Dontopedia

Multi-threading

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

Multi-threading has 9 facts recorded in Dontopedia across 4 references, with 2 live disagreements.

9 facts·6 predicates·4 sources·2 in dispute

Mostly:rdf:type(2), describes(2), is incomplete(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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hasSectionHas Section(1)

Other facts (8)

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.

8 facts
PredicateValueRef
Rdf:typeDocument Section[1]
Rdf:typeSection[3]
DescribesMulti Threading[3]
DescribesFaiss Omp Set Num Threads[3]
Is Incompletetrue[2]
Lacks Detailtrue[2]
Statusincomplete[4]
Expected Contentthread-configuration-guidance[4]

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.

typebeam/deee8e59-885e-45e2-98e2-b079298375cc
ex:DocumentSection
isIncompletebeam/2b210dd9-dd14-4daf-ba9f-ea7913237b0a
true
lacksDetailbeam/2b210dd9-dd14-4daf-ba9f-ea7913237b0a
true
typebeam/6496cb96-ccfe-4ec6-a519-16a7270f4904
ex:Section
labelbeam/6496cb96-ccfe-4ec6-a519-16a7270f4904
Multi-threading
describesbeam/6496cb96-ccfe-4ec6-a519-16a7270f4904
ex:multi-threading
describesbeam/6496cb96-ccfe-4ec6-a519-16a7270f4904
ex:faiss-omp-set-num-threads
statusbeam/8c21f541-c703-4998-aae0-19638ef54326
incomplete
expectedContentbeam/8c21f541-c703-4998-aae0-19638ef54326
thread-configuration-guidance

References (4)

4 references
  1. ctx:claims/beam/deee8e59-885e-45e2-98e2-b079298375cc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/deee8e59-885e-45e2-98e2-b079298375cc
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      - `IndexIVFPQ` is used instead of `IndexIVFFlat` to provide faster approximate nearest neighbor search. 2. **Tuning Parameters**: - `nlist`: Number of clusters. A higher value can improve accuracy but also increases memory usage.
  2. ctx:claims/beam/2b210dd9-dd14-4daf-ba9f-ea7913237b0a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2b210dd9-dd14-4daf-ba9f-ea7913237b0a
      Show excerpt
      Here's an optimized version of your code using `IndexIVFFlat` and enabling multi-threading: ```python import faiss import numpy as np # Assume we have a dataset of 100,000 vectors vectors = np.random.rand(100000, 128).astype('float32') #
  3. ctx:claims/beam/6496cb96-ccfe-4ec6-a519-16a7270f4904
    • full textbeam-chunk
      text/plain1 KBdoc:beam/6496cb96-ccfe-4ec6-a519-16a7270f4904
      Show excerpt
      - `nlist`: Number of clusters. A higher value can improve accuracy but also increases memory usage. - `M`: Number of sub-quantizers. A higher value can improve accuracy but also increases memory usage. - `nbits`: Number of bits per
  4. ctx:claims/beam/8c21f541-c703-4998-aae0-19638ef54326
    • full textbeam-chunk
      text/plain1 KBdoc:beam/8c21f541-c703-4998-aae0-19638ef54326
      Show excerpt
      faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create an IVFPQ index nlist = 100 # Number of clusters M = 8 # Number of sub-quantizers nbits = 8 # Number of bits

See also

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