We propose PipeANN, an on-disk graph-based approximate nearest neighbor search (ANNS) system, which significantly bridges the latency gap with in-memory ones. We achieve this by aligning the best-first search algorithm with SSD characteristics, avoiding strict compute-I/O order across search steps. Experiments show that PipeANN has 1.14x-2.02x search latency compared to in-memory Vamana, and 35.0% of the latency of on-disk DiskANN in billion-scale datasets, without sacrificing search accuracy.
RDMA-enabled memory disaggregation has emerged as an attractive approach to reducing memory costs in modern data centers. While RDMA enables efficient remote read/write operations, it presents challenges in remote memory (de)allocation. Consequently, existing systems adopt coarse-grained allocations (in GBs), leading to memory waste. We introduce FineMem, an RDMA-connected remote memory management system that enables high-performance, fine-grained memory allocation. FineMem addresses latency and scalability challenges related to fine-grained allocations. It removes RDMA memory region (MR) registration costs from allocation paths through per-compute node MR preregistration, while ensuring remote memory isolation using RDMA memory windows and a trusted allocation service on each compute node. It employs a lock-free, one-sided RDMA-based protocol to allocate memory chunks (e.g., 4KB, 2MB) without involving the memory node's CPU and maintains metadata consistency during compute node failures via logging. We show that FineMem reduces remote memory allocation latency by as much as 95% compared to state-of-the-art remote memory management systems. It enables memory malloc systems, key-value stores systems, and swap systems running on FineMem to achieve low memory waste with minimal overhead.
Tiered memory systems often rely on access frequency ("hotness") to guide data placement. However, hot data is not always performance-critical, limiting the effectiveness of hotness-based policies. We introduce amortized offcore latency (AOL), a novel metric that precisely captures the true performance impact of memory accesses by accounting for memory access latency and memory-level parallelism (MLP). Leveraging AOL, we present two powerful tiering mechanisms: SOAR, a profile-guided allocation policy that places objects based on their performance contribution, and ALTO, a lightweight page migration regulation policy to eliminate unnecessary migrations. SOAR and ALTO outperform four state-of-the-art tiering designs across a diverse set of workloads by up to 12.4x, while underperforming in a few cases by no more than 3%.
Rendering service is an indispensable OS service on smart-device OSes like Android, iOS and OpenHarmony. However, the recent shift towards highly scalable display scenarios, such as foldable and multiple screens, has notably amplified the rendering workload, leading to low frame rates that degrade user experience. Yet, rendering services predominantly follow a sequential model, which is notoriously hard to parallelize due to the complex state dependency, drawing order dependency, and interface dependency. This paper observes that a significant portion of the rendering procedure is potentially parallelizable through proper state pre-untangling and drawing order post-preserving. To this end, this paper introduces Spars, a scalable parallelized OS rendering service inspired by the out-of-order execution with in-order commit in computer architecture. Spars revolutionizes the rendering procedure by initially generating self-contained rendering tasks through in-order preparation, executing such tasks in an out-of-order manner to maximize multi-core parallelism, and subsequently committing the tasks in-order to enforce drawing order dependencies. Evaluation results on state-of-the-art single-screen, dual-fold, and tri-fold smartphones (Mate 70, X5, XT) as well as one-chip-multiple-screen configurations show an average frame rate improvement of 1.76x-1.91x. Moreover, Spars is able to decrease the device power consumption by 3.0% or increase the budget of graphics primitives by 2.31x for more appealing visual effects with the same stable frame rate.
Memory- and type-safe languages promise to eliminate entire classes of systems vulnerabilities by construction. In practice, though, even clean-slate systems often need to incorporate libraries written in other languages with fewer safety guarantees. Because these interactions threaten the soundness of safe languages, they can reintroduce the exact vulnerabilities that safe languages prevent in the first place. This paper presents Omniglot: the first framework to efficiently uphold safety and soundness of Rust in the presence of unmodified and untrusted foreign libraries. Omniglot facilitates interactions with foreign code by integrating with a memory isolation primitive and validation infrastructure, and avoids expensive operations such as copying or serialization. We implement Omniglot for two systems: we use it to integrate kernel components in a highly-constrained embedded operating system kernel, as well as to interface with conventional Linux userspace libraries. Omniglot performs comparably to approaches that deliver weaker guarantees and significantly better than those with similar safety guarantees.
