Posts
Jul, 18
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval – where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art […]
Jul, 18
Suitability of NVIDIA GPUs for SKA1-Low
In this memo we investigate the applicability of NVIDIA Graphics Processing Units (GPUs) for SKA1-Low station and Central Signal Processing (CSP)-level processing. Station-level processing primarily involves generating a single station beam which will then be correlated with other beams in CSP. Fine channelisation can be performed either at the station of CSP-level, while coarse channelisation […]
Jul, 17
Efficient implementation of computationally intensive algorithms on parallel computing platforms
Two different types of computationally intensive problems have been researched to investigate the design methodology of the acceleration and to give a high-performance implementation on parallel architectures. Each problem was accelerated via a different architecture, and the results of the investigation were summarized in different thesis groups. The design methodology proposed in Thesis 1 can […]
Jul, 17
Rapid Modelling of Interactive Geological Illustrations with Faults and Compaction
In this paper, we propose new methods for building geological illustrations and animations. We focus on allowing geologists to create their subsurface models by means of sketches, to quickly communicate concepts and ideas rather than detailed information. The result of our sketch-based modelling approach is a layer-cake volume representing geological phenomena, where each layer is […]
Jul, 17
The Use of GPUs for Solving the Computed Tomography Problem
Computed tomography (CT) is a widespread method used to study the internal structure of objects. The method has applications in medicine, industry and other fields of human activity. In particular, Electronic Imaging, as a species CT, can be used to restore the structure of nanosized objects. Accurate and rapid results are in high demand in […]
Jul, 17
Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs
GPUs gain high popularity in High Performance Computing, due to their massive parallelism and high performance per Watt. Despite their popularity, data transfer between multiple GPUs in a cluster remains a problem. Most communication models require the CPU to control the data flow; also intermediate staging copies to host memory are often inevitable. These two […]
Jul, 17
Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs
In this paper, we propose a basic technique to minimize the computational time in executing the infinite-stage dynamic programming (DP) on a GPU. The infinite-stage DP involves computations to probe whether a value function gets sufficiently close to the optimal one. Such computations for probing convergence become obvious when an infinite-stage DP is executed on […]
Jul, 16
GPUdrive: Reconsidering Storage Accesses for GPU Acceleration
GPU-accelerated data-intensive applications demonstrate in excess of ten-fold speedups over CPU-only approaches. However, file-driven data movement between the CPU and the GPU can degrade performance and energy efficiencies by an order of magnitude as a result of traditional storage latency and ineffectual memory management. In this paper, we first analyze these two critical performance bottlenecks […]
Jul, 16
Interactive GPU Ray Casting using Progressive Blue Noise Sampling
We describe a generic approach to incorporate progressive refinement into GPU-based ray casting. Our approach allows to interactively navigate through highly complex scenes that may usually take several seconds to render while producing high-quality anti-aliased images in late stages of the refinement process. It maintains interactivity by initially evaluating only a small number of screen […]
Jul, 16
Parallel Variable Pre-Selection and Lookahead Solving on GPUs
SAT solving strategies that perform backtracking or clause learning are usually difficult to implement efficiently on massively-parallel architectures because the necessary synchronization does not scale linear with the number of processors available. Strategies like Lookahead Solving and Cube and Conquer are more promising. In order to evaluate a potential GPU implementation of Cube and Conquer, […]
Jul, 15
Computational Simulation of Freely Falling Water Droplets on Graphics Processing Units
This work describes and demonstrates a novel numerical framework suitable for simulating the behaviour of freely falling liquid droplets. The specific case studied is designed such that the properties of the system are similar to those of raindrops falling through air. The study of raindrops is interesting from both an engineering standpoint and from a […]
Jul, 15
CUD@SAT: SAT Solving on GPUs
The parallel computing power offered by Graphical Processing Units (GPUs) has been recently exploited to support general purpose applications-by exploiting the availability of general API and the SIMT-style parallelism present in several classes of problems (e.g., numerical simulations, matrix manipulations) – where relatively simple computations need to be applied to all items in large sets […]