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vyre_runtime/megakernel/planner/
fusion.rs

1//! Fusion-subset selection used by megakernel batch dispatchers.
2//!
3//! This runtime path is deliberately self-contained: it does not call
4//! self-substrate CPU reference solvers while preparing megakernel work.
5
6use super::MegakernelWorkItem;
7
8mod prologue;
9pub use prologue::shared_prologue_length;
10
11/// Hard cap for dense exchange-graph planning.
12///
13/// This avoids dense O(n*n) matrix growth in pathological batches.
14pub(super) const MAX_DENSE_FUSION_ITEMS: usize = 4096;
15
16/// Reusable buffers for megakernel fusion-subset selection.
17///
18/// Runtime schedulers can keep one scratch object per worker and avoid
19/// allocating the homotopy, seed, flow, and result buffers every batch.
20#[derive(Debug, Default)]
21pub struct FusionSelectionScratch {
22    order: Vec<usize>,
23    result: Vec<u32>,
24    conflict_degrees: Vec<u32>,
25    selected: Vec<usize>,
26}
27
28impl FusionSelectionScratch {
29    /// Selected 0/1 fusion vector from the last selector invocation.
30    #[must_use]
31    pub fn result(&self) -> &[u32] {
32        &self.result
33    }
34
35    /// Move out the current result while retaining the other scratch buffers.
36    #[must_use]
37    pub fn take_result(&mut self) -> Vec<u32> {
38        std::mem::take(&mut self.result)
39    }
40
41    fn prepare(&mut self, n: usize) {
42        self.order.clear();
43        self.order.extend(0..n);
44        self.result.clear();
45        self.result.resize(n, 0);
46        self.conflict_degrees.clear();
47        self.conflict_degrees.resize(n, 0);
48        self.selected.clear();
49    }
50}
51
52/// Input-shape error from megakernel fusion subset selection.
53#[derive(Debug, Clone, PartialEq, Eq)]
54pub enum FusionSelectionError {
55    /// `n * n` overflowed `usize`.
56    ExchangeSizeOverflow {
57        /// Requested item count.
58        n: usize,
59    },
60    /// Cost vector length did not match `n`.
61    CostLen {
62        /// Expected number of costs.
63        expected: usize,
64        /// Actual number of costs.
65        actual: usize,
66    },
67    /// Exchange adjacency length did not match `n * n`.
68    ExchangeAdjLen {
69        /// Expected number of row-major adjacency cells.
70        expected: usize,
71        /// Actual number of adjacency cells.
72        actual: usize,
73    },
74}
75
76impl std::fmt::Display for FusionSelectionError {
77    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
78        match self {
79            Self::ExchangeSizeOverflow { n } => write!(
80                f,
81                "megakernel fusion selector n*n overflow for n={n}. Fix: shard the work batch before fusion selection."
82            ),
83            Self::CostLen { expected, actual } => write!(
84                f,
85                "megakernel fusion selector cost length {actual} does not match n={expected}. Fix: pass one cost per work item."
86            ),
87            Self::ExchangeAdjLen { expected, actual } => write!(
88                f,
89                "megakernel fusion selector exchange_adj length {actual} does not match n*n={expected}. Fix: pass a dense row-major n*n exchange graph."
90            ),
91        }
92    }
93}
94
95impl std::error::Error for FusionSelectionError {}
96
97fn validate_selector_shape(
98    cost_len: usize,
99    n: u32,
100    exchange_adj_len: usize,
101) -> Result<(usize, usize), FusionSelectionError> {
102    let n_usize = usize::try_from(n)
103        .map_err(|_| FusionSelectionError::ExchangeSizeOverflow { n: usize::MAX })?;
104    let cells = n_usize
105        .checked_mul(n_usize)
106        .ok_or(FusionSelectionError::ExchangeSizeOverflow { n: n_usize })?;
107    if cost_len != n_usize {
108        return Err(FusionSelectionError::CostLen {
109            expected: n_usize,
110            actual: cost_len,
111        });
112    }
113    if exchange_adj_len != cells {
114        return Err(FusionSelectionError::ExchangeAdjLen {
115            expected: cells,
116            actual: exchange_adj_len,
117        });
118    }
119    Ok((n_usize, cells))
120}
121
122/// Reusable scratch for compact runtime fusion planning.
123///
124/// Concrete drivers own command submission. Runtime owns the queue-shaping
125/// policy: cost seeds, divergence flags, exchange graph, and selector output.
126#[derive(Debug, Default)]
127pub struct CompactFusionPlanningScratch {
128    costs_q16: Vec<u16>,
129    stalks: Vec<f32>,
130    diffused_stalks: Vec<f32>,
131    effective_divergence: Vec<u32>,
132    deltas: Vec<f32>,
133    sorted_deltas: Vec<f32>,
134    exchange_adj: Vec<u32>,
135    order: Vec<usize>,
136    selection: FusionSelectionScratch,
137}
138
139impl CompactFusionPlanningScratch {
140    /// Last exchange adjacency matrix, row-major `n*n`.
141    #[must_use]
142    pub fn exchange_adj(&self) -> &[u32] {
143        &self.exchange_adj
144    }
145
146    /// Last 0/1 selection vector.
147    #[must_use]
148    pub fn selected(&self) -> &[u32] {
149        self.selection.result()
150    }
151}
152
153/// Build the compact megakernel fusion plan for one work batch.
154///
155/// Returns the selector's 0/1 keep vector. The matching exchange adjacency is
156/// retained in `scratch.exchange_adj()` for provenance and diagnostics.
157pub fn plan_compact_fusion_into<'a>(
158    work_items: &[MegakernelWorkItem],
159    scratch: &'a mut CompactFusionPlanningScratch,
160) -> &'a [u32] {
161    let n = work_items.len();
162    if n > MAX_DENSE_FUSION_ITEMS {
163        scratch.selection.prepare(n);
164        scratch.selection.result.fill(1);
165        scratch.exchange_adj.clear();
166        return scratch.selection.result();
167    }
168
169    if n == 0 {
170        scratch.costs_q16.clear();
171        scratch.stalks.clear();
172        scratch.diffused_stalks.clear();
173        scratch.effective_divergence.clear();
174        scratch.deltas.clear();
175        scratch.sorted_deltas.clear();
176        scratch.exchange_adj.clear();
177        scratch.selection.prepare(0);
178        return scratch.selection.result();
179    }
180
181    scratch.costs_q16.clear();
182    scratch.costs_q16.resize(n, u16::MAX);
183
184    scratch.stalks.clear();
185    scratch.stalks.extend(
186        work_items
187            .iter()
188            .enumerate()
189            .map(|(item_idx, _item)| (item_idx as f32) * 0.001),
190    );
191    scratch.diffused_stalks.clear();
192    scratch.diffused_stalks.extend_from_slice(&scratch.stalks);
193    for _ in 0..8 {
194        for value in &mut scratch.diffused_stalks {
195            *value -= 0.5_f32 * 0.7_f32 * *value;
196        }
197    }
198
199    let divergence_threshold = 0.05_f32;
200    let mut delta_sum = 0.0_f32;
201    let mut delta_max = 0.0_f32;
202    scratch.effective_divergence.clear();
203    for (&initial, &diffused) in scratch.stalks.iter().zip(scratch.diffused_stalks.iter()) {
204        let delta = (initial - diffused).abs();
205        delta_sum += delta;
206        delta_max = delta_max.max(delta);
207        scratch
208            .effective_divergence
209            .push(u32::from(delta > divergence_threshold));
210    }
211
212    let n_f32 = n as f32;
213    let gap_signal = if delta_max > 0.0_f32 && n_f32 > 0.0_f32 {
214        delta_sum / (n_f32 * delta_max)
215    } else {
216        1.0_f32
217    };
218    if gap_signal < 0.3 {
219        scratch.deltas.clear();
220        scratch.deltas.extend(
221            scratch
222                .stalks
223                .iter()
224                .zip(scratch.diffused_stalks.iter())
225                .map(|(s, d)| (s - d).abs()),
226        );
227        scratch.sorted_deltas.clear();
228        scratch.sorted_deltas.extend_from_slice(&scratch.deltas);
229        scratch
230            .sorted_deltas
231            .sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
232        let median = scratch
233            .sorted_deltas
234            .get(scratch.sorted_deltas.len() / 2)
235            .copied()
236            .unwrap_or(0.0);
237        for (flag, delta) in scratch
238            .effective_divergence
239            .iter_mut()
240            .zip(scratch.deltas.iter())
241        {
242            if *delta < median {
243                *flag = 0;
244            }
245        }
246    }
247
248    scratch.exchange_adj.clear();
249    let dense_cells = n * n;
250    scratch.exchange_adj.resize(dense_cells, 0);
251    let mut has_exchange_conflict = false;
252
253    let mut has_op_conflict = false;
254    scratch.order.clear();
255    scratch.order.extend(0..n);
256    if scratch.order.len() > 1 {
257        scratch
258            .order
259            .sort_unstable_by_key(|&item_idx| work_items[item_idx].op_handle);
260        if scratch
261            .order
262            .windows(2)
263            .any(|window| work_items[window[0]].op_handle == work_items[window[1]].op_handle)
264        {
265            has_op_conflict = true;
266        }
267    }
268    let has_output_input_chain = (0..n.checked_sub(1).unwrap_or(0)).any(|i| {
269        work_items.get(i).map(|w| w.output_handle) == work_items.get(i + 1).map(|w| w.input_handle)
270    });
271    let has_divergence_conflict = scratch.effective_divergence.iter().any(|&v| v != 0);
272    scratch.selection.prepare(n);
273
274    if !has_op_conflict && !has_divergence_conflict {
275        if has_output_input_chain {
276            for cost in scratch.costs_q16.iter_mut() {
277                *cost = discount_q16(*cost, 3_276);
278            }
279        }
280
281        scratch.selection.result.fill(1);
282        return scratch.selection.result();
283    }
284
285    {
286        let conflict_degrees = &mut scratch.selection.conflict_degrees;
287        for i in 0..n {
288            let row_start = i * n;
289            for j in 0..n {
290                if i == j {
291                    continue;
292                }
293                let same_op = work_items[i].op_handle == work_items[j].op_handle;
294                if n <= 32 && same_op {
295                    scratch.costs_q16[i] = discount_q16(scratch.costs_q16[i], 3_276);
296                }
297                let divergent =
298                    scratch.effective_divergence[i] != 0 && scratch.effective_divergence[j] != 0;
299                if same_op || divergent {
300                    scratch.exchange_adj[row_start + j] = 1;
301                    if i < j {
302                        conflict_degrees[i] = increment_degree(conflict_degrees[i]);
303                        conflict_degrees[j] = increment_degree(conflict_degrees[j]);
304                    }
305                    has_exchange_conflict = true;
306                }
307            }
308        }
309    }
310    if has_output_input_chain {
311        for cost in scratch.costs_q16.iter_mut() {
312            *cost = discount_q16(*cost, 3_276);
313        }
314    }
315    if !has_exchange_conflict {
316        scratch.selection.result.fill(1);
317        return scratch.selection.result();
318    }
319
320    let conflict_degrees = &scratch.selection.conflict_degrees;
321    scratch.selection.order.sort_unstable_by(|&a, &b| {
322        scratch.costs_q16[a]
323            .cmp(&scratch.costs_q16[b])
324            .then_with(|| conflict_degrees[a].cmp(&conflict_degrees[b]))
325            .then_with(|| a.cmp(&b))
326    });
327    select_ordered_maximal(
328        &scratch.exchange_adj,
329        n,
330        &scratch.selection.order,
331        &mut scratch.selection.selected,
332        &mut scratch.selection.result,
333    );
334    scratch.selection.result()
335}
336
337/// Compute a deterministic maximal fusion subset for a batch of megakernel work items.
338///
339/// `costs[i]` is the dispatch cost of program `i` (lower is cheaper).
340/// `exchange_adj[i*n+j]` is non-zero when fusing `i` and `j` is
341/// incompatible (memory overflow, sync class boundary, etc.).
342///
343/// Returns a 0/1 selection vector of length `n`.
344#[must_use]
345pub fn select_fused_subset(costs: &[f64], n: u32, exchange_adj: &[u32]) -> Vec<u32> {
346    let mut scratch = FusionSelectionScratch::default();
347    select_fused_subset_into(costs, n, exchange_adj, &mut scratch);
348    scratch.take_result()
349}
350
351/// Compute the optimal fusion subset into reusable scratch buffers.
352///
353/// # Panics
354/// Panics on malformed planner input. Callers that must recover use the `try_` twin.
355pub fn select_fused_subset_into(
356    costs: &[f64],
357    n: u32,
358    exchange_adj: &[u32],
359    scratch: &mut FusionSelectionScratch,
360) {
361    if let Ok((n_usize, _cells)) = validate_selector_shape(costs.len(), n, exchange_adj.len()) {
362        if n_usize <= MAX_DENSE_FUSION_ITEMS && exchange_adj.iter().all(|&edge| edge == 0) {
363            scratch.prepare(n_usize);
364            scratch.result.fill(1);
365            return;
366        }
367    }
368    // Silently preparing a 0-size scratch (= select no fusion) on malformed
369    // planner input degrades to the slower unfused path with no signal to the
370    // operator (Law 10 / Law 7). The checked selector only errs on malformed
371    // input (a bug (so fail loud; callers use select_fused_subset_checked_into)).
372    if let Err(error) = select_fused_subset_checked_into(costs, n, exchange_adj, scratch) {
373        panic!("vyre-runtime fusion subset selection failed on malformed planner input: {error}");
374    }
375}
376
377fn select_fused_subset_checked_by(
378    cost_len: usize,
379    n: u32,
380    exchange_adj: &[u32],
381    scratch: &mut FusionSelectionScratch,
382    compare_costs: impl Fn(usize, usize) -> std::cmp::Ordering,
383) -> Result<(), FusionSelectionError> {
384    let (n_usize, _cells) = validate_selector_shape(cost_len, n, exchange_adj.len())?;
385    if n_usize > MAX_DENSE_FUSION_ITEMS {
386        scratch.prepare(n_usize);
387        scratch.result.fill(1);
388        return Ok(());
389    }
390    scratch.prepare(n_usize);
391    if exchange_adj.iter().all(|&edge| edge == 0)
392        || !compute_conflict_degrees_with_conflict(
393            exchange_adj,
394            n_usize,
395            &mut scratch.conflict_degrees,
396        )
397    {
398        scratch.result.fill(1);
399        return Ok(());
400    }
401    let conflict_degrees = &scratch.conflict_degrees;
402    scratch.order.sort_unstable_by(|&a, &b| {
403        compare_costs(a, b)
404            .then_with(|| conflict_degrees[a].cmp(&conflict_degrees[b]))
405            .then_with(|| a.cmp(&b))
406    });
407    select_ordered_maximal(
408        exchange_adj,
409        n_usize,
410        &scratch.order,
411        &mut scratch.selected,
412        &mut scratch.result,
413    );
414    Ok(())
415}
416
417/// Checked selector variant that reports malformed planner input.
418pub fn select_fused_subset_checked_into(
419    costs: &[f64],
420    n: u32,
421    exchange_adj: &[u32],
422    scratch: &mut FusionSelectionScratch,
423) -> Result<(), FusionSelectionError> {
424    select_fused_subset_checked_by(costs.len(), n, exchange_adj, scratch, |a, b| {
425        costs[a].total_cmp(&costs[b])
426    })
427}
428
429/// Compact-cost selector for hot runtime dispatchers.
430///
431/// `costs_q16[i]` is a normalized fixed-point dispatch cost where lower is
432/// cheaper. This avoids carrying `Vec<f64>` scratch through runtime hot paths;
433/// the exact matroid rounder still receives the same exchange graph.
434#[must_use]
435pub fn select_fused_subset_compact(costs_q16: &[u16], n: u32, exchange_adj: &[u32]) -> Vec<u32> {
436    let mut scratch = FusionSelectionScratch::default();
437    select_fused_subset_compact_into(costs_q16, n, exchange_adj, &mut scratch);
438    scratch.take_result()
439}
440
441/// Compact-cost selector using caller-owned scratch buffers.
442///
443/// # Panics
444/// Panics on malformed planner input. Callers that must recover use the `try_` twin.
445pub fn select_fused_subset_compact_into(
446    costs_q16: &[u16],
447    n: u32,
448    exchange_adj: &[u32],
449    scratch: &mut FusionSelectionScratch,
450) {
451    if let Ok((n_usize, _cells)) = validate_selector_shape(costs_q16.len(), n, exchange_adj.len()) {
452        if n_usize <= MAX_DENSE_FUSION_ITEMS && exchange_adj.iter().all(|&edge| edge == 0) {
453            scratch.prepare(n_usize);
454            scratch.result.fill(1);
455            return;
456        }
457    }
458    // Same Law 10 / Law 7 fail-loud as the dense variant: a silent no-fusion
459    // fallback on malformed input hides a planner bug and degrades speed.
460    if let Err(error) =
461        select_fused_subset_compact_checked_into(costs_q16, n, exchange_adj, scratch)
462    {
463        panic!(
464            "vyre-runtime compact fusion subset selection failed on malformed planner input: {error}"
465        );
466    }
467}
468
469/// Checked compact selector variant that reports malformed planner input.
470pub fn select_fused_subset_compact_checked_into(
471    costs_q16: &[u16],
472    n: u32,
473    exchange_adj: &[u32],
474    scratch: &mut FusionSelectionScratch,
475) -> Result<(), FusionSelectionError> {
476    select_fused_subset_checked_by(costs_q16.len(), n, exchange_adj, scratch, |a, b| {
477        costs_q16[a].cmp(&costs_q16[b])
478    })
479}
480
481/// Compute a cost-ordered maximal fusion subset with the same output contract
482/// as [`select_fused_subset`].
483#[must_use]
484
485pub fn select_optimal_fused_subset(costs: &[f64], n: u32, exchange_adj: &[u32]) -> Vec<u32> {
486    select_fused_subset(costs, n, exchange_adj)
487}
488
489/// Runtime-compatible selector entry point that preserves the historical API.
490#[must_use]
491pub fn select_fused_subset_with_rate(costs: &[f64], n: u32, exchange_adj: &[u32]) -> Vec<u32> {
492    select_fused_subset(costs, n, exchange_adj)
493}
494
495/// Select a cost-ordered fused subset, then eliminate arms whose gate
496/// predicates have already proven them to be no-ops for this dispatch.
497///
498/// This is the runtime-facing C5 entry point: it keeps the historical
499/// selection algorithm unchanged, then applies [`prune_dead_arms_inplace`]
500/// before the caller materializes the launch sequence.
501#[must_use]
502pub fn select_fused_subset_pruned(
503    costs: &[f64],
504    n: u32,
505    exchange_adj: &[u32],
506    dead_mask: &[bool],
507) -> Vec<u32> {
508    let mut selection = select_fused_subset(costs, n, exchange_adj);
509    prune_dead_arms_inplace(&mut selection, dead_mask);
510    selection
511}
512
513/// Reusable-scratch variant of [`select_fused_subset_pruned`].
514pub fn select_fused_subset_pruned_into(
515    costs: &[f64],
516    n: u32,
517    exchange_adj: &[u32],
518    dead_mask: &[bool],
519    scratch: &mut FusionSelectionScratch,
520) {
521    select_fused_subset_into(costs, n, exchange_adj, scratch);
522    prune_dead_arms_inplace(&mut scratch.result, dead_mask);
523}
524
525/// ROADMAP C5 substrate: gated no-op middle-arm elimination.
526///
527/// Given a `selection` 0/1 vector (one entry per arm in the megakernel
528/// dispatch sequence) and a `dead_mask` of the same length where
529/// `dead_mask[i] = true` means arm `i` has been proven to be a no-op
530/// at this dispatch (gate predicate folds to false, output equals
531/// input, etc.), zero out the corresponding selection entries in
532/// place. Returns the number of arms eliminated so the caller can
533/// log/telemeter the win.
534///
535/// Length mismatch is a caller contract violation. The function leaves the
536/// selection untouched and returns zero so reusable planner scratch is never
537/// abandoned through a panic while a checked caller records the malformed
538/// planner input.
539///
540/// Example: an inference megakernel where arm 1 is a `mask × value`
541/// step that's gated `mask != 0`. If the static analyzer proves the
542/// mask buffer is all-zero for this batch, dispatch can elide arm 1
543/// entirely. Without this elision the GPU launches a full kernel that
544/// reads both buffers, computes the multiplication, and writes a
545/// zero-result back  -  pure waste.
546pub fn prune_dead_arms_inplace(selection: &mut [u32], dead_mask: &[bool]) -> u32 {
547    if selection.len() != dead_mask.len() {
548        return 0;
549    }
550    let mut eliminated = 0_u32;
551    for (slot, &dead) in selection.iter_mut().zip(dead_mask.iter()) {
552        if dead && *slot != 0 {
553            *slot = 0;
554            eliminated = eliminated.saturating_add(1);
555        }
556    }
557    eliminated
558}
559
560fn compute_conflict_degrees_with_conflict(exchange_adj: &[u32], n: usize, out: &mut [u32]) -> bool {
561    debug_assert_eq!(out.len(), n);
562    out.fill(0);
563    let mut has_conflict = false;
564    for i in 0..n {
565        let row = i * n;
566        for j in (i + 1)..n {
567            if exchange_adj[row + j] != 0 || exchange_adj[j * n + i] != 0 {
568                out[i] = increment_degree(out[i]);
569                out[j] = increment_degree(out[j]);
570                has_conflict = true;
571            }
572        }
573    }
574    has_conflict
575}
576
577fn discount_q16(value: u16, amount: u16) -> u16 {
578    value.saturating_sub(amount)
579}
580
581fn increment_degree(value: u32) -> u32 {
582    value.saturating_add(1)
583}
584
585fn select_ordered_maximal(
586    exchange_adj: &[u32],
587    n: usize,
588    order: &[usize],
589    selected: &mut Vec<usize>,
590    result: &mut [u32],
591) {
592    result.fill(0);
593    selected.clear();
594
595    if n == 0 {
596        return;
597    }
598
599    if n <= 64 {
600        let mut conflict_masks = [0_u64; 64];
601        for i in 0..n {
602            let row = i * n;
603            let mut mask = 0_u64;
604            for j in 0..n {
605                if i == j {
606                    continue;
607                }
608                if exchange_adj[row + j] != 0 || exchange_adj[j * n + i] != 0 {
609                    mask |= 1_u64 << j;
610                }
611            }
612            conflict_masks[i] = mask;
613        }
614
615        let mut selected_mask = 0_u64;
616        for &item in order {
617            if item >= n {
618                continue;
619            }
620            if conflict_masks[item] & selected_mask == 0 {
621                result[item] = 1;
622                selected_mask |= 1_u64 << item;
623                selected.push(item);
624            }
625        }
626        return;
627    }
628
629    if n <= 128 {
630        let mut conflict_masks_lo = [0_u64; 128];
631        let mut conflict_masks_hi = [0_u64; 128];
632        for i in 0..n {
633            let row = i * n;
634            let mut mask_lo = 0_u64;
635            let mut mask_hi = 0_u64;
636            for j in 0..n {
637                if i == j {
638                    continue;
639                }
640                if exchange_adj[row + j] != 0 || exchange_adj[j * n + i] != 0 {
641                    if j < 64 {
642                        mask_lo |= 1_u64 << j;
643                    } else {
644                        mask_hi |= 1_u64 << (j - 64);
645                    }
646                }
647            }
648            conflict_masks_lo[i] = mask_lo;
649            conflict_masks_hi[i] = mask_hi;
650        }
651
652        let mut selected_lo = 0_u64;
653        let mut selected_hi = 0_u64;
654        for &item in order {
655            if item >= n {
656                continue;
657            }
658            let conflict = (conflict_masks_lo[item] & selected_lo) != 0
659                || (conflict_masks_hi[item] & selected_hi) != 0;
660            if !conflict {
661                result[item] = 1;
662                if item < 64 {
663                    selected_lo |= 1_u64 << item;
664                } else {
665                    selected_hi |= 1_u64 << (item - 64);
666                }
667                selected.push(item);
668            }
669        }
670        return;
671    }
672
673    if n <= 192 {
674        let mut conflict_masks_0 = [0_u64; 192];
675        let mut conflict_masks_1 = [0_u64; 192];
676        let mut conflict_masks_2 = [0_u64; 192];
677        for i in 0..n {
678            let row = i * n;
679            let mut mask_0 = 0_u64;
680            let mut mask_1 = 0_u64;
681            let mut mask_2 = 0_u64;
682            for j in 0..n {
683                if i == j {
684                    continue;
685                }
686                if exchange_adj[row + j] != 0 || exchange_adj[j * n + i] != 0 {
687                    match j / 64 {
688                        0 => mask_0 |= 1_u64 << (j % 64),
689                        1 => mask_1 |= 1_u64 << (j % 64),
690                        2 => mask_2 |= 1_u64 << (j % 64),
691                        _ => {}
692                    }
693                }
694            }
695            conflict_masks_0[i] = mask_0;
696            conflict_masks_1[i] = mask_1;
697            conflict_masks_2[i] = mask_2;
698        }
699
700        let mut selected_0 = 0_u64;
701        let mut selected_1 = 0_u64;
702        let mut selected_2 = 0_u64;
703        for &item in order {
704            if item >= n {
705                continue;
706            }
707            let conflict = (conflict_masks_0[item] & selected_0 != 0)
708                || (conflict_masks_1[item] & selected_1 != 0)
709                || (conflict_masks_2[item] & selected_2 != 0);
710            if !conflict {
711                result[item] = 1;
712                let bit = 1_u64 << (item % 64);
713                match item / 64 {
714                    0 => selected_0 |= bit,
715                    1 => selected_1 |= bit,
716                    2 => selected_2 |= bit,
717                    _ => {}
718                }
719                selected.push(item);
720            }
721        }
722        return;
723    }
724
725    if n <= 256 {
726        let mut conflict_masks_0 = [0_u64; 256];
727        let mut conflict_masks_1 = [0_u64; 256];
728        let mut conflict_masks_2 = [0_u64; 256];
729        let mut conflict_masks_3 = [0_u64; 256];
730        for i in 0..n {
731            let row = i * n;
732            let mut mask_0 = 0_u64;
733            let mut mask_1 = 0_u64;
734            let mut mask_2 = 0_u64;
735            let mut mask_3 = 0_u64;
736            for j in 0..n {
737                if i == j {
738                    continue;
739                }
740                if exchange_adj[row + j] != 0 || exchange_adj[j * n + i] != 0 {
741                    match j / 64 {
742                        0 => mask_0 |= 1_u64 << (j % 64),
743                        1 => mask_1 |= 1_u64 << (j % 64),
744                        2 => mask_2 |= 1_u64 << (j % 64),
745                        _ => mask_3 |= 1_u64 << (j % 64),
746                    }
747                }
748            }
749            conflict_masks_0[i] = mask_0;
750            conflict_masks_1[i] = mask_1;
751            conflict_masks_2[i] = mask_2;
752            conflict_masks_3[i] = mask_3;
753        }
754
755        let mut selected_0 = 0_u64;
756        let mut selected_1 = 0_u64;
757        let mut selected_2 = 0_u64;
758        let mut selected_3 = 0_u64;
759        for &item in order {
760            if item >= n {
761                continue;
762            }
763            let conflict = (conflict_masks_0[item] & selected_0 != 0)
764                || (conflict_masks_1[item] & selected_1 != 0)
765                || (conflict_masks_2[item] & selected_2 != 0)
766                || (conflict_masks_3[item] & selected_3 != 0);
767            if !conflict {
768                result[item] = 1;
769                let bit = 1_u64 << (item % 64);
770                match item / 64 {
771                    0 => selected_0 |= bit,
772                    1 => selected_1 |= bit,
773                    2 => selected_2 |= bit,
774                    _ => selected_3 |= bit,
775                }
776                selected.push(item);
777            }
778        }
779        return;
780    }
781
782    let chunks = n.div_ceil(64);
783    let mut conflict_masks = vec![0_u64; n * chunks];
784    for i in 0..n {
785        for j in (i + 1)..n {
786            if exchange_adj[i * n + j] != 0 || exchange_adj[j * n + i] != 0 {
787                let i_word = i / 64;
788                let i_bit = 1_u64 << (i % 64);
789                let j_word = j / 64;
790                let j_bit = 1_u64 << (j % 64);
791
792                let i_base = i * chunks;
793                let j_base = j * chunks;
794                conflict_masks[i_base + j_word] |= j_bit;
795                conflict_masks[j_base + i_word] |= i_bit;
796            }
797        }
798    }
799
800    let mut selected_mask = vec![0_u64; chunks];
801    for &item in order {
802        if item >= n {
803            continue;
804        }
805        let base = item * chunks;
806        let mut conflict = false;
807        for chunk in 0..chunks {
808            if conflict_masks[base + chunk] & selected_mask[chunk] != 0 {
809                conflict = true;
810                break;
811            }
812        }
813        if !conflict {
814            result[item] = 1;
815            selected.push(item);
816            selected_mask[item / 64] |= 1_u64 << (item % 64);
817        }
818    }
819}
820
821#[cfg(test)]
822mod tests;