Posts
Jun, 21
Beam Dynamics Simulations with a GPU-accelerated Version of ELEGANT
Large scale beam dynamics simulations can derive significant benefit from efficient implementation of general-purpose particle tracking on GPUs. We present the latest results of our work on accelerating Argonne National Lab’s accelerator simulation code ELEGANT, using CUDA-enabled GPUs. We summarize the performance of beamline elements ported to GPU, and discuss optimization techniques for some core […]
Jun, 21
Applying the “Simple Accelerator Modelling in MATLAB” (SAMM) Code to High Luminosity LHC Upgrade
The “Simple Accelerator Modelling in Matlab” (SAMM) code is a set of Matlab routines for modelling beam dynamics in high energy particle accelerators. It includes a set of CUDA codes that can be run on a graphics processing unit. These can be called from SAMM and can potentially give a significant increase in tracking speed. […]
Jun, 21
A Numerical Study of Continuous Data Assimilation for the 2D-NS Equations Using Nodal Points
This thesis conducts a number of numerical experiments using massively parallel GPU computations to study a new continuous data assimilation algorithm. We test the algorithm on two-dimensional incompressible fluid flows given by the Navier-Stokes equations. In this context, observations of the Eulerian velocity field given at a finite resolution of nodal points in space may […]
Jun, 21
libCudaOptimize: an Open Source Library of GPU-based Metaheuristics
Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, a major one is their natural predisposition to parallelization. In this paper, we introduce libCudaOptimize, an open source library which implements some metaheuristics for continuous […]
Jun, 21
CFMDS: CUDA-based fast multidimensional scaling for genome-scale data
BACKGROUND: Multidimensional scaling (MDS) is a widely used approach to dimensionality reduction. It has been applied to feature selection and visualization in various areas. Among diverse MDS methods, the classical MDS is a simple and theoretically sound solution for projecting data objects onto a low dimensional space while preserving the original distances among them as […]
Jun, 21
Artificial Neural Network Simulation on CUDA
The advent of low cost GPU hardware and user friendly parallel programming APIs, such as NVIDIA CUDA means that affordable, programmable, high-performance computing environments for simulation are now attainable for development of scientific simulations. In this paper the authors present the MineHunter program, a parallel simulation of neural networks on NVIDIA CUDA. The simulation consists […]
Jun, 21
On the Effect of Using Multiple GPUs in Solving QAPs with CUDA
In this paper, we implement ACO algorithms on a PC which has 4 GTX 480 GPUs. We implement two types of ACO models; the island model, and the other is the master/slave model. When we compare the island model and the master/slave model, the island model shows promising speedup values on class (iv) QAP instances. […]
Jun, 21
Continuous Representation of Projected Attribute Spaces of Multifields over Any Spatial Sampling
For the visual analysis of multidimensional data, dimension reduction methods are commonly used to project to a lower-dimensional visual space. In the context of multifields, i.e., volume data with a multidimensional attribute space, the spatial arrangement of the samples in the volumetric domain can be exploited to generate a Continuous Representation of the Projected Attribute […]
Jun, 19
Parallel Algorithms for Hybrid Multi-core CPU-GPU Implementations of Component Labelling in Critical Phase Models
Optimising the use of all the cores of a hybrid multi-core CPU and its accelerating GPUs is becoming increasingly important as such combined systems become widely available. We show how a complex interplay of cross-calling kernels and host components can be used to support good throughput performance on hybrid simulation tasks that have inherently serial […]
Jun, 19
Deep learning with COTS HPC systems
Scaling up deep learning algorithms has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level features. Recent efforts to train extremely large networks (with over 1 billion parameters) have relied on cloud-like computing infrastructure and thousands of CPU cores. In this paper, we present technical details and […]
Jun, 19
Megakernels Considered Harmful: Wavefront Path Tracing on GPUs
When programming for GPUs, simply porting a large CPU program into an equally large GPU kernel is generally not a good approach. Due to SIMT execution model on GPUs, divergence in control flow carries substantial performance penalties, as does high register usage that lessens the latency-hiding capability that is essential for the high-latency, high-bandwidth memory […]
Jun, 19
Real-Time Geometry Decompression on Graphics Hardware
Real-Time Computer Graphics focuses on generating images fast enough to cause the illusion of a continuous motion. It is used in science, engineering, computer games, image processing, and design. Special purpose graphics hardware, a so-called graphics processing unit (GPU), accelerates the image generation process substantially. Therefore, GPUs have become indispensable tools for Real-Time Computer Graphics. […]