SPEC CPU2026: Characterization, Representativeness, and Cross-Suite Comparison

Specialized accelerators dominate AI workloads, but CPUs remain critical for orchestrating accelerators and running daily services. CPU performance therefore shapes end-to-end system efficiency, making benchmarks reflect modern workloads and bottlenecks. Yet it remains unclear whether the newest general-purpose CPU benchmark suite changes the architectural conclusions drawn from prior SPEC CPU generations. We present the first comprehensive characterization of SPEC CPU2026 across nine recent Intel, AMD, Ampere, and Nvidia platforms. Compared with SPEC CPU2017, SPEC CPU2026 increases instruction volume and memory footprint and shifts pressure toward emerging bottlenecks, especially instruction-cache stress. These shifts raise two practical questions: how much of the new suite is needed to preserve behavioral coverage, and how should that coverage be interpreted relative to modern domain-specific suites? Using clustering-based representativeness analysis, we identify compact subsets of 4-5 workloads per group that preserve 96.4-99.9% of full-suite behavior. We then compare SPEC CPU2026 against SPEC CPU2017, DCPerf, and MLPerf using cross-suite microarchitectural metrics. SPEC CPU2026 remains a complementary general-purpose suite: it moves closer to datacenter-like frontend pressure than prior SPEC CPU generations, while remaining less vector-intensive than MLPerf and less frontend-extreme than DCPerf. Finally, case studies on page sizes and allocators, prefetching, compiler optimizations, ISA sensitivity, many-core scaling, and rolling round-robin proxy workloads show that SPEC CPU2026 supports architectural studies beyond aggregate scores. Overall, SPEC CPU2026 updates the standardized general-purpose CPU baseline for the next decade of architecture evaluation.