HPC on a budget: on the CPU's impact on dense linear algebra computation with GPUs - Assis, Lucas Barros de; Schnorr, Lucas Mello (Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD); 2025: Anais do XXVI Simpósio em Sistemas Computacionais de Alto DesempenhoDO - 10.5753/sscad.2025.16739, 2025)
While GPU-equipped nodes dominate compute-intensive operations in high-performance computing, the role of host processors in overall system performance remains underexplored, particularly for hardware procurement decisions in resource-constrained environments. This study investigates the influence of the CPU microarchitecture on application performance using an identical modern GPU. We compare two systems with the same NVIDIA RTX4090 GPU but different CPUs: a 2016 Intel Xeon E5-2620v4 and a 2023 Intel Core i9-14900KF. We assess CPU impact on compute-bound workloads using dense Cholesky and LU factorizations from the Chameleon library assisted by the StarPU runtime system. Our findings demonstrate that: (1) CPU influence is negligible with modern GPU accelerators, even if some operations lack GPU implementations; (2) CPU-handled operations are sufficiently small to attenuate performance differences between processor generations; and (3) modern GPUs perform effectively in legacy hardware with minimal penalties. These results suggest selective GPU upgrades offer cost-effective performance improvements without complete system overhauls, providing valuable insights for academic and research institution procurement strategies.