## 4 Discussion  
### Key Points  
Regularized Discriminant Analysis (RDA) appropriated 77.69% of scalar processor cycles, magnifying arithmetic stress born from covariance inversion routines intrinsic to discriminant calculation. MultiSURF, by contrast, required merely 17.08% yet generated approximately \(1.77\times 10^{15}\) floating-point operations. This antithetical conjunction reiterates that cache traffic rather than the raw multiply-accumulate rate governs observable utilization, a phenomenon encapsulated by Stiction Theory, wherein memory stalls tether execution irrespective of nominal throughput capacity. XGBoost situated upon scalar cores achieved the largest arithmetic volume, \(2.09\times 10^{15}\) FLOPS, whereas processor duty plateaued near 23.53%. Histogram construction proceeds through burst-style kernels separated by intervals of memory latency; thus, sustained saturation never materialized. Neural Net ADA compiled for the same host delivered the smallest counted workload, \(1.8\times 10^{7}\) FLOPS, because its topology shelters three compact dense layers. Nevertheless, utilization reached nearly half of the available cycles, since diminutive matrices resist efficient vectorization. LSTM variants occupied an intermediary niche. Hidden-state reuse curtailed cache misses, while sequence gating inflated operation count relative to simple perceptrons; consequently, FLOPS hovered close to \(6.6\times 10^{7}\) on graphics devices, utilization approximated fourteen per cent, indicating equilibrated interplay between compute-bound segments and latency-bound segments. Total system memory remained fixed at 62.66 GB by virtue of constant hardware, yet free capacity oscillated. nn_ada_cpu preserved 59.15 GB unallocated, a testament to its austere footprint. CUDA-based CNN-VGG16-TL claimed 1.28 GB device VRAM, marking the summit within the cohort. Corresponding percentage memory usage confirmed these trends: LSTM configurations stabilized near 3.10%, XGBoost peaked at 4.59%, whereas MultiSURF registered 2.69%, signaling parsimonious buffer occupation throughout neighborhood scoring. FLOPS stratification placed RDA at \(1.95\times 10^{15}\), apex among scalar algorithms. CNN-VGG16-TL, running on graphics hardware, resided mid-table with \(9.6\times 10^{12}\), reflecting convolution reuse across feature maps. GRU instantiated on both execution venues displayed nearly identical utilization together with operation count despite hardware disparity; the gating structure mirrors LSTM, albeit with fewer parameters; hence, convergence occurs towards a similar runtime profile. Such consonance confounds naive mapping between device specification and resource demand, demonstrating that kernel scheduling strategy dictates realized workload. Divergent motifs permeate further metrics--processor duty, memory load, and emission estimate. Algorithms maximizing statistical expressiveness frequently maintain long residency within execution units; selection procedures and dimensionality reducers, conversely, manifest brief spikes yet survive with lighter thermal signatures. Carbon audits confirm this dichotomy. Restricted Boltzmann Machine running upon scalar cores emitted the loftiest carbon index, attributable to near-continuous duty despite modest arithmetic density. Graphics pipelines yielded inferior duty readings, although instruction tallies remained voluminous; power expenditure arrived in punctuated bursts, reducing cumulative emission. RDA, together with XGBoost under scalar governance, delivered elevated carbon figures, mirroring their penchant for protracted socket engagement. Conversely, Perceptron GPU as well as Neural Net ADA GPU recorded minimal discharge because kernels completed expeditiously, enabling throttle circuitry to revert into a quiescent state. Comparison between hosts reveals alternative strategies for performance-energy equilibrium. Graphics pathways shift effort toward massive parallel shaders, close epochs swiftly, produce heterogeneous FLOPS yet conserve battery budget.