Semi-dense feature matching methods have shown strong performance in challenging scenarios. However, the existing pipeline relies on a global search across the entire feature map to establish coarse matches, limiting further improvements in accuracy and efficiency. Motivated by this limitation, we propose a novel pipeline, CasP, which leverages cascaded correspondence priors for guidance. Specifically, the matching stage is decomposed into two progressive phases, bridged by a region-based selective cross-attention mechanism designed to enhance feature discriminability. In the second phase, one-to-one matches are determined by restricting the search range to the one-to-many prior areas identified in the first phase. Additionally, this pipeline benefits from incorporating high-level features, which helps reduce the computational costs of low-level feature extraction. The acceleration gains of CasP increase with higher resolution, and our lite model achieves a speedup of $\sim2.2\times$ at a resolution of 1152 compared to the most efficient method, ELoFTR. Furthermore, extensive experiments demonstrate its superiority in geometric estimation, particularly with impressive cross-domain generalization. These advantages highlight its potential for latency-sensitive and high-robustness applications, such as SLAM and UAV systems. Code is available at https://github.com/pq-chen/CasP.

To ensure compilers' quality, compiler testing has received more and more attention, and test-program generation is the core task. In recent years, some approaches have been proposed to explore test configurations for generating more effective test programs, but they either are restricted by historical bugs or suffer from the cost-effectiveness issue. Here, we propose a novel test-program generation approach (called MCS) to further improving the performance of compiler testing. MCS conducts memoized search via multi-agent reinforcement learning (RL) for guiding the construction of effective test configurations based on the memoization for the explored test configurations during the on-the-fly compiler-testing process. During the process, the elaborate coordination among configuration options can be also well learned by multi-agent RL, which is required for generating bug-triggering test programs. Specifically, MCS considers the diversity among test configurations to efficiently explore the input space and the testing results under each explored configuration to learn which portions of space are more bug-triggering. Our extensive experiments on GCC and LLVM demonstrate the performance of MCS, significantly outperforming the state-of-the-art test-program generation approaches in bug detection. Also, MCS detects 16 new bugs on the latest trunk revisions of GCC and LLVM, and all of them have been confirmed or fixed by developers. MCS has been deployed by a global IT company (i.e., Huawei) for testing their in-house compiler, and detects 10 new bugs (covering all the 5 bugs detected by the compared approaches), all of which have been confirmed.

A typical compiler such as GCC supports hundreds of optimizations controlled by compilation flags for improving the runtime performance of the compiled program. Due to the large number of compilation flags and the exponential number of flag combinations, it is impossible for compiler users to manually tune these optimization flags in order to achieve the required runtime performance of the compiled programs. Over the years, many compiler autotuning approaches have been proposed to automatically tune optimization flags, but they still suffer from the efficiency problem due to the huge search space. In this paper, we propose the first Bayesian optimization based approach, called BOCA, for efficient compiler autotuning. In BOCA, we leverage a tree-based model for approximating the objective function in order to make Bayesian optimization scalable to a large number of optimization flags. Moreover, we design a novel searching strategy to improve the efficiency of Bayesian optimization by incorporating the impact of each optimization flag measured by the tree-based model and a decay function to strike a balance between exploitation and exploration. We conduct extensive experiments to investigate the effectiveness of BOCA on two most popular C compilers (i.e., GCC and LLVM) and two widely-used C benchmarks (i.e., cBench and PolyBench). The results show that BOCA significantly outperforms the state-of-the-art compiler autotuning approaches and Bayesion optimization methods in terms of the time spent on achieving specified speedups, demonstrating the effectiveness of BOCA.

A new broad of video services that support live streaming has become tremendously popular in recent years. Compared with traditional video-on-demand (VOD) services, live video streaming has much higher requirements on Quality-of-Experience (QoE), including low rebuffering, high definition, low latency and low bitrate oscillations. While previous adaptive bitrate algorithms (ABR) solely optimize bitrate for ensuring QoE of VOD, live video streaming has a larger decision space, making the optimization problem more difficult to solve. We propose Deeplive, which maximizes QoE through deep reinforcement learning (DRL), so it does not rely on fixed rules. To accelerate the training process of Deeplive, we further propose optimization including window completion with historical data and quick-start with rate-based algorithm. We compare Deeplive with other advanced ABR algorithms in a frame-level dynamic adaptive video streaming simulator using different network traces, QoE definitions, and video categories. In all experiments, we find that Deeplive not only has significant improvement in training time, but also shows an average of 15-55% improvement on QoE than the state-of-the-art ABR algorithms.