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Keyword: GPUChallenges and New Paradigms in Conservation of Heritage-based Villages in Rural India -A case of Pragpur and Garli Villages in Himachal Pradesh
The research paper aims to focus on the issues and challenges in developing a sustainable model of an ideal heritage village project by using descriptive and empirical investigation methods. To capture the perception and understanding of the concept of sustainability of a Heritage Village, a mixed-methods approach was conducted by the researcher where document where…
Read MoreAutomatic Stochastic Dithering Techniques on GPU: Image Quality and Processing Time Improved
Dithering or error diffusion is a technique used to obtain a binary image, suitable for printing, from a grayscale one. At each step, the algorithm computes an allowed value of a pixel from a grayscale one, applying a threshold and, therefore, causing a conversion error. To obtain the optical illusion of a continuous tone, the…
Read MoreMachine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database
With the current amount of data nowadays, the need for processing power has vastly grown. By relying on CPU processing power, current processing power is depending on the frequency and parallelism of the current CPU device. This means this method will lead to increased power consumption. Current research has shown that by utilize the power…
Read MoreParallelizing Combinatorial Optimization Heuristics with GPUs
Combinatorial optimization problems are often NP-hard and too complex to be solved within a reasonable time frame by exact methods. Heuristic methods which do not offer a convergence guarantee could obtain some satisfactory resolution for combinatorial optimization problems. However, it is not only very time consuming for Central Processing Units (CPU) but also very difficult…
Read MoreSoftware and Hardware Enhancement of Convolutional Neural Networks on GPGPUs
Convolutional Neural Networks (CNNs) have gained attention in recent years for their ability to perform complex machine learning tasks with high accuracy and resilient to noise of inputs. The time-consuming convolution operations of CNNs pose great challenges to both software as well as hardware designers. To achieve superior performance, a design involves careful concerns between…
Read MoreHigh Performance SqueezeNext: Real time deployment on Bluebox 2.0 by NXP
DNN implementation and deployment is quite a challenge within a resource constrained environment on real-time embedded platforms. To attain the goal of DNN tailor made architecture deployment on a real-time embedded platform with limited hardware resources (low computational and memory resources) in comparison to a CPU or GPU based system, High Performance SqueezeNext (HPS) architecture…
Read MoreNeural Network for 2D Range Scanner Navigation System
Navigation of a moving object (drone, vehicle, robot, and so on) and related localization in unknown scenes is nowadays a challenging subject to be addressed. Typically, different source devices, such as image sensor, Inertial Measurement Unit (IMU), Time of Flight (TOF), or a combination of them can be used to reach this goal. Recently, due…
Read MorePerformance Portability and Unified Profiling for Finite Element Methods on Parallel Systems
The currently available variety of modern, highly-parallel universal processors includes multi-core CPU and many-core GPU (Graphics Processing Units) from different vendors. Systems composed of such processors enable high-performance execution of demanding applications like numerical Finite Element Methods. However, today’s application pro- gramming for parallel systems lacks performance portability: the same program code cannot achieve stable…
Read MoreParallel Hybrid Testing Tool for Applications Developed by Using MPI + OpenACC Dual-Programming Model
Building massively parallel applications has become increasingly important with coming Exascale related technologies. For building these applications, a combination of programming models is needed to increase the system’s parallelism. One of these combinations is the dual-programming model (MPI+X) which has many structures that increase parallelism in heterogeneous systems that include CPUs and GPUs. MPI +…
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