In complex$360^{\circ }$scenes, depth estimation is challenging for small objects and the depth of object boundaries, which cannot be effectively solved with existing works.$360^{\circ }$depth estimation is unable to produce uniform depth estimate findings in both indoor and outdoor settings due to the datasets. In this paper, the Useg-PanoDepth and PanoDepth dataset is proposed to improve the above problems effectively. The Diagonal-aware Attention Module (DAM) effectively estimates small objects in complex scenes. Enhanced Boundary Module (EBM), for enhancing boundary information,can also effectively solve the problem of depth unification of indoor and outdoor scenes. Extensive experiments on our constructed PanoDepth dataset, Useg-PanoDepth achieves SOTA results. The Relative accuracy (deltahttps://github.com/xjh6/Useg-PanoDepth.

Ransomware attacks have emerged as one of the most significant cybersecurity threats. Despite numerous methods proposed for detecting and defending against ransomware, existing approaches face two fundamental limitations in large-scale industrial applications: (1) Behavior-based detection engines suffer from the enormous overhead of monitoring all processes and resource constraints for model inference, failing to meet the requirements for real-time detection; (2) Decoy-based detection engines generate an overwhelming number of false positives in large-scale industrial clusters, leading to intolerable disruptions to critical processes and excessive inspection efforts from security analysts. To address these challenges, we propose CanCal, a real-time and lightweight ransomware detection system. Specifically, instead of indiscriminately analyzing all processes, CanCal selectively filters suspicious processes by the monitoring layers and then performs in-depth behavioral analysis to isolate ransomware activities from benign operations, minimizing alert fatigue while ensuring lightweight computational and storage overhead. The experimental results on a large-scale industrial environment (1,761 ransomware, ~ 3 million events, continuous test over 5 months) indicate that CanCal achieves a remarkable 99.65% true positive rate on 555,678 unknown ransomware behavior events, with near-zero false positives. CanCal is as effective as state-of-the-art techniques while enabling rapid inference within 30ms and real-time response within a maximum of 3 seconds. CanCal dramatically reduces average CPU utilization by 91.04% (from 6.7% to 0.6%) and peak CPU utilization by 76.69% (from 26.6% to 6.2%), while avoiding 76.50% (from 3,192 to 750) of the inspection efforts from security analysts. By the time of this writing, CanCal has been integrated into a commercial product and successfully deployed on 3.32 million endpoints for over a year. From March 2023 to April 2024, CanCal successfully detected and thwarted 61 ransomware attacks. A detailed manual forensic analysis of 27 ransomware attacks from March to June 2023 (including 13 n-day exploits and 5 high-risk zero-day attacks) demonstrates the effectiveness of CanCal in combating sophisticated and unknown ransomware threats in real-world scenarios.

Migration algorithm is one of the most essential methods in seismic application to image the underground geology, and to help scientists and researchers in geophysics exploration better understand the earth system. However, due to the desire in migration algorithm for covering lager region and acquiring better resolution, many tough challenges have to be tackled for current state-of-the-art computing systems. This work optimized and scaled the elastic migration algorithm onto the Sunway TaihuLight supercomputer, one of the most powerful systems of the world. Targeting at the major process, the reverse time migration (RTM) algorithm, a set of algorithmic, process-level, and thread-level optimizations is proposed, to significantly improve the performance (up to 163× speedup in time-to-solution) on Sunway CPU. Our design is successfully scaled to over two million cores (2,662,400 cores in total) on the Sunway TaihuLight supercomputer, with nearly ideal weak-scaling efficiency. The largest run is able to achieve a sustainable performance of processing over 859 billion cells per second.

The Lattice Boltzmann Method (LBM) is a relatively new class of Computational Fluid Dynamics methods. In this paper, we report our work on SunwayLB, which enables LBM based solutions aiming for industrial applications. We propose several techniques to boost the simulation speed and improve the scalability of SunwayLB, including a customized multi-level domain decomposition and data sharing scheme, a carefully orchestrated strategy to fuse kernels with different performance constraints for a more balanced workload, and optimization strategies for assembly code, which bring up to 137x speedup. Based on these optimization schemes, we manage to perform the largest direct numerical simulation which involves up to 5.6 trillion lattice cells, achieving 11,245 billion cell updates per second (GLUPS), 77% memory bandwidth utilization and a sustained performance of 4.7 PFlops. We also demonstrate a series of computational experiments for extreme-large scale fluid flow, as examples of real-world applications, to check the validity and performance of our work. The results show that SunwayLB is competent for a practical solution for industrial applications.

This article demonstrates an approach for combining general tuning techniques with the POWER8 hardware architecture through optimizing three representative stencil benchmarks. Two typical real-world applications, with kernels similar to those of the winning programs of the Gordon Bell Prize 2016 and 2017, are employed to illustrate algorithm modifications and a combination of hardware-oriented tuning strategies with the application algorithms. This work fills the gap between hardware capability and software performance of the POWER8 processor, and provides useful guidance for optimizing stencil-based scientific applications on POWER systems.

As scientific applications are increasingly ported to GPUs to benefit from both the powerful computing capacity and high throughput, accelerating explicit solvers for GPU-based finite volume methods is gaining more and more attention. In this paper, based on the detailed analysis of the FVM algorithm, we present a set of novel optimization methods, including the explicit data cache mechanism, optimal global memory loading strategy, as well as the inner-thread rescheduling method, which derives a suitable mapping from the solver algorithm to the underlying GPU hardware architecture, so as to remarkably improve the solving performance of structured mesh based FVM. We demonstrate the impact of our tuning techniques on two widely-used atmospheric dynamic kernels (3-D Euler and 2-D SWE) on five kinds of mainstream GPU platforms, and make a detailed analysis of the different tuning methodologies so as to demonstrate how to select the proper tuning strategy to different applications on various GPU platforms. Specifically, 93.9x speedup is achieved for the 3D Euler solver on Nvidia V100 over one 12-core Intel E5-2697 (v2) CPU, which is a 77 percent improvement compared with the original speedup without adopting the tuning techniques presented in this work.

As a traditional application on various supercomputers, atmospheric modeling has long been suffering from the low performance efficiency. In this paper, we pick the 3D Euler equation solver (the most essential dynamic component for a non-hydrostatic atmospheric model) as the target application, and explore the maximum performance efficiency that can be achieved on CPU-GPU hybrid architectures. Besides presenting the suitable hybrid domain decomposition methodology and taking proper usage of tuning techniques for both the CPU and GPU parts, we further propose a novel GPU tuning technique, namely the customizable data caching mechanism with thread warp rescheduling scheme, which is specifically designed for the Euler solver. Combining all the optimizing approaches together, remarkable performance boost has been achieved on mainstream GPU architectures including Tesla Fermi C2050, K20×, K40 and K80. Especially, on the latest Tesla K80, we demonstrate a 31.64× speedup over the performance of 12-core E5-2697 CPU. In addition, based on a hybrid CPU-GPU node with two 12-core E5-2697 CPUs and two Tesla K80 GPUs, a sustained double-precision performance of 1.04 Tflops (16% of the peak) is achieved, which is remarkably higher than the efficiency of similar optimizing tasks based on heterogeneous platforms (strictly less than 10%, as demonstrated in the related work). In addition, a nearly linear weak scaling efficiency is achieved which demonstrate the effectiveness of our domain decomposition method.

In this paper we choose the widely used Jacobi stencil algorithm as our target program to evaluate the effectiveness of tuning techniques based on the latest POWER8 processor, thus to provide optimization guidelines to similar stencil based algorithms.

Scientific HPC applications are increasingly ported to GPUs to benefit from both the high throughput and the powerful computing capacity. Many of these applications, such as atmospheric modeling and hydraulic erosion simulation, are adopting the finite volume method (FVM) as the solver algorithm. However, the communication components inside these applications generally lead to a low flop-to-byte ratio and an inefficient utilization of GPU resources. This paper aims at optimizing FVM solver based on the structured mesh. Besides a high-level overview of the finite-volume method as well as its basic optimizations on modern GPU platforms, we further present two generalized tuning techniques including an explicit cache mechanism as well as an inner-thread rescheduling method that tries to achieve a suitable mapping between the algorithm feature and the platform architecture. To the end, we demonstrate the impact of our generalized optimization methods in two typical atmospheric dynamic kernels (Euler and SWE) based on four mainstream GPU platforms. According to the experimental results of Tesla K80, speedups of 24.4x for SWE and 31.5x for Euler could be achieved over a 12-core Intel E5-2697 CPU, which is a great promotion compared with its original speedup (18x and 15.47x) without applying these two methods.

This paper reports our efforts on refactoring and optimizing the Community Atmosphere Model (CAM) on the Sunway TaihuLight supercomputer, which uses a many-core processor that consists of management processing elements (MPEs) and clusters of computing processing elements (CPEs). To map the large code base of CAM to the millions of cores on the Sunway system, we take OpenACC-based refactoring as the major approach, and apply source-to-source translator tools to exploit the most suitable parallelism for the CPE cluster, and to fit the intermediate variable into the limited on-chip fast buffer. For individual kernels, when comparing the original ported version using only MPEs and the refactored version using both the MPE and CPE clusters, we achieve up to 22× speedup for the compute-intensive kernels. For the 25km resolution CAM global model, we manage to scale to 24,000 MPEs, and 1,536,000 CPEs, and achieve a simulation speed of 2.81 model years per day.