General purpose computing on GPU for scientific computing has been rapidly growing in recent years. We investigate the applicability of GPU to discrete element method (DEM) often used in particle motion simulation. NVIDIA provides a sample code for this type of simulation, which obtained superior performance than CPU in computational time. A computational model of the contact force in NVIDIA’s ...

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...

General Purpose GPU computing, or GPGPU, is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing. The model for GPU computing is to use a CPU and GPU together in a heterogeneous co-processing computing platform. The sequential part of the application runs on the CPU and the computationally-intensive part is accelerated by the GPU. From the users...

Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility in their design, management and deployment. Although GPUs have demonstrated their power in significantly accelerating network functions, they have not been effectively integrated into NFV systems for the following reasons. First, GPUs are severely underutilized in NFV systems with existing GPU virt...

Graphic Processing Units (GPU) has been proved to be a promising platform to accelerate large size Fast Fourier Transform (FFT) computation. However, current GPU-based FFT implementation only uses GPU to compute, but employs CPU as a mere memory-transfer controller. The computation power in today’s high-performance CPU is wasted. In this project, a hybrid optimization framework is proposed to u...

Integrated CPU-GPU architecture provides excellent acceleration capabilities for data parallel applications on embedded platforms while meeting the size, weight and power (SWaP) requirements. However, sharing of main memory between CPU applications and GPU kernels can severely affect the execution of GPU kernels and diminish the performance gain provided by GPU. For example, in the NVIDIA Jetso...

This paper presents a compiler toolkit that addresses two important emerging challenges: (1) effectively compiling dynamic array-based languages such as MATLAB, Python and R; and (2) effectively utilizing a wide range of rapidly evolving hybrid CPU/GPU architectures. The toolkit provides: a high-level IR specifically designed to express a wide range of arraybased computations and indexing modes...

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ...

Heterogeneous CPU-GPU system is a powerful way to accelerate compute-intensive applications, such as the subset-sum problem. Many parallel algorithms for solving the problem have been implemented on graphics processing units (GPUs). However, these GPU implementations may fail to fully utilize all the CPU cores and the GPU resources. When the GPU performs computational task, only one CPU core is...

We present new parallel algorithms that solve continuous-state partially observable Markov decision process (POMDP) problems using the GPU (gPOMDP) and a hybrid of the GPU and CPU (hPOMDP). We choose the Monte Carlo value iteration (MCVI) method as our base algorithm and parallelize this algorithm using the multi-level parallel formulation of MCVI. For each parallel level, we propose efficient ...