File-search service is a valuable facility to accelerate many analytics applications, because it can drastically reduce the scale of the input data. The main challenge facing the design of large-scale and accurate file-search services is how to support real-time indexing in an efficient and scalable way. To address this challenge, we propose a distributed file-search service, called Propeller, which utilizes a special file-access pattern, called access-causality, to partition file-indices in order to expose substantial access locality and parallelism to accelerate the file-indexing process. The extensive evaluations of Propeller show that it is real-time in file-indexing operations, accurate in file-search results, and scalable in large datasets. It achieves significantly better file-indexing and file-search performance (up to 250x) than a centralized solution (MySQL) and much higher accuracy and substantially lower query latency (up to 22x than a state-of-the-art desktop search engine (Spotlight).

In order to maintain the transactional semantics, Transactional Memory (TM) must guarantee isolated read and write operations in each transaction, meaning that it must spend a non-negligible and potentially significant amount of time on keeping track of the transactional modifications in its undo or redo log and switching to the proper version at the end of each transaction. Existing TMs failed to minimize the overheads incurred by these operations that are poised to impose more significant TM overheads in current and future many-core CMPs. A direct consequence of this is that extra and different data movements are needed to manage these modifications depending on commit or abort. To address this problem, we propose a novel Single-Update Version-management (SUV) scheme to redirect each transactional store operation to another memory address, track the mapping information between the original and redirected addresses, and switch to the proper version of data upon the transaction's commit or abort. There is only one data update (movement) in our SUV regardless of commit or abort, thus significantly reducing the TM overheads while allowing it to exploit more thread parallelism. We use SUV to replace version-management schemes in some existing hardware TMs to assess SUV's performance advantages. Our extensive execution-driven experiments show that SUV-TM consistently outperforms the state-of-the-art HTM schemes Log TM-SE, FasTM and DynTM under the STAMP benchmark suite. Moreover, we use CACTI to estimate the hardware overheads of SUV and find it is feasible in hardware implementation.

Recent advances in text-to-video (T2V) generation with diffusion models have garnered significant attention. How ever, they typically perform well in scenes with a single object and motion, struggling in compositional scenarios with multiple objects and distinct motions to accurately reflect the semantic content of text prompts. To address these challenges, we propose StarVid, a plug-and-play, training-free method that improves semantic alignment between multiple subjects, their motions, and text prompts in T2V models. StarVid first employs large language models (LLMs) to perform two-stage motion trajectory planning based on the input prompt, providing object-level spatial priors that guide the generation process. These priors are then used to impose a spatial-aware loss that refines cross-attention (CA) maps, encouraging attention to be focused on distinct regions corresponding to different subjects. Furthermore, a syntax guided contrastive constraint is introduced to explicitly enhance the association between verbs and their corresponding nouns in the CA maps, thereby improving motion-subject binding. Both qualitative and quantitative evaluations demonstrate that the proposed framework significantly outperforms baseline methods, delivering videos of higher quality with improved semantic consistency.