Parallel and High-Throughput Topic Modeling

Running sbert at full speed without changing a single result

2026-08-22

A topic model in sbert is built in three stages, and each scales differently:

1. Encode — text becomes Sentence-BERT vectors, through the ONNX model.
2. Tokenize — text becomes terms, for the topic labels and coherence.
3. Cluster — vectors become topics, through deterministic k-means.

Encoding dominates everything else. On the bundled covid abstracts it costs 16.7 ms per document against 0.4 ms for the entire clustering, term-weighting and labelling stage — roughly 97% of the total. Within a batch, ONNX inference is 88% and everything R does is the remaining 12%. So the question is never “how do I make the topic model faster”; it is “how do I encode less”.

There are three answers, in descending order of payoff: don’t encode the same text twice (across sessions, with cache; within one, with topic_corpus()), don’t encode padding (sort_by_length), and spread the work across cores (threads, cores). The first is worth two orders of magnitude; the others are worth tens of percent.

Nothing here changes what you get. cache, cores and threads are exactly reproducible; the three knobs that are not are flagged where they appear.

Do the expensive work once

Encoding is almost always the most expensive stage, and tokenization is the next after it once the corpus is large. Both depend only on the documents, so computing them once and reusing them is the single biggest lever when you fit more than one model — a topic-count sweep, a comparison of term settings, or any loop over n_topics.

Pass precomputed embeddings. Encode once, then hand the matrix to every fit:

model <- load_model("all-MiniLM-L6-v2", threads = 8)
embeddings <- encode(corpus$text, model, batch_size = 64)

# topics()/select_topics() now skip encoding entirely
m <- topics(corpus$text, n_topics = 8, embeddings = embeddings)

Prepare a corpus once, fit many. topic_corpus() goes further: it caches both the embeddings and the tokenization, so a sweep or loop repeats neither.

prepared <- topic_corpus(corpus$text, embeddings = embeddings)

sweep <- select_topics(prepared, n_topics = c(6, 8, 10, 12, 15))
model8 <- fitted(sweep, n_topics = 8)   # chosen without refitting

select_topics() already shares one prepared corpus across all candidates internally, so even select_topics(corpus$text, ...) no longer re-tokenizes per candidate. Passing a prepared topic_corpus on top lets you reuse it across several sweeps or manual loops.

Reuse across sessions: encode(cache =)

Everything above reuses work within one R session. cache reuses it across sessions, across scripts, and — crucially — across edits to the corpus.

Give encode() a file path and each document is stored under a digest of its own text plus the model configuration. On the next call, only documents with no matching entry are encoded:

store <- "embeddings/covid.rds"

embeddings <- encode(covid$Abstract, model, cache = store)   # cold: full price
embeddings <- encode(covid$Abstract, model, cache = store)   # warm: no encoding

Because the key is per document rather than per corpus, an edited corpus reuses everything that did not change. Correct a few abstracts, append this year’s records, reorder the rows, or take a subset — only genuinely new text is encoded:

corrected <- fix_a_few_records(covid$Abstract)

# encodes only the records whose text actually changed
embeddings <- encode(corrected, model, cache = store)

Measured on the bundled 3,847-abstract covid corpus:

Call	Time	Versus a full encode
Cold — builds the cache	36.59 s	—
Warm — nothing changed	0.07 s	515×
After editing 38 documents	0.41 s	89×
Reordered or subset corpus	no encoding at all	—

Repeated documents are also encoded only once per call and expanded by position: 295 of those 3,847 abstracts are duplicates of another, so a cold run already skips 7.7% of the work.

What is in the key. The document text as the tokenizer sees it (so any prefix is included), plus the model id, revision, dimension, maximum length, pooling method, and normalize. Change any of them and you get a miss, not a stale hit — a second model never reuses the first model’s rows.

Stable to first computation, not to every batching. batch_size and sort_by_length are deliberately not in the key, because they move embeddings only at the ~1e-7 level and including them would destroy reuse whenever you changed a batch size. Cached rows therefore reproduce whatever computed them first, rather than being bit-identical to a fresh encode under different batching. A cache that cannot be read, or that was written for a different embedding dimension, is discarded with a warning and rebuilt rather than trusted.

The two parallel knobs

threads — parallel encoding

load_model(threads = N) sets the number of ONNX inference threads, and every encode() call using that model runs multithreaded. Use "auto" to get the machine’s performance-core count:

model <- load_model("all-MiniLM-L6-v2", threads = "auto")
embeddings <- encode(text, model)

Expect a modest gain, and do not simply raise it to the core count. Inference scales with performance cores, not logical ones. Median of three repetitions at 200, 600 and 1,500 documents on an Apple M4 (4 performance + 6 efficiency cores, so detectCores() reports 10):

Threads	Speed-up
1	1.00×
2	1.22×
4	1.29×	best — matches the performance-core count
6	1.22×	efficiency cores add contention
8	1.18×	slower still

Past four threads the wall clock gets worse while CPU time climbs steeply. threads = "auto" picks that knee for you.

Threads never change results. Output was identical() at every thread count across all 45 measured runs. Unlike batch_size, this knob is numerically free.

Benchmark threaded code with repetition. Run-to-run spread reached 26% at the smallest corpus size, against under 3% single-threaded. A single timing run is not evidence.

cores — parallel tokenization, segmentation, and sweeps

cores is available on topics(), select_topics(), topic_corpus(), coherence(), segment(), and topic_gamma(). Above one it forks worker processes to:

• tokenize the corpus in parallel (documents split across workers),
• in select_topics(), fit the independent candidates in parallel, and
• in segment() / topic_gamma(), split documents in parallel.

# tokenization runs in parallel; the corpus is prepared once
prepared <- topic_corpus(corpus$text, embeddings = embeddings, cores = 8)

# candidates are fitted across cores, tokenization already cached
sweep <- select_topics(prepared, n_topics = c(6, 8, 10, 12, 15), cores = 8)

Platform notes. cores uses parallel::mclapply, which forks — available on Linux and macOS, and falls back to serial on Windows. Forking has overhead, so parallel work only engages for corpora of 2,000 documents or more; smaller inputs run serially by design. A good default is cores = parallel::detectCores() - 1.

Sentence-level topic_gamma()

topic_gamma() reports each document’s mixture of topics by splitting it into sentences (or clauses), embedding every segment, and pooling the assignments. It has three cost stages, each with its own lever:

1. Segmenting the documents — parallelize with cores.
2. Encoding every segment — the dominant cost; group by length with sort_by_length, multithread with load_model(threads =), and optionally deduplicate (below).
3. Pooling into per-document mixtures — already negligible.

Measured at clause level on 300 abstracts, the split is stark: encoding 91.2%, segmenting 8.5%, pooling 0.2%. topic_gamma() is expensive because of fan-out — those 300 documents become 3,621 clauses, twelve units to encode for every one that topics() handles.

model <- load_model("all-MiniLM-L6-v2", threads = "auto")
gamma <- topic_gamma(
  topic_model, documents,
  model = model,
  level = "sentence",
  cores = 8               # split documents across cores
)

sort_by_length — the single biggest lever here

Every sequence in a batch is padded to the longest member, so a batch costs the model its maximum length, not its mean. Segments vary enormously in length — a three-word clause batched beside a 119-token one pays for 119 tokens. Grouping similar lengths together removes most of that waste:

Ordering	Token work that is pure padding
Input order, clause level	60.1%
Sorted by length	1.6%

gamma <- topic_gamma(
  topic_model, documents,
  model = model,
  level = "clause",
  sort_by_length = TRUE     # 1.89x measured at clause level
)

Whole documents gain far less (1.08×) because abstracts are already of similar length. This is a segment-level optimization. Row order is always restored, so the result lines up with your input regardless.

Off by default, same ~1e-7 reason. Regrouping changes batch composition, so embeddings move at the 1e-7 level — the same trade-off as dedupe_segments. On the bundled covid corpus that drift changed no gamma value and no document’s dominant topic, but it is opt-in rather than assumed.

Segment once, reuse everywhere

Encoding is the dominant stage, so the largest — and fully reproducible — win is to not encode twice. Segment the corpus once, encode those segments, then hand both the segments and their embeddings to topic_gamma(): it uses your segmentation as-is and skips encoding entirely, dropping to just pooling.

# segment and encode once, deterministically
segments <- segment(documents, level = "sentence", cores = 8)
segment_embeddings <- encode(segments$text, model)

# pass the segment() frame itself, not raw documents — the rows already align
gamma <- topic_gamma(topic_model, segments, embeddings = segment_embeddings)

topic_gamma() accepts either raw documents (which it segments internally) or the data frame segment() returns. Passing the frame means there is no second, internal segmentation to disagree with your embeddings — the rows line up by construction. That matters the moment you segment with anything other than the defaults, for example capping segment length so nothing is truncated:

segments <- segment(documents, level = "sentence", max_tokens = 256, model = model)
gamma <- topic_gamma(topic_model, segments, embeddings = encode(segments$text, model))

One segmentation, one encode, reused across every sentence-level step — this topic_gamma(), sentence-level representatives(), or a blend() that carries document context into each sentence.

Deduplicating repeated segments

A long corpus repeats segments — boilerplate sentences, short stock clauses — and dedupe_segments = TRUE encodes each distinct segment once, expanding the result by position. How much this saves depends entirely on how repetitive your corpus is, so measure before reaching for it:

segments <- segment(documents, level = "clause")
1 - length(unique(segments$text)) / nrow(segments)   # duplicate fraction

The bundled covid abstracts are only 15% duplicated at clause level and 8% at sentence level, so deduplication adds about 3% there on top of sort_by_length. A corpus of templated or boilerplate-heavy text will do far better.

gamma <- topic_gamma(
  topic_model, documents,
  model = model,
  level = "sentence",
  cores = 8,
  dedupe_segments = TRUE
)

One-time shift, not free reproducibility. Deduplication is off by default because encoding a smaller batched set perturbs the embeddings at the ~1e-7 level, which can flip a rare borderline segment’s assignment — the same trade-off as changing batch_size. With the static potion-base-8M model, whose encoding has no batch effect, the result is identical either way.

The maximum-speed recipe

Putting it together for the common case — one corpus, several models:

library(sbert)
install_runtime()

# 1. Multithreaded encoding, once, cached to disk for every future session
model <- load_model("all-MiniLM-L6-v2", threads = "auto")
embeddings <- encode(corpus$text, model, cache = "embeddings/corpus.rds")

# 2. Prepare the corpus once (encode + tokenize cached), tokenizing in parallel
prepared <- topic_corpus(corpus$text, embeddings = embeddings, cores = 8)

# 3. Sweep across cores, then pick a model with no refit
sweep <- select_topics(prepared, n_topics = c(6, 8, 10, 12, 15), cores = 8)
model8 <- fitted(sweep, n_topics = 8)

Re-run that script tomorrow, or after editing a handful of records, and step 1 costs almost nothing.

The static model

For a corpus so large that encoding dominates, the static potion-base-8M model embeds with no transformer and no ONNX Runtime at all — measured 45× faster than all-MiniLM-L6-v2:

fast <- load_model("potion-base-8M")   # 31 MB, Model2Vec token lookup
embeddings <- encode(corpus$text, fast)

Not simply “faster but worse” — test it on your corpus. On the covid abstracts it scored better than all-MiniLM-L6-v2 on NPMI, UMass and topic diversity at 4 of 5 topic counts. On the bundled feedback_translations it lost on 2 of 5, 1 of 5 and 0 of 5 respectively. The advantage is corpus-specific and does not generalize, which is exactly why it is not the default. Sweep both with select_topics() and compare coherence() before committing.

What to expect

Measured on a 10-core Apple Silicon machine (macOS, Accelerate BLAS, R 4.5) with precomputed embeddings. Wall-clock carries some machine-load variance; the speed-ups grow with the number of models fitted and with document length.

Workload	Baseline	Serial	8 cores
Sweep, 5 counts, 15k short docs	163.5 s	12.2 s (13×)	6.9 s (24×)
Sweep, 3 counts, 5k long docs (~8k chars)	321.1 s	56.0 s (5.7×)	10.3 s (31×)
Tokenization, 15k short docs	—	11.7 s	3.1 s (3.8×)
Tokenization, 5k long docs	—	52.5 s	8.6 s (6.1×)

The “baseline” column is the pre-reuse path that re-encoded and re-tokenized on every model. Two patterns stand out: longer documents make parallel tokenization pay off more (6.1× vs 3.8×), and the corpus-reuse win scales with the number of models — roughly 3.5× at five models and 4× at ten.

Memory improves independently of document length, because clustering works on the embeddings, not the raw text: at 40 topics, peak memory for a 40,000-document run dropped from about 2,195 MB to 1,053 MB.

Reproducibility

cores and threads change speed, never results. Tokenization is split by document and recombined in order, and sweep candidates are independent and deterministic, so the output is identical for any core or thread count. There is no random seed anywhere in the topic pipeline.

The proof runs offline — the same model, fitted single-threaded and across four cores, is byte-identical:

set.seed(1)
n <- 2500L
text <- paste(
  "document", seq_len(n),
  sample(c("alpha beta gamma", "delta epsilon zeta", "eta theta iota"), n, TRUE)
)
# clustered stand-in embeddings, so no model download is needed
centers <- matrix(rnorm(8L * 64L), 8L, 64L)
embeddings <- centers[sample(8L, n, replace = TRUE), ] +
  matrix(rnorm(n * 64L, sd = 0.6), n, 64L)

serial   <- topics(text, n_topics = 6, embeddings = embeddings, cores = 1)
parallel <- topics(text, n_topics = 6, embeddings = embeddings, cores = 4)

identical(
  serial[c("documents", "topics", "terms", "representatives", "centers")],
  parallel[c("documents", "topics", "terms", "representatives", "centers")]
)
#> [1] TRUE

Three knobs shift results at the ~1e-7 level: batch_size, sort_by_length, and dedupe_segments. All three work by changing which documents share a batch, and batch composition perturbs embeddings slightly. Pick your settings and keep them — results are reproducible for a fixed combination, just not necessarily across different ones. cores, threads and cache are all free of this.

Do not bother tuning batch_size for speed. Across 640 and 3,847 documents, sizes from 8 to 128 differed by less than the run-to-run noise, and an apparent gain at one corpus size did not survive at another.

Checklist

• Running the same corpus more than once, ever? Add encode(cache = "path.rds"). Re-runs cost essentially nothing, and edited corpora re-encode only what changed. This is the largest single win available.
• Fitting many models on one corpus in a session? Use topic_corpus() (or precompute embeddings) so encoding and tokenization happen once.
• Encoding is the bottleneck? Set load_model(threads = "auto") — but expect ~1.3×, not linear scaling. For a much larger jump, evaluate the potion-base-8M static model on your corpus.
• Using sentence- or clause-level topic_gamma()? Add sort_by_length = TRUE. Nearly 2× at clause level, because 60% of the token work is otherwise padding.
• On Linux or macOS with a big corpus (≥ 2,000 docs)? Add cores = detectCores() - 1 to topic_corpus() / select_topics().
• On Windows, or a small corpus? cores safely no-ops to serial; rely on cache, reuse and threads instead.
• Need exact reproducibility? cores, threads and cache are safe at any value; fix batch_size, sort_by_length and dedupe_segments once and leave them.
• Benchmarking any of this yourself? Repeat every measurement. Threaded timings vary by up to 26% run to run, and a single sample will mislead you.