Locality-Aware CTA Scheduling for Gaming Applications

The compute work rasterizer or the GigaThread Engine of a modern NVIDIA GPU focuses on maximizing compute work occupancy across all streaming multiprocessors in a GPU while retaining design simplicity. In this article, we identify the operational aspects of the GigaThread Engine that help it meet those goals but also lead to less-than-ideal cache locality for texture accesses in 2D compute shaders, which are an important optimization target for gaming applications. We develop three software techniques, namely LargeCTAs , Swizzle , and Agents , to show that it is possible to effectively exploit the texture data working set overlap intrinsic to 2D compute shaders. We evaluate these techniques on gaming applications across two generations of NVIDIA GPUs, RTX 2080 and RTX 3080, and find that they are effective on both GPUs. We find that the bandwidth savings from all our software techniques on RTX 2080 is much higher than the bandwidth savings on baseline execution from inter-generational cache capacity increase going from RTX 2080 to RTX 3080. Our best-performing technique, Agents , records up to a 4.7% average full-frame speedup by reducing bandwidth demand of targeted shaders at the L1-L2 and L2-DRAM interfaces by 23% and 32%, respectively, on the latest generation RTX 3080. These results acutely highlight the sensitivity of cache locality to compute work rasterization order and the importance of locality-aware cooperative thread array scheduling for gaming applications.

Performance Analysis of Compression Techniques for Chronic Wound Image Transmission Under Smartphone-Enabled Tele-Wound Network

The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.

Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming

360∘ panorama video displayed through Virtual Reality (VR) glasses or large screens offers immersive user experiences, but as such technology becomes commonplace, the need for efficient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360∘ panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the different trade-offs in order to save bandwidth while preserving the video quality in the user’s field-of-view (FoV). Using 360∘ VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top-box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection configurations. In this paper, we present an efficient end-to-end design and real-world implementation of such a 360∘ streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user’s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.

Performance Analysis of Compression Techniques for Chronic Wound Image Transmission Under Smartphone-Enabled Tele-Wound Network

The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.

Anonymity in Peer-assisted CDNs: Inference Attacks and Mitigation

The peer-assisted CDN is a new content distribution paradigm supported by CDNs (e.g., Akamai), which enables clients to cache and distribute web content on behalf of a website. Peer-assisted CDNs bring significant bandwidth savings to website operators and reduce network latency for users. In this work, we show that the current designs of peer-assisted CDNs expose clients to privacy-invasive attacks, enabling one client to infer the set of browsed resources of another client. To alleviate this, we propose an anonymous peer-assisted CDN (APAC), which employs content delivery while providing initiator anonymity (i.e., hiding who sends the resource request) and responder anonymity (i.e., hiding who responds to the request) for peers. APAC can be a web service, compatible with current browsers and requiring no client-side changes. Our anonymity analysis shows that our APAC design can preserve a higher level of anonymity than state-of-the-art peer-assisted CDNs. In addition, our evaluation demonstrates that APAC can achieve desired performance gains.

Scalable and Anonymous Group Communication with MTor

This paper presents MTor, a low-latency anonymous group communication system. We construct MTor as an extension to Tor, allowing the construction of multi-source multicast trees on top of the existing Tor infrastructure. MTor does not depend on an external service to broker the group communication, and avoids central points of failure and trust. MTor’s substantial bandwidth savings and graceful scalability enable new classes of anonymous applications that are currently too bandwidth-intensive to be viable through traditional unicast Tor communication-e.g., group file transfer, collaborative editing, streaming video, and real-time audio conferencing.We detail the design of MTor and then analyze its performance and anonymity. By simulating MTor in Shadow and TorPS using realistic models of the live Tor network’s topology and recent consensus records from the live Tor network, we show that MTor achieves a 29% savings in network bandwidth and a 73% reduction in transmission time as compared to the baseline approach for anonymous group communication among 20 group members. We also demonstrate that MTor scales gracefully with the number of group participants, and allows dynamic group composition over time. Importantly, as more Tor users switch to group communication, we show that the overall performance and utilization for group communication improves. Finally, we discuss the anonymity implications of MTor and measure its resistance to traffic correlation.

Scalability and Efficiency of Push-Driven P2PTV Systems

Television transmitted over IP (IPTV) presents numerousopportunities for users as well as service providers, and has attracted significant interest from industry as well asresearch communities in recent years. Among the emergingIPTV delivery architectures, the peer-to-peer based deliverymechanism is considered attractive due to the relative ease of service deployment and potential bandwidth savings. However, the question of how well P2PTV networks would support a growing number of users has not been fully investigated so far. In this paper, we try to address this question by studying scalability and efficiency factors in a typical P2P based live streaming network. Through the use of the data provided by a production P2PTV network, we carry out simulations whose results show that there are still hurdles to overcome before P2P based live streaming could become widely deployed.

The State of the Art and Open Problems in Data Replication in Grid Environments

Data Grids provide services and infrastructure for distributed data-intensive applications that need to access, transfer and modify massive datasets stored at distributed locations around the world. For example, the next-generation of scientific applications such as many in high-energy physics, molecular modeling, and earth sciences will involve large collections of data created from simulations or experiments. The size of these data collections is expected to be of multi-terabyte or even petabyte scale in many applications. Ensuring efficient, reliable, secure and fast access to such large data is hindered by the high latencies of the Internet. The need to manage and access multiple petabytes of data in Grid environments, as well as to ensure data availability and access optimization are challenges that must be addressed. To improve data access efficiency, data can be replicated at multiple locations so that a user can access the data from a site near where it will be processed. In addition to the reduction of data access time, replication in Data Grids also uses network and storage resources more efficiently. In this chapter, the state of current research on data replication and arising challenges for the new generation of data-intensive grid environments are reviewed and open problems are identified. First, fundamental data replication strategies are reviewed which offer high data availability, low bandwidth consumption, increased fault tolerance, and improved scalability of the overall system. Then, specific algorithms for selecting appropriate replicas and maintaining replica consistency are discussed. The impact of data replication on job scheduling performance in Data Grids is also analyzed. A set of appropriate metrics including access latency, bandwidth savings, server load, and storage overhead for use in making critical comparisons of various data replication techniques is also discussed. Overall, this chapter provides a comprehensive study of replication techniques in Data Grids that not only serves as a tool to understanding this evolving research area but also provides a reference to which future e orts may be mapped.