Inner Loop
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Inner Loop has 26 facts recorded in Dontopedia across 11 references, with 2 live disagreements.
Mostly:rdf:type(5), iterates over(2), depends on(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (13)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
containsContains(3)
- Outer Loop
ex:outer-loop - Process Queries in Batches Function
ex:process-queries-in-batches-function - Training Loop
ex:training-loop
appliedInApplied in(1)
- Parallelizable Vectorized Update
ex:parallelizable-vectorized-update
appliesInlineInInnerLoopApplies Inline in Inner Loop(1)
- Redundant Rmsnorm Computation
ex:redundant-rmsnorm-computation
consistsOfConsists of(1)
- Nested Loop Structure
ex:nested-loop-structure
controlsControls(1)
- Outer Loop
ex:outer-loop
enclosesEncloses(1)
- Outer Loop
ex:outer-loop
exitsLoopExits Loop(1)
- Break Statement
ex:break-statement
isFullyUtilizedBecauseIs Fully Utilized Because(1)
- M4 Gpu 10 Core Compute Unit
ex:m4-gpu-10-core-compute-unit
isLoopVariableIs Loop Variable(1)
- Query
ex:query
iteratedByIterated by(1)
- Worker Counts
ex:worker_counts
precedesPrecedes(1)
- Outer Loop
ex:outer-loop
Other facts (26)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | For Loop | [5] |
| Rdf:type | Code Structure | [6] |
| Rdf:type | Iteration Statement | [7] |
| Rdf:type | Batch Loop | [9] |
| Rdf:type | Nested Loop | [11] |
| Iterates Over | Factors List | [5] |
| Iterates Over | Dataset | [9] |
| Depends on | Low Rank Harmonic | [1] |
| Is | Lohe Local Synchronization | [1] |
| Performs | Gated Lohe Phase Update | [2] |
| Receives Positive Evaluation | auto-vectorizes perfectly | [3] |
| Auto Vectorizes Across | All Alu Lanes | [3] |
| Auto Vectorizes Perfectly | Alu Lanes | [3] |
| Computes Dot Products | ~65K | [4] |
| Candidate for Acceleration | Metal or Amx | [4] |
| Iteration Variable | factor | [5] |
| Nested Within | Outer Loop | [5] |
| Has Property | auto-vectorizes | [6] |
| Vectorization Scope | all ALU lanes | [6] |
| Vectorization Quality | perfectly | [6] |
| Is Contained in | Outer Loop | [8] |
| Has Start Value | I Variable | [10] |
| Has End Value | Min Function Call | [10] |
| Is Controlled by | Outer Loop | [10] |
| Enclosed by | Outer Loop | [11] |
| Iterates | Worker Counts | [11] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (11)
ctx:discord/blah/watt-activation/part-208ctx:discord/blah/watt-activation/part-209ctx:discord/blah/watt-activation/part-529ctx:discord/blah/watt-activation/part-632ctx:claims/beam/4138d5af-2f28-48bd-82f2-ede483c92f8c- full textbeam-chunktext/plain1 KB
doc:beam/4138d5af-2f28-48bd-82f2-ede483c92f8cShow excerpt
:param weights: Dictionary of weights for each factor :return: Weighted score """ weighted_score = sum(option_scores[factor] * weights[factor] for factor in option_scores) return weighted_score def main(): # Define …
ctx:discord/blah/watt-activation/526- full textwatt-activation-526text/plain2 KB
doc:agent/watt-activation-526/9508ee51-23f2-48ee-a3d1-068a9c342df7Show excerpt
[2026-03-23 02:05] xenonfun: ## Performance Summary ``` ┌────────────────────────────────────┬─────────────┬────────────┐ │ Level │ Throughput │ vs │ │ │ …
ctx:claims/beam/e3b4edc5-6ce9-47ff-b092-3eb3e280084b- full textbeam-chunktext/plain1 KB
doc:beam/e3b4edc5-6ce9-47ff-b092-3eb3e280084bShow excerpt
return lang # Fallback to polyglot for rare languages detector = Detector(text) return detector.language.code except langdetect.LangDetectException: logging.error(f"Unable to detect l…
ctx:claims/beam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865ctx:claims/beam/0dc41777-2feb-464f-977d-396cd9e9853c- full textbeam-chunktext/plain1 KB
doc:beam/0dc41777-2feb-464f-977d-396cd9e9853cShow excerpt
- **Mixed Precision Training**: Use mixed precision training (e.g., `torch.cuda.amp`) to further improve performance. Would you like to explore any specific aspect further, such as mixed precision training or gradient accumulation? [Turn …
ctx:claims/beam/789ff1ce-e287-4688-bacb-e009f454ec0f- full textbeam-chunktext/plain1 KB
doc:beam/789ff1ce-e287-4688-bacb-e009f454ec0fShow excerpt
# Simulate covering groups of steps for i in range(1000, 14550, 100): # Cover steps in groups of 100 for j in range(i, min(i + 100, 14550)): self.steps[j].assert_called() self.cov…
ctx:claims/beam/fef4fa6f-c278-4da1-b9a8-0acd2941b0c7- full textbeam-chunktext/plain1 KB
doc:beam/fef4fa6f-c278-4da1-b9a8-0acd2941b0c7Show excerpt
worker_counts = [5, 10, 20] for batch_size in batch_sizes: for worker_count in worker_counts: start_time = time.time() reformulated_queries = handle_queries(test_queries[:batch_size], max_workers=worker_count) e…
See also
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