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chore: generalize compact interning and fetching (#1237)
Interning/fetching environments and provenances share the same logic, so we can unify their implementations to improve code reusability.
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Benchmarks
Table of Contents
Overview
This benchmark report shows the Fibonacci GPU benchmark.
NVIDIA L4
Intel(R) Xeon(R) CPU @ 2.20GHz
32 vCPUs
125 GB RAM
Workflow run: https://github.com/lurk-lab/lurk-rs/actions/runs/8687905562
Benchmark Results
LEM Fibonacci Prove - rc = 100
ref=467bf18e6b8b094eba924b89b229d6604c148486
ref=bab22ba2eb600c8f83c9d30b439084537a0f3b53
num-100
1.48 s
(✅ 1.00x)1.48 s
(✅ 1.00x slower)num-200
2.80 s
(✅ 1.00x)2.81 s
(✅ 1.00x slower)LEM Fibonacci Prove - rc = 600
ref=467bf18e6b8b094eba924b89b229d6604c148486
ref=bab22ba2eb600c8f83c9d30b439084537a0f3b53
num-100
1.87 s
(✅ 1.00x)1.86 s
(✅ 1.01x faster)num-200
3.08 s
(✅ 1.00x)3.05 s
(✅ 1.01x faster)Made with criterion-table