Standard compiler optimization levels, such as -O3 , which provides a fixed optimization strategy for all programs, often fail to deliver the optimal performance. Compiler auto-tuning techniques can deliver substantial speedups, but existing methods present a difficult tradeoff. While dynamic iterative approaches are effective, their requirement for repeated compilation and execution incurs high overhead, which limits their practicality. Conversely, static prediction methods offer a low-overhead alternative. However, they face a vast search space and must comprehensively learn both option-option interactions and option-program feature relationships. To overcome the challenge, we propose PredComp , a novel static framework that leverages the divide and conquer paradigm to predict desired option sets. PredComp decomposes the search space by partitioning options into distinct subspaces based on their relationships, making the prediction problem tractable. It first predicts promising option sub-sets within each subspace, focusing only on intra-subspace option interactions and their preferred program features. Then, it adopts a combination model that aggregates these top-ranked sub-sets, prioritizes inter-subspace option interactions and corresponding features to construct globally desired sets. Experiments on three widely used benchmark suites and one real-world application show that PredComp achieves average speedups of 1.1011× over -O3 with a single prediction. Notably, it achieves performance comparable to dynamic iterative methods while reducing tuning time from hours or days to seconds, thereby making static prediction a practical solution for large-scale and frequently evolving software.

Huge pages are effective in reducing address translation overhead under virtualization. However, huge pages can lead to the memory bloat problem, which manifests in two primary forms: hot bloat and usage bloat . Hot bloat occurs when accesses to a huge page are heavily skewed towards a small subset of base pages, leading the hypervisor to (mistakenly) classify the entire huge page as hot. Hot bloat undermines several critical virtualization techniques, including tiered memory and page sharing. Usage bloatrefers to the base pages within a huge page that has not yet been allocated, causing virtual machines (VMs) to demand excessive memory. Prior work addressing memory bloat either requires hardware modification or targets a specific scenario and is not applicable to a hypervisor. This article presents HugeScope , a lightweight, effective and generic system that addresses the memory bloat problem under virtualization based on commodity hardware. HugeScope includes an efficient and precise page tracking mechanism, leveraging the other level of indirect memory translation in the hypervisor. HugeScope provides a generic framework to support page splitting and coalescing policies, considering the memory pressure, as well as the recency, frequency, and skewness of page access. Moreover, HugeScope is general and modular. It can not only be easily applied to various scenarios concerning hot bloat , including tiered memory management ( HS-TMM ) and page sharing ( HS-Share ), but also seamlessly expose its capabilities to VMs to address the usage bloat problem ( HS-HP ). Evaluation shows that HugeScope incurs less than 4% overhead, by addressing hot bloat , HS-TMM improves performance by up to 61% over vTMM while HS-Share saves 41% more memory than Ingens while offering comparable performance, and By addressing usage bloat , HS-HP can eliminate excessive memory usage, and achieve performance improvements of up to 11% over HawkEye.