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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 131 |
| Published: August 2026 |
| Authors: Satish Chavali |
10.5120/ijca0d863fc2cc22
|
Satish Chavali . Transparent huge Pages in Linux: A Formal Analysis of Performance Benefits, Pathological Workloads, and Unresolved Failure Modes. International Journal of Computer Applications. 187, 131 (August 2026), 60-68. DOI=10.5120/ijca0d863fc2cc22
@article{ 10.5120/ijca0d863fc2cc22,
author = { Satish Chavali },
title = { Transparent huge Pages in Linux: A Formal Analysis of Performance Benefits, Pathological Workloads, and Unresolved Failure Modes },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 131 },
pages = { 60-68 },
doi = { 10.5120/ijca0d863fc2cc22 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Satish Chavali
%T Transparent huge Pages in Linux: A Formal Analysis of Performance Benefits, Pathological Workloads, and Unresolved Failure Modes%T
%J International Journal of Computer Applications
%V 187
%N 131
%P 60-68
%R 10.5120/ijca0d863fc2cc22
%I Foundation of Computer Science (FCS), NY, USA
Transparent Huge Pages (THP) is a Linux kernel mechanism that automatically promotes base 4 KiB pages into 2 MiB huge pages without application-level modification, with the stated objective of reducing translation lookaside buffer (TLB) miss rates and the attendant page-table walk overhead. While the theoretical benefit is well established for large, spatially contiguous memory workloads, the empirical record is considerably more ambiguous. This paper presents a formal mathematical treatment of THP's performance model — covering TLB coverage, effective memory access time, fragmentation cost, and compaction overhead — alongside a structured reporting of negative experimental results obtained across four representative workload classes: in-memory key-value stores, columnar database engines, real-time signal processing, and sparse scientific computing. The analysis demonstrates that THP promotion degrades throughput by 14.5% and increases P99 latency by 492% in random-access key-value workloads, introduces compaction-induced tail latency regressions of up to P99.9 +396% in mixed OLTP/OLAP database environments, causes a six-fold increase in deadline miss rate under hard real-time scheduling, and imposes 38.4% memory overhead without measurable performance return in sparse graph analytics. Three unresolved failure modes in the current kernel implementation are formally characterized — compaction-induced latency spikes, memory over-commitment amplification, and NUMA locality degradation — and a unified cost model is derived that consolidates each performance and fragmentation cost into a single workload-parameterized expression. The evidence indicates that the current THP default configuration (always) is inappropriate for a majority of production server workloads; this finding is presented as a formal negative result rather than an implementation caveat.