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tlaplus-performance

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Atualizado15 de junho de 2026 às 22:10

Work with large-scale / real-world TLA+ models and TLC performance measurement - the general/performance case studies (Paxos, Bookkeeper, SwarmKit, ...), the measure.tla/measure.sh harness, the perf.yml regression job, the JMH benchmark target, and state-explosion tuning. Use when scaling TLC, studying perf case studies, or running performance measurements. For routine checking see tlaplus-model-checking.

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Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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Fonte

jackfoxy

jackfoxy/foxy-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

Explorador de arquivos

4 arquivos

SKILL.md

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name	tlaplus-performance
description	Work with large-scale / real-world TLA+ models and TLC performance measurement - the general/performance case studies (Paxos, Bookkeeper, SwarmKit, ...), the measure.tla/measure.sh harness, the perf.yml regression job, the JMH benchmark target, and state-explosion tuning. Use when scaling TLC, studying perf case studies, or running performance measurements. For routine checking see tlaplus-model-checking.
user-invocable	true
disable-model-invocation	false

TLC Performance & Scaling

Covers general/performance/ and perf tooling. Routine checking + flags: [[tlaplus-model-checking]]; OOM/explosion diagnosis: [[tlaplus-debugging]].

Run a measurement (validated harness)

cd general/performance
# orchestrated run -> out_run-stats.csv (what perf CI runs):
java -jar ../../tlatools/org.lamport.tlatools/dist/tla2tools.jar -config measure.tla measure.tla
# parameterized model with explicit tuning knobs (default spec Grid5k):
./measure.sh 04 01 10 04   # K L N C [WORKERS] [HEAP] [DIRECT]; defaults nproc/8G/8g

The case-study corpus (Paxos, Bookkeeper, SwarmKit, Ghostferry, …), the harness files, the perf CI job, the JMH target, and the full tuning table are in references/case-studies-and-tuning.md.

JMH micro-benchmarks

ant -f customBuild.xml benchmark        # -> target/benchmarks.jar
java -jar target/benchmarks.jar

For hot-path Java (value enumeration, fingerprinting), not whole-spec timing.

Tuning at a glance

-workers (throughput), -Xmx/-fpmem + OffHeap FPSet (capacity), -fpbits (collisions vs memory), CONSTRAINT/smaller constants (shrink space), SYMMETRY (safety only — unsound with liveness), -simulate/-dfid (sampling/deep). Methodology: snapshot a real spec → choose constants/constraints → measure → tune → compare CSV vs baseline. Details: the reference file.

Validation

Reproduce one case study's measurement run, capture out_run-stats.csv, and explain how a chosen knob (workers/symmetry/constraint) changed the state count or runtime.

References (local)

references/case-studies-and-tuning.md — corpus, harness, perf CI, JMH, tuning table
general/performance/ — case-study dirs (README.md, LINK.md), measure.{tla,sh}, Grid5k.tla, stats.{R,tla}
.github/workflows/perf.yml, tlatools/org.lamport.tlatools/customBuild.xml (benchmark), test-benchmark/

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name	tlaplus-performance
description	Work with large-scale / real-world TLA+ models and TLC performance measurement - the general/performance case studies (Paxos, Bookkeeper, SwarmKit, ...), the measure.tla/measure.sh harness, the perf.yml regression job, the JMH benchmark target, and state-explosion tuning. Use when scaling TLC, studying perf case studies, or running performance measurements. For routine checking see tlaplus-model-checking.
user-invocable	true
disable-model-invocation	false

TLC Performance & Scaling

Covers general/performance/ and perf tooling. Routine checking + flags: [[tlaplus-model-checking]]; OOM/explosion diagnosis: [[tlaplus-debugging]].

Run a measurement (validated harness)

cd general/performance
# orchestrated run -> out_run-stats.csv (what perf CI runs):
java -jar ../../tlatools/org.lamport.tlatools/dist/tla2tools.jar -config measure.tla measure.tla
# parameterized model with explicit tuning knobs (default spec Grid5k):
./measure.sh 04 01 10 04   # K L N C [WORKERS] [HEAP] [DIRECT]; defaults nproc/8G/8g

The case-study corpus (Paxos, Bookkeeper, SwarmKit, Ghostferry, …), the harness files, the perf CI job, the JMH target, and the full tuning table are in references/case-studies-and-tuning.md.

JMH micro-benchmarks

ant -f customBuild.xml benchmark        # -> target/benchmarks.jar
java -jar target/benchmarks.jar

For hot-path Java (value enumeration, fingerprinting), not whole-spec timing.

Tuning at a glance

Validation

Reproduce one case study's measurement run, capture out_run-stats.csv, and explain how a chosen knob (workers/symmetry/constraint) changed the state count or runtime.

References (local)

references/case-studies-and-tuning.md — corpus, harness, perf CI, JMH, tuning table
general/performance/ — case-study dirs (README.md, LINK.md), measure.{tla,sh}, Grid5k.tla, stats.{R,tla}
.github/workflows/perf.yml, tlatools/org.lamport.tlatools/customBuild.xml (benchmark), test-benchmark/