Supersampling has proven highly effective in enhancing visual fidelity by reducing aliasing, increasing resolution, and generating interpolated frames. It has become a standard component of modern real-time rendering pipelines. However, on mobile platforms, deep learning-based supersampling methods remain impractical due to stringent hardware constraints, while non-neural supersampling techniques often fall short in delivering perceptually high-quality results. In particular, producing visually pleasing reconstructions and temporally coherent interpolations is still a significant challenge in mobile settings. In this work, we present a novel, lightweight supersampling framework tailored for mobile devices. Our approach substantially improves both image reconstruction quality and temporal consistency while maintaining real-time performance. For super-resolution, we propose an intra-pixel object coverage estimation method for reconstructing high-quality anti-aliased pixels in edge regions, a gradient-guided strategy for non-edge areas, and a temporal sample accumulation approach to improve overall image quality. For frame interpolation, we develop an efficient motion estimation module coupled with a lightweight fusion scheme that integrates both estimated optical flow and rendered motion vectors, enabling temporally coherent interpolation of object dynamics and lighting variations. Extensive experiments demonstrate that our method consistently outperforms existing baselines in both perceptual image quality and temporal smoothness, while maintaining real-time performance on mobile GPUs. A demo application and supplementary materials are available on the project page.

Achieving immersive virtual reality (VR) experiences typically requires extensive computational resources to ensure high-definition visuals, high frame rates, and low latency in stereoscopic rendering. This challenge is particularly pronounced for lower-tier and standalone VR devices with limited processing power. To accelerate rendering, existing supersampling and image reprojection techniques have shown significant potential, yet to date, no previous work has explored their combination to minimize stereo rendering overhead. In this paper, we introduce a lightweight supersampling framework that integrates image projection with spatio-temporal supersampling to accelerate stereo rendering. Our approach effectively leverages the temporal and spatial redundancies inherent in stereo videos, enabling rapid image generation for unshaded viewpoints and providing resolution-enhanced and anti-aliased images for binocular viewpoints. We first blend a rendered low-resolution (LR) frame with accumulated temporal samples to construct an high-resolution (HR) frame. This HR frame is then reprojected to the other viewpoint to directly synthesize a new image. To address disocclusions in reprojected images, we utilize accumulated history data and low-pass filtering for filling, ensuring high-quality results with minimal delay. Extensive evaluations on both the PC and the standalone device confirm that our framework requires short runtime to generate high-fidelity images, making it an effective solution for stereo rendering across various VR platforms.

The computing paradigm of "HPC in the Cloud" has gained a surging interest in recent years, due to its merits of cost-efficiency, flexibility, and scalability. Cloud is designed on top of distributed file systems such as Google file system (GFS). The capability of running HPC applications on top of data-intensive file systems is a critical catalyst in promoting Clouds for HPC. However, the semantic gap between data-intensive file systems and HPC imposes numerous challenges. For example, N-1 (N to 1) is a widely used data access pattern for HPC applications such as check pointing, but cannot perform well on data-intensive file systems. In this study, we propose the CHunk-Aware I/O (CHAIO) strategy to enable efficient N-1 data access on data-intensive distributed file systems. CHAIO reorganizes I/O requests to favor data-intensive file systems and avoid possible access contention. It balances the workload distribution and promotes data locality. We have tested the CHAIO design over the Kosmos file system (KFS). Experimental results show that CHAIO achieves a more than two-fold improvement in I/O bandwidth for both write and read operations. Experiments in large-scale environment confirm the potential of CHAIO for small and irregular requests. The aggregator selection algorithm works well to balance the workload distribution. CHAIO is a critical and necessary step to enable HPC in the Cloud.