Energy-Efficient SIMD-Accelerated Expression Parsing: A Cross-Architectural Microarchitectural Analysis in Managed Environments

Abstract

Mathematical expression evaluation is a critical component in high-performance software systems. Traditional state-dependent algorithms resist Single Instruction, Multiple Data vectorization, while managed runtime environments introduce Garbage Collection overhead. This paper presents a hybrid parsing pipeline that isolates sequential state operations from parallelizable tasks, combining a SIMD-accelerated lexical analyzer with a vector evaluator that allocates no managed memory in its inner loop. Against established .NET evaluators the pipeline leads only below a few hundred evaluations of a given expression (approximately 230 on AMD and 310 on Intel), above which compiling once to a delegate dominates, and vectorized tokenization inverts beyond roughly thirty thousand characters of input. AVX-2 (Vector256) instructions reduce the isolated evaluation of a seven-operand reference expression to 69.30 ns with zero bytes allocated, while the complete pipeline allocates 1216 bytes and remains subject to generational collection. Under a fixed duty cycle holding delivered work constant, active-state residency falls from 69.1% to 51.6% and package energy falls by 15.9%, a measured effect rather than an inferred one. Benchmark-synchronized power instrumentation shows that the energy cost of wider vectors on AMD Zen 4 is expressed in time rather than in power. On both platforms 512-bit packed operations essentially retain the latency of their 256-bit counterparts but consume twice the issue slots. In the evaluation workload they take longer. Where lanes are fully occupied and operations independent, the ordering reverses and the 512-bit path draws less power and consumes 23.8% less energy per element. Power and energy were measured on the AMD platform only. The Intel platform supports the latency and allocation comparison.