Search Word(s) in Title, Keywords, Authors, and Abstract: CUDA

Accelerating Group Fusion for Ligand-Based Virtual Screening on Multi-core and Many-core Platforms
Mohd-Norhadri Mohd-Hilmi, Marwah Haitham Al-Laila and Nurul Hashimah Ahamed Hassain Malim
Page: 724~740, Vol. 12, No.4, 2016
10.3745/JIPS.01.0012
Keywords: Chemoinformatics, Graphical Processing Unit, Group Fusion, Open Multiprocessing, Virtual Screening
Show / Hide Abstract
The performance issues of screening large database compounds and multiple query compounds in virtual screening highlight a common concern in Chemoinformatics applications. This study investigates these problems by choosing group fusion as a pilot model and presents efficient parallel solutions in parallel platforms, specifically, the multi-core architecture of CPU and many-core architecture of graphical processing unit (GPU). A study of sequential group fusion and a proposed design of parallel CUDA group fusion are presented in this paper. The design involves solving two important stages of group fusion, namely, similarity search and fusion (MAX rule), while addressing embarrassingly parallel and parallel reduction models. The sequential, optimized sequential and parallel OpenMP of group fusion were implemented and evaluated. The outcome of the analysis from these three different design approaches influenced the design of parallel CUDA version in order to optimize and achieve high computation intensity. The proposed parallel CUDA performed better than sequential and parallel OpenMP in terms of both execution time and speedup. The parallel CUDA was 5-10x faster than sequential and parallel OpenMP as both similarity search and fusion MAX stages had been CUDA-optimized


Accelerating the Sweep3D for a Graphic Processor Unit
Chunye Gong, Jie Liu, Haitao Chen, Jing Xie and Zhenghu Gong
Page: 63~74, Vol. 7, No.1, 2011
10.3745/JIPS.2011.7.1.063
Keywords: Sweep3D, Neutron Transport, GPU, CUDA
Show / Hide Abstract
As a powerful and flexible processor, the Graphic Processing Unit (GPU) can offer a great faculty in solving many high-performance computing applications. Sweep3D, which simulates a single group time-independent discrete ordinates (Sn) neutron transport deterministically on 3D Cartesian geometry space, represents the key part of a real ASCI application. The wavefront process for parallel computation in Sweep3D limits the concurrent threads on the GPU. In this paper, we present multi-dimensional optimization methods for Sweep3D, which can be efficiently implemented on the finegrained parallel architecture of the GPU. Our results show that the overall performance of Sweep3D on the CPU-GPU hybrid platform can be improved up to 4.38 times as compared to the CPU-based implementation.