
Cross-architecture benchmarks implicitly assume equal software optimization—an assumption that rarely holds. We quantify this effect through vector database performance on IBM POWER and AMD EPYC. In same-generation comparison (POWER10 vs EPYC 7313P, both 7nm/2021), the x86 advantage is 27% in pgvector (neither platform SIMD-optimized) but 186% in MariaDB (mature AVX-512 paths, minimal POWER VSX investment). This 7x variation suggests that over 80% of the observed gap in optimized workloads may reflect software investment asymmetry rather than architectural limitations. Compiler experiments reinforce this hypothesis: IBM XL versus GCC yields up to 8x throughput difference on identical POWER hardware. Database engine selection produces 2.1–5.5x effects—exceeding typical hardware generational improvements. These findings suggest that cross-architecture comparisons may conflate hardware capability with optimization maturity. For POWER platforms, the path to competitive vector search performance likely runs through software investment rather than hardware replacement.
LLM, PostgreSQL, AI, MariaDB, ppc64, HPC, ppc64le, VectorDB, IBM Power, RAG
LLM, PostgreSQL, AI, MariaDB, ppc64, HPC, ppc64le, VectorDB, IBM Power, RAG
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
