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...

Graphics Processing Unit (GPU) virtualization is an enabling technology in emerging virtualization scenarios. Unfortunately, existing GPU virtualization approaches are still suboptimal in performance and full feature support. This paper introduces gVirt, a product level GPU virtualization implementation with: 1) full GPU virtualization running native graphics driver in guest, and 2) mediated pa...

Much of the current focus in high performance computing (HPC) for computational fluid dynamics (CFD) deals with grid based methods. However, parallel implementations for new meshfree particle methods such as Smoothed Particle Hydrodynamics (SPH) are less studied. In this work, we present optimizations for both central processing unit (CPU) and graphics processing unit (GPU) of a SPH method. The...

We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements ...

Remote procedure call (RPC) is a simple, transparent and useful paradigm for providing communication between two processes across a network. The compute unified device architecture (CUDA) programming toolkit and runtime enhance the programmability of the graphics processing unit (GPU) and make GPU more versatile in high performance computing. The current researches mainly focus on the accelerat...

We discuss an investigation into parallelizing the computation of a singular value decomposition (SVD). We break the process into three steps: bidiagonalization, computation of the singular values, and computation of the singular vectors. We discuss the algorithms, parallelism, implementation, and performance of each of these three steps. The original goal was to accomplish all three tasks usin...

The computation model of the DT-CNN is classified into two types. One is called the synchronous model. The other is the asynchronous model. In recent years, the graphics processing unit (GPU) is getting a lot more attention. Because the GPU has many processor cores, it appears that the GPU accelerates the computation of the synchronous model. In this paper, for evaluating computational performa...

This paper makes two principal contributions. The first is that there appears to be no previous a description in the research literature of an artificial neural network implementation on a graphics processor unit (GPU) that uses the Levenberg-Marquardt (LM) training method. The second is an initial attempt at determining when it is computationally beneficial to exploit a GPU’s parallel nature i...

An efficient error reconciliation scheme is important for post-processing of quantum key distribution (QKD). This paper concerns the improvement performance. Recently, a multi-matrix low-density parity-check code-based algorithm which can provide remarkable perspectives high-efficiency information was proposed by Gao (Opt Express 27:14545–14566, 2019). The implemented and optimized on graphics ...