Real-time flow-based video abstraction using OpenCL

A non-photorealistic rendering technique is a method to show various effects different from those of realistic image generation.Of the various techniques,flow-based image abstraction displays the shape and color features well and performs a stylistic visual abstraction.But real-time rendering is impossible when CPU is used because it applies various filtering and iteration methods.In this paper,we present real-time processing methods of video abstraction using open open computing language(OpenCL),technique of general-purpose computing on graphics processing units(GPGPU).Through the acceleration of general-purpose computing(GPU),16 frame-per-second(FPS)or greater is shown to process video abstraction.

Understanding Science Through the Computational Lens

This article explores the changing nature of the interaction between computer science and the natural and social sciences. After briefly tracing the history of scientific computation, the article presents the concept of computational lens, a metaphor for a new relationship that is emerging between the world of computation and the world of the sciences. Our main thesis is that, in many scientific fields, the processes being studied can be viewed as computational in nature, in the sense that the processes perform dynamic transformations on information represented as digital data. Viewing natural or engineered systems through the lens of their computational requirements or capabilities provides new insights and ways of thinking. A number of examples are discussed in support of this thesis. The examples are from various fields, including quantum computing, statistical physics, the World Wide Web and the Internet, mathematics, and computational molecular biology.

An MPI+OpenACC-Based PRM Scalar Advection Scheme in the GRAPES Model over a Cluster with Multiple CPUs and GPUs

A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor.The Piecewise Rational Method(PRM) scalar advection scheme in the Global/Regional Assimilation and Prediction System(GRAPES) solves the moisture flux advection equation based on PRM.Computation of the scalar advection involves boundary exchange,and computation of higher bandwidth requirements is complicated and time-consuming in GRAPES.Recently,Graphics Processing Units(GPUs) have been widely used to solve scientific and engineering computing problems owing to advancements in GPU hardware and related programming models such as CUDA/OpenCL and Open Accelerator(OpenACC).Herein,we present an accelerated PRM scalar advection scheme with Message Passing Interface(MPI) and OpenACC to fully exploit GPUs’ power over a cluster with multiple Central Processing Units(CPUs) and GPUs,together with optimization of various parameters such as minimizing data transfer,memory coalescing,exposing more parallelism,and overlapping computation with data transfers.Results show that about 3.5 times speedup is obtained for the entire model running at medium resolution with double precision when comparing the scheme’s elapsed time on a node with two GPUs(NVIDIA P100) and two 16-core CPUs(Intel Gold 6142).Further,results obtained from experiments of a higher resolution model with multiple GPUs show excellent scalability.

Implementation of the moving particle semi-implicit method on GPU

The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouring particles and solving the large sparse matrix equations (Poisson-type equation) are very time-consuming. In order to utilize the tremendous power of parallel computation of Graphics Processing Units (GPU), this study has developed a GPU-based MPS model employing the Compute Unified Device Architecture (CUDA) on NVIDIA GTX 280. The efficient neighbourhood particle searching is done through an indirect method and the Poisson-type pressure equation is solved by the Bi-Conjugate Gradient (BiCG) method. Four different optimization levels for the present general parallel GPU-based MPS model are demonstrated. In addition, the elaborate optimization of GPU code is also discussed. A benchmark problem of dam-breaking flow is simulated using both codes of the present GPU-based MPS and the original CPU-based MPS. The comparisons between them show that the GPU-based MPS model outperforms 26 times the traditional CPU model.

Real-time 3D Microtubule Gliding Simulation Accelerated by GPU Computing

A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics on computers, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation paranleters in a real-time fashion. One of the technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. Graphics processor unit （GPU） programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of general-purpose computing on graphics processing units （GPGPU） programming we are able to run the simulation in a massively parallel fashion, even when dealing with more complex interactions between microtubules such as overriding and snuggling. Due to performance being an important factor, a performance n, odel has also been constructed from the analysis of the microtubule simulation and it is consistent with the performance measurements on different GPGPU architectures with regards to the number of cores and clock cycles.