we could make a compile-time x86 VM, so we can run it at compile time... no, thank you, it's a topic for another fever dream article.
- Hot-reload! I mean, no hot-reload. I won't even mention it anymore, considering that the script is compiled at compile-time and is builtin right into the binary file. I could implement it with hot memory patching or smth, but who really needs it.

Let's start.
## Bypassing constexpr limitations
C++ 20 lets us to dynamically allocate memory at compile-time and even use std::vector that really expands our borders. But there's one very important note - you can't declare a compile-time vector and extract it into the runtime. No constexpr std::vector<int> data = makeData();, it won't compile. So we need to hack it.

### Passing strings in templates
Sadly, the C++ Committee made a lot of cool compile-time features, but not enough (at least for me). We still can't use strings in templates without hacks. But we can easily bypass it with a well-known trick.

template<std::size_t N>  
struct const_string {  
  constexpr const_string() = default;  
  
  // implicit-constructor that lets us to do bad things
  constexpr const_string(const char (&str)[N]) {
    std::copy_n(str, N, value);  
  }  
  
  constexpr operator std::string_view() const {  
    return {value, value + N - 1};  
  }  
  
  char value[N]{};  
  const std::size_t length = N;  
};

// using it
template<const_string str>
auto very_smart_function(...) { /* ... */ }

### Extracting vectors from compile time
It turned out to be not really that hard, but I didn't really find any ready examples on Internet, unlike with const_string.
To extract std::vector<T> from constexpr we need to make it std::array<T, N> somehow. The main problem is that we can't write std::array<T, myVector.size()>, because myVector.size() won't be a constant value. So we must to make it constant somehow.
I thought of passing vector as a template parameter, but we can't do it legally. C++ 20 allows us to pass only the structs with all-public members. After deeply thinking a bit (not really), I discovered that I could simply pass the lambda that returns our vector (I didn't think I could just pass a pointer actually).

// data_getter is our lambda
template<auto data_getter>
constexpr auto to_array() {  
  using value_type = typename decltype(data_getter())::value_type;  
  constexpr static std::size_t size = data_getter().size();  
  
  // Create a static array with a "dynamic" size and copy all data
  std::array<value_type, size> out;  
  auto in = data_getter();  
  for (std::size_t i = 0; i < size; ++i) {  
    out[i] = in[i];  
  }  
  return out; // yay
}

template<const_string str>
constexpr auto lex() {
 constexpr static auto data_getter = [] constexpr {
  // .lex() returns the vector of tokens
  return lexer{static_cast<std::string_view>(str)}.lex();
 };
 // All our data are available for runtime now =D
 return to_array<data_getter>();
}

### Printing errors
For nice errors C++ has static_assert that allows us to even print our custom message! But it must be always a literal (until C++

Building a compiler that works at compile-time so you can compile your program while you compile your program.

In short, I wanted to build a compiler of some C subset that would work at compile-time. It compiles into a custom byte-code for a runtime VM.

I've once tried to write a compile-time C compiler, but I abandoned that project, because I made it overly complex (one-pass compiler right into x86). No clear separation between parser, lexer, etc.

Why would I even want this? Idk. But how can it be useful?
- The code of the compiler doesn't go to the resulting binary,
- No need to waste time for compilation at runtime too,
- Guaranteed type-safety. There can't be such thing as "oh, I changed the function signature, but forgot to update the bindings and it crashed at runtime"

And I shouldn't forget about cons:
- No optimisations. Real compilers spent decades on them and I'm definitely not going to implement LLVM at runtime. Although we could make a compile-time x86 VM, so we can run it at compile time... no, thank you, it's a topic for another fever dream article.
- Hot-reload! I mean, no hot-reload. I won't even mention it anymore, considering that the script is compiled at compile-time and is builtin right into the binary file. I could implement it with hot memory patching or smth, but who really needs it.

Let's start.
## Bypassing constexpr limitations
C++ 20 lets us to dynamically allocate memory at compile-time and even use `std::vector` that really expands our borders. But there's one very important note - you can't declare a compile-time vector and extract it into the runtime. No `constexpr std::vector<int> data = makeData();`, it won't compile. So we need to hack it.

### Passing strings in templates
Sadly, the C++ Committee made a lot of cool compile-time features, but not enough (at least for me). We still can't use strings in templates without hacks. But we can easily bypass it with a well-known trick.
```cpp
template<std::size_t N>
struct const_string {
constexpr const_string() = default;

// implicit-constructor that lets us to do bad things
constexpr const_string(const char (&str)[N]) {
std::copy_n(str, N, value);
}

constexpr operator std::string_view() const {
return {value, value + N - 1};
}

char value[N]{};
const std::size_t length = N;
};

// using it
template<const_string str>
auto very_smart_function(...) { /* ... */ }
```

### Extracting vectors from compile time
It turned out to be not really that hard, but I didn't really find any ready examples on Internet, unlike with `const_string`.
To extract `std::vector<T>` from constexpr we need to make it `std::array<T, N>` somehow. The main problem is that we can't write `std::array<T, myVector.size()>`, because `myVector.size()` won't be a constant value. So we must to make it constant somehow.
I thought of passing vector as a template parameter, but we can't do it legally. C++ 20 allows us to pass only the structs with all-public members. After deeply thinking a bit (not really), I discovered that I could simply pass the lambda that returns our vector (I didn't think I could just pass a pointer actually).

```C++
// data_getter is our lambda
template<auto data_getter>
constexpr auto to_array() {
using value_type = typename decltype(data_getter())::value_type;
constexpr static std::size_t size = data_getter().size();

// Create a static array with a "dynamic" size and copy all data
std::array<value_type, size> out;
auto in = data_getter();
for (std::size_t i = 0; i < size; ++i) {
out[i] = in[i];
}
return out; // yay
}

template<const_string str>
constexpr auto lex() {
constexpr static auto data_getter = [] constexpr {
// .lex() returns the vector of tokens
return lexer{static_cast<std::string_view>(str)}.lex();
};
// All our data are available for runtime now =D
return to_array<data_getter>();
}
```

### Printing errors
For nice errors C++ has `static_assert` that allows us to even print our custom message! But it must be always a literal (until C++

26)
```cpp
constexpr auto parse() {
// Allowed
static_assert(false, "Expected ';'");

// Not allowed :( (until C++ 26)
std::size_t line = 5;
static_assert(false, "Expected ';' at line " + to_string(line));
}
```
I didn't want my project to require C++ 26, so I used another trick. The formatted string gets turned into a static array just like a vector (into `const_string` actually) and then it's passed into `ErrorMessage<const_string Msg>` that triggers compilation error. So we force the compiler to print the full type name that includes our error. But sadly the type name has a limit about 100 symbols. I think I could solve it with splitting the message into several ErrorMessages... God, I don't want to read this in my console.
```c++
template<const_string Msg>
struct ErrorMessage {
static_assert(false, "Check the template parameter for details");
};

template<auto err_getter>
consteval auto report_error() -> void {
// C++ 26 support
#ifdef KORKA_FEATURE_FORMATTED_STATIC_ASSERT
static_assert(false, to_string(err_getter()));
#else
constexpr auto msg = const_string_from_string_view<[] { return to_string(err_getter()); }>();
std::ignore = ErrorMessage<msg>{};
#endif
}
```

I don't want you to see it, so I'll just show C++ 26 version.
```
error: static assertion failed: Lexer Error: Unterminated string at line 12
```

### Mapping signatures to names. And vice versa
In our little runtime C++ we are used to `std::unordered_map<string, value_t>` and other standard or non-standard (hello, Boost!) containers. But I needed a table where a key is a string and the value is a TYPE. And in C++ I can't treat types as values, I can't just put them into a dict... :(

So, welcome another hack!
```c++
template<auto, class>
struct signature_mapper;

// function_info_getter takes an index to our mapped function,
// and Is... holds all indices
template<auto function_info_getter, std::size_t... Is>
struct signature_mapper<
function_info_getter,
std::index_sequence<Is...>
> {
// hash func
consteval static auto hash(auto &&v) -> std::size_t {
return frozen::elsa<std::string_view>{}(v, 0);
}

// Our function overloaded with many unique types based on hash of the mapped function
constexpr static auto _overloaded = overloaded{
(
[](unique_type<hash(function_info_getter(Is).name)>)
-> const_function_info_to_signature_t<[] { return function_info_getter(Is); }> * {
return nullptr;
}
)...
};

// Extracting the type by name
template<const_string name>
using get_signature_t = std::remove_pointer_t<decltype(
_overloaded(
unique_type<hash(name)>{}
)
)>;

};
```

We use well-known function overload (~~but for evil things~~). Basically, one type inherits a lot of lambdas that take an empty `unique_type<hash>` that serves as our key and returns the pointer to our type.
```cpp
// How our mapper looks after expanding our params
struct overloaded : lambda1, lambda2, lambda3 {
using lambda1::operator();
using lambda2::operator();
using lambda3::operator();
};

// And every lambda looks like this
auto lambda_fib = [](unique_type<hash("fib")>) -> signature_of_fib* { return nullptr; };
```
When we call `_overloaded(unique_type<hash(name)>())` our poor compiler has to resolve the overload. And he looks for right one through all `()` operators. And then we just take that it returns (our `T*`) and get the `T`.
I use this "mechanism" to extract script functions into the native C++.
```cpp
constexpr auto script_fib = compile_result.function<"fib">();
```

### Bindings from C++ to our script lang
This was the most exhausting part. Well, how "exhausting" exactly... I was thinking for a few evenings and then made it work one morning. The problem was with me. I wanted to make a pretty API that was impossible in the current standard (maybe it's possible in C++ 26, but I didn't check it).
I wanted it to look like this:
```cpp
auto func() -> void;
auto foo(int) -> int;

// Примерно так
constexpr auto bindings = korka::make_bindings<
"func", func,
"foo", foo
>();

// Или так
constexpr auto

bindings = korka::make_bindings(
"func", func,
"foo", foo
);
```
But why couldn't I make it work?
In C++ you can't pass a string into `template <auto ...args>`. We need `const_string`. We can't mix types in the one stream of variadic args and make compiler guess it right. Templates require explicitness and it's impossible to write a universal parser.
Variant #2 works, but you can't extract the function into the runtime. You just can't. Functions may have different signatures, but you need to make them all the same type, and create a FFI wrapper along the way. Compile-time doesn't allow `reinterpretet_cast<void*>(&func)`.

So I designed this:
```cpp
constexpr auto bindings = korka::make_bindings(
korka::wrap<fib>("cpp_fib"),
korka::wrap<print_n>("print_n")
);
```
Not so elegant, but still not bad.
`wrap` is very simple
```cpp
// our FFI signature
using vm_external_function_type = void(vm::context_base &context);

// info for our compiler
template<class Signature>
struct wrapped_function {
using signature_t = Signature;

vm_external_function_type &external_func;
std::string_view name;
};

template<auto func>
consteval auto wrap(std::string_view name) {
return wrapped_function<std::decay_t<decltype(func)>>{
binding_wrapper<func>,
name
};
}
```
The most interesting part is inside `binding_wrapper<func>`. I won't show the full code here, because I still didn't tell about the VM architecture that will execute it. But in short binding_wrapper just checks the signature, generates some code that extracts arguments from VM, calls native functions and then puts the result back. Simple.
## The compiler and the VM
Maybe the most interesting part of the article. I have never written any compilers before (the thing I mentioned in the beginning of the article doesn't count), so I made it according to the first articles I found in Google.

Compiler has 3 modules:
- the lexer - splitting the code into tokens,
- the parser - building a tree from the tokens,
- the compiler itself - making the tree into byte-code. And doing semantic analysis at the same time (I was too lazy to make another module)
I think I could compose everything into one class via composition or smth, but it's too late already.

### Lexer
Primitive. We just look for tokens in a loop until we reach EOF.
```cpp
constexpr auto scan_token() -> std::optional<std::expected<lex_token, error_t>> {
char c = advance();
switch (c) {
case '{':
return make_token(lex_kind::kOpenBrace);
case '}':
return make_token(lex_kind::kCloseBrace);
case '(':
return make_token(lex_kind::kOpenParenthesis);
case ')':
// ...

case ' ':
case '\r':
case '\t':
// Ignore whitespace
return std::nullopt;

// ...

default:
if (is_digit(c)) {
return scan_number();
} else if (is_alpha(c)) {
return scan_identifier();
}
}
}
```

### Parser
More interesting. We need to build the AST (abstract syntax tree). And we need to store this tree somehow. The usual way with `Node` that keeps pointers to other nodes won't do, because we're at compile-time. I mean, we can write it this way, it will work, but extracting this tree into compile time? No. We would need serialisation or something. So we can use simple trick with `std::vector<Node>` and just make nodes store indices to each other.
This approach also increases the cache locality of the data for the CPU, but I doubt the CPU will be even aware of our "smart" trick, since everything is executed at compile-time.

The parser is recursive, while parsing one expression we parse another. Small fragment of the code:
```cpp
constexpr auto parse_statement() -> parse_result {
auto tok = peek();
if (!tok) return make_error("Unexpected end of input");

switch (tok->kind) {
case lex_kind::kOpenBrace: return parse_compound_stmt(); // { ... }
case lex_kind::kIf: return parse_if_statement(); // if (...) ...
case lex_kind::kWhile: return parse_while_statement(); //

while (...) ...
case lex_kind::kReturn: return parse_return_statement(); // return ...;
default: return parse_expression_stmt(); /// ...;
}
}

constexpr auto parse_return_statement() -> parse_result {
if (!match(lex_kind::kReturn)) return make_error("Expected 'return'");

index_t expr_idx = empty_node; // empty_node = -1
if (auto next = peek(); next && next->kind != lex_kind::kSemicolon) {
auto expr = parse_expression(); // another recursive call
if (!expr) return std::unexpected{expr.error()};
expr_idx = *expr;
}

if (!match(lex_kind::kSemicolon)) return make_error("Expected ';' after return");
return m_pool.add(stmt_return{expr_idx});
}
```
parse_return_statement goes into parse_expression, that goes into parse_assigment, that goes into parse_logical_or, that goes... Well, you got it. That's how operator priority works here.

### Their Majesty Compiler (and analyser)
I may have cheated here a bit.

Before we even write a compiler, we must know for what architecture we do it. x86, ARM or even JVM.
Initially when I was working on a similar project, I planned to generate raw assembly for x86 (last versions of Clang and GCC support passing `constexpr std::string_view` into `asm(...)` statement), but honestly writing a compiler for a zoo of x86 instructions is the right way to madhouse.

And even so, if we downgrade our compiler we won't have nice constexpr asm anymore. And we can't also generate raw machine instructions because of DEP (data execution prevention). We'll have to call non-crossplatform `mmap` or `VirtualAlloc` to allocate some memory, copy the code there... Good riddance cross platform build compler, hello Windows Defender that will kill our app for such tricks with memory.

So where have I cheated? I made my own architecture that will execute inside a VM. A stack VM.
Why stack it? It turned out to be incredibly easy to generate the bytecode for. If you are doing a register architecture (as in processors or Lua), then you will have to write register allocation algorithms (it is difficult). And in the stack everything is much simpler.

If we need to sum A and B we just do this:
1. Put A into the stack.
2. Put B into the stack.
3. Execute sum instruction. It takes these two values and puts back their sum.

So I had this set of instructions at the end:
```cpp
enum class op_code : char {
// Loads/saves locals to/from the stack (variables).
lload, lsave,

i64_const, // Puts a constant onto the stack

// Math
i64_add, i64_sub, i64_mul, i64_div,

// Puts 1 if values are equal (i made <, <= later)
i64_cmp,

jmp, // jumps by offset
jmpz, // conditional jump by offset, only when 0 on the stack

call, // calls a function
ret, // returns from the function

trap, // calls a native C++ function
};
```

### So what about the analysis?
In classical compilers phases are strictly splitted: lexers builds tokens, parser builds tree, semantic analyser checks the types and variables, then optimisations, then codegen and then optimisations again.

As you can remember, I'm pretty lazy. And keep in mind that `constexpr` ops are not infinite. I didn't want to make a separate pipeline phase. So my compiler combines these two functions: semantic analysis and code generation.

They usually call it Single-Pass Compilation, but it's not really the case here, since it's only about these two phases. My compiler is a bit hybrid.

My compiler recursively walks the tree and does two things:
1. Checks the semantics: "was this variable declared and what's its type" before we even try to multiply something. Do function param types match? Does this function even exist? Etc.
2. Generates the byte-code. If semantics is ok, then we just write corresponding instructions immediately into the `std::vector<std::byte>` (the one we're going to elegantly extract via `to_array`).
And in the result we receive a ready, semantically-correct and absolute safe (let's pretend that I wrote the compiler bug-free, huh) byte-code that we feed to the

VM.

## So what do we have?
Let's look how the API of my poor lib looks (let's call it Korka).

This example uses bindings (100% type safe, I swear on the standard):
```cpp

// Our native C++ functions
auto fib(std::int64_t n) -> std::int64_t {
if (n == 0) return 0;
if (n == 1) return 1;
return fib(n - 1) + fib(n - 2);
}

auto print_n(std::int64_t n) -> void {
std::cout << n << '\n';
}

// Our not-so-native script
constexpr char code[] = R"(
int fib(int n) {
if (n == 0) return 0;
if (n == 1) return 1;
return fib(n-1) + cpp_fib(n-2);
}

void print_fib(int n) {
int result = fib(n);
print_n(result);
return;
}
)";

// We create bindings
constexpr auto bindings = korka::make_bindings(
korka::wrap<fib>("cpp_fib"),
korka::wrap<print_n>("print_n")
);

// Compile at compile-time, yay
constexpr auto compile_result = korka::compile<code, &bindings>();

// Extract function adressess + their types
constexpr auto script_fib = compile_result.function<"fib">();
constexpr auto script_print_fib = compile_result.function<"print_fib">();

int main() {
// Init VM
korka::vm::context ctx{compile_result.bytes, bindings};

// Call fib that returns int64_t
auto result = ctx.call(script_fib, 12L);
std::cout << "fib(12) = " << result << '\n'; // prints 144

// Call print_fib
ctx.call(script_print_fib, 16L); // prints 987

return 0;
}
```

Ta da! It works.

## Small analysis

Out of curiosity, I decided to compare Korka with other scripting languages. A pretty API is great, sure, but was it worth the effort performance-wise? So, let's pit Korka head-to-head against Python and Lua.

For the benchmark, I used the recursive calculation of the $N$-th Fibonacci number, an excellent test to fairly evaluate overhead on function calls, stack management, and overall runtime efficiency (the first thing that came to my head).

I tested everything on a franken-server put together from spare parts, powered by an Intel Xeon E5-2689 (3.6 GHz).

I measured two stages:
- **Initialization time** from runtime startup to being ready to execute the first instruction,
- **Execution time** of the algorithm itself.
### Stage 1: Initialization

|**Language / Library**|**Initialization time**|
|---|---|
|**Korka**|**1.5 µs**|
|**Lua**|152.6 µs|
|**Python**|25,097.0 µs|

Korka takes the lead: it starts 100 times faster than Lua and over 15,000 times faster than Python.

The explanation is simple: while Lua and Python are busy reading the script at startup, parsing it, compiling it into their byte-codes, and spinning up heavy infrastructure (including the GC), Korka does not. All the virtual machine has to do is grab the pre-compiled output (and allocate a tiny bit of memory).

### Stage 2: Runtime

After a series of optimizations, the results turned out pretty solid (I know comparing statically typed and dynamic languages isn't entirely fair, but who's gonna stop me?):

| **N** | **Iterations** | **Korka (ms)** | **Lua (ms)** | **Python (ms)** | **vs. Python** |
| ------ | -------------- | -------------- | ------------ | --------------- | -------------- |
| **10** | 100 000 | **1 055,67** | 1 534,98 | 1 591,10 | **1,51x** |
| **15** | 50 000 | **5 492,40** | 8 149,17 | 8 753,77 | **1,59x** |
| **20** | 20 000 | **24 263,39** | 36 268,86 | 38 459,88 | **1,59x** |
| **23** | 10 000 | **51 367,20** | 76 494,22 | 82 257,71 | **1,60x** |
| **25** | 5 000 | **67 441,69** | 100 850,20 | 108 836,54 | **1,61x** |
| **28** | 2 000 | **114 341,21** | 169 626,06 | 184 869,57 | **1,62x** |
| **30** | 1 000 | **149 190,02** | 223 229,86 | 241 838,28 | **1,62x** |


### Conclusion
I built a (mostly) full-fledged C compiler that runs entirely in `constexpr`. Why? No idea. Especially considering it's been done before, you can check out [constexpr-8cc](https://github.com/keiichiw/constexpr-8cc). But that one lacks C++ bindings and cross-platform support.

The source code is