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General-Purpose Graphics Processing Units (GPGPXJs) his been widely used in modern high performance computing systems. The Single-Instruction Multi-Thread (SIMT) execution model of GPGPUs results in a lower page hit rate, and requires Page Walk Cache (PWC) to reduce the actual number of page table accesses for irregular applications. There is a lot of redundant information in the traditional PWC and the capacity is limited, so the actual effect is not good. We analyze the information redundancy in the traditional PWC and propose a new structure： Compressed PWC. Compressed PWC completely eliminates redundant information and compresses the space while keeping the same search overhead unchanged, so that PWC can record more page table access history, thereby effectively reducing the number of page table accesses during the address translation. Experimental results indicate that, compared with the traditional PWC of the same capacity，compressed PWC can significantly improve the efficiency of virtual-to-physical address translation. [ABSTRACT FROM AUTHOR]

Smoke detection is a key component of disaster and accident detection. Despite the wide variety of smoke detection methods and sensors that have been proposed, none has been able to maintain a high frame rate while improving detection performance. In this paper, a smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information. The method takes advantage of the use of a stationary camera by using a background subtraction method to detect changes in the scene. The color of the smoke is used to assess the probability that pixels in the scene belong to a smoke region. Due to the variable density of the smoke, not all pixels of the actual smoke area appear in the foreground mask. These separate pixels are united by morphological operations and connected-component labeling methods. The existence of a smoke region is confirmed by analyzing the roughness of its boundary. The final step of the algorithm is to check the density of edge pixels within a region. Comparison of objects in the current and previous frames is conducted to distinguish fluid smoke regions from rigid moving objects. Some parts of the algorithm were boosted by means of parallel processing using compute unified device architecture graphics processing unit, thereby enabling fast processing of both low-resolution and high-definition videos. The algorithm was tested on multiple video sequences and demonstrated appropriate processing time for a realistic range of frame sizes. [ABSTRACT FROM PUBLISHER]

Robotic controllers have to execute various complex independent tasks repeatedly. Massive processing power is required by the motion controllers to compute the solution of these computationally intensive algorithms. General-purpose graphics processing unit (GPGPU)-enabled mobile phones can be leveraged for acceleration of these motion controllers. Embedded GPUs can replace several dedicated computing boards by a single powerful and less power-consuming GPU. In this paper, the inverse kinematic algorithm based numeric controllers is proposed and realized using the GPGPU of a handheld mobile device. This work is the extension of a desktop GPU-accelerated robotic controller presented at DAS'16 where the comparative analysis of different sequential and concurrent controllers is discussed. First of all, the inverse kinematic algorithm is sequentially realized using Arduino-Due microcontroller and the field-programmable gate array (FPGA) is used for its parallel implementation. Execution speeds of these controllers are compared with two different GPGPU architectures (Nvidia Quadro K2200 and Nvidia Shield K1 Tablet), programmed with Compute Unified Device Architecture (CUDA) computing language. Experimental data shows that the proposed mobile platform-based scheme outperforms the FPGA by 5× and boasts a 100× speedup over the Arduino-based sequential implementation. speedup over the Arduino-based sequential implementation. [ABSTRACT FROM AUTHOR]