In this paper, we present a parallel implementation of the Particle Swarm Optimization (PSO) on GPU using CUDA. By fully utilizing the processing power of graphic processors, our implementation provides a speedup of 215x compared to a sequential implementation on CPU. This speedup is significantly superior to what has been reported in recent papers and is achieved by a few simple optimizations ...

a r t i c l e i n f o a b s t r a c t We present a CPU–GPU system for runtime acceleration of large molecular simulations using GPU computation and memory swaps. The memory architecture of the GPU can be used both as container for simulation data stored on the graphics card and as floating-point code target, providing an effective means for the manipulation of atomistic or molecular data on the...

This thesis presents a parallel, dynamic programming based model which is deployed on the GPU of a system to accelerate the solving of optimisation problems. This is achieved by simultaneously running GPU based computations, and memory transactions, allowing computation to never pause, and overcoming the memory constraints of solving large problem instances. Due to this some optimisation proble...

The graphics processing unit (GPU) is a specialized and highly parallel microprocessor designed to offload and accelerate 2D or 3D rendering from the central processing unit (CPU). GPUs can be found in a wide range of systems, from desktops and laptops to mobile phones and super computers [3]. This paper provides a summary of the history and evolution of GPU hardware architecture. The informati...

Previous work created the systemic computer – a model of computation designed to exploit many natural properties observed in biological systems, including parallelism. The approach has been proven through two existing implementations and many biological models and visualizations. However to date the systemic computer implementations have all been sequential simulations that do not exploit the t...

In this paper, we propose an inherent parallel scheme for 3D image segmentation of large volume data on a GPU cluster. This method originates from an extended Lattice Boltzmann Model (LBM), and provides a new numerical solution for solving the level set equation. As a local, explicit and parallel scheme, our method lends itself to several favorable features: (1) Very easy to implement with the ...

Recent advances in general purpose GPU computing technology allow new data parallel kernel jobs to be dispatched dynamically during kernel execution. This enables significantly more expressive programming using nested data parallelism (NDP), where the restrictive need for flat data structures and computation has been lifted. Functional programming is fundamentally well suited for expressing dat...

Abstract Hybrid platforms combining multicore central processing units (CPU) with many-core hardware accelerators such as graphic (GPU) can be smartly exploited to provide efficient parallel implementations of wireless communication algorithms for Fifth Generation (5G) and beyond systems. Massive multiple-input multiple-output (MIMO) systems are a key element the 5G standard, involving several ...

We present a novel parallel binomial algorithm to compute prices of American options. The algorithm partitions a binomial tree into blocks of multiple levels of nodes, and assigns each such block to multiple processors. Each processor in parallel with the others computes the option’s values at nodes assigned to it. The computation consists of two phases, where the second phase can not start unt...