There is a multicore platform that is currently concentrating an enormous attention due to its tremendous potential in terms of sustained performance: the NVIDIA Tesla boards. These cards intended for general-purpose computing on graphic processing units (GPGPUs) are used as data-parallel computing devices. They are based on the Computed Unified Device Architecture (CUDA) which is common to the latest NVIDIA GPUs. The bottom line is a multicore platform which provides an enormous potential performance benefit driven by a non-traditional programming model. In this paper we try to provide some insight into the peculiarities of CUDA in order to target scientific computing by means of a specific example. In particular, we show that the parallelization of the two-dimensional fast wavelet transform for the NVIDIA Tesla C870 achieves a speedup of 20.8 for an image size of 8192times8192, when compared with the fastest host-only version implementation using OpenMP and including the data transfers between main memory and device memory.

The race for automation has reached farms and agricultural fields. Many of these facilities use the Internet of Things technologies to automate processes and increase productivity. Besides, Machine Learning and Deep Learning allow performing continuous decision making based on data analysis. In this work, we fill a gap in the literature and present a novel architecture based on IoT and Machine Learning / Deep Learning technologies for the continuous assessment of agricultural crop quality. This architecture is divided into three layers that work together to gather, process, and analyze data from different sources to evaluate crop quality. In the experiments, the proposed approach based on data aggregation from different sources reaches a lower percentage error than considering only one source. In particular, the percentage error achieved by our approach in the test dataset was 6.59, while the percentage error achieved exclusively using data from sensors was 6.71.

GPUs have recently attracted our attention as accelerators on a wide variety of algorithms, including assorted examples within the image analysis field. Among them, wavelets are gaining popularity as solid tools for data mining and video compression, though this comes at the expense of a high computational cost. After proving the effectiveness of the GPU for accelerating the 2D Fast Wavelet Transform , we present in this paper a novel implementation on manycore GPUs and multicore CPUs for a high performance computation of the 3D Fast Wavelet Transform (3DFWT). This algorithm poses a challenging access pattern on matrix operators demanding high sustainable bandwidth, as well as mathematical functions with remarkable arithmetic intensity on ALUs and FPUs. On the GPU side, we focus on CUDA programming to develop methods for an efficient mapping on manycores and to fully exploit the memory hierarchy, whose management is explicit by the programmer. On multicore CPUs, OpenMP and Pthreads are used as counterparts to maximize parallelism, and renowned techniques like tiling and blocking are exploited to optimize the use of memory. Experimental results on an Nvidia Tesla C870 GPU and an Intel Core 2 Quad Q6700 CPU indicate that our implementation runs three times faster on the Tesla and up to fifteen times faster when communications are neglected, which enables the GPU for processing real-time videos in many applications where the 3D-FWT is involved.