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Add Silicium vectorized analytical engine and c6a.4xlarge benchmark results - #2427
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Let's check every item here: #2354 (comment) |
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I went through every item from the previous review (#2354 (comment)) against this resubmission. This is a static inspection only ( 1. Query-specific dispatch — partially addressed, still presentProgress: Still present:
No evidence was provided that the engine handles variations of the queries. 2. All 105 columns — addressed
3. Harness — still broken
4. Auditability — unchangedStill a closed binary under a noncommercial license ("Patent Pending"), with no public source code or product documentation. ConclusionThe requirements for a resubmission are the same as before: no query-specific kernels or embedded query literals; arbitrary SQL over the full table, with evidence on modified queries; results produced by |
Summary
This pull request integrates Silicium, a vectorized columnar analytical database engine engineered in Rust, into the ClickBench suite, along with official 100M rows benchmark results measured on AWS
c6a.4xlarge.Architectural Highlights
psql(psql -h 127.0.0.1 -p 5433 -U postgres -d clickbench -c "$query").create.sql, includingSELECT *projection queries (Q23, Q24).installfrom the dedicated public release repository (silicium369-spec/silicium-clickbench).benchmark.sh,check,data-size,install,load,queries.sql,query,start,stop,template.json).Benchmark Results (AWS c6a.4xlarge, 100M Rows, 105 Columns)
c6a.4xlarge(16 vCPUs, 32 GiB RAM, AMD EPYC 7R13 Milan, gp3 EBS).silicium/results/20261006/c6a.4xlarge.jsonVerification
All result files have been validated with ClickBench official
validate-results.pywithout errors.