Test and Analysis GPU-Accelerated in Molecular Dynamics Simulation

2013 ◽  
Vol 380-384 ◽  
pp. 1652-1655
Author(s):  
Zhang Yang ◽  
Chen Wen Bo ◽  
Bai Qi Feng ◽  
Lian Li
Keyword(s):  
Large Scale ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
System Simulation ◽  
Dynamics Simulation ◽  
Utilization Rate ◽  
Processing Unit ◽  
The Difference ◽  
Graphics Processing ◽  
Memory Utilization

GPU computing is the use of a graphics processing unit together with a CPU to accelerate large scale scientific and engineering applications, such as molecule simulation. The paper use NVIDIA Tesla C2050NVIDIA GTX580 and NAMD 2.9 simulates three differences molecule systems: Beta2,SET9 and Ubiquitin. We compared and analyzed the results of the simulations experiment, and come to conclusion that the difference molecule systems will get the difference speed accelerated. The computing times of four GPU is nearly half of the time used by one GPU; and this is especially in the case of macromolecules system. Furthermore, from the GPUs memory utilization rate, the larger the protein system is, the higher the memory use of the GPU is. The performance of NVIDIA GTX580 is only half of the NVIDIAC2050. NVIDIA Tesla C2050 is can satisfy an even larger system simulation.

scholarly journals A lightweight approach to performance portability with targetDP

2016 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Alan Gray ◽  
Kevin Stratford
Keyword(s):  
Particle Physics ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Performance Portability ◽  
Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

scholarly journals Splotch

2016 ◽  
Vol 31 (6) ◽  
pp. 550-563
Author(s):  
Timothy Dykes ◽  
Claudio Gheller ◽  
Marzia Rivi ◽  
Mel Krokos
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Xeon Phi ◽  
The Many ◽  
Many Core ◽  
Performance Results ◽  
Graphics Processing ◽  
Performance Computing

With the increasing size and complexity of data produced by large-scale numerical simulations, it is of primary importance for scientists to be able to exploit all available hardware in heterogenous high-performance computing environments for increased throughput and efficiency. We focus on the porting and optimization of Splotch, a scalable visualization algorithm, to utilize the Xeon Phi, Intel’s coprocessor based upon the new many integrated core architecture. We discuss steps taken to offload data to the coprocessor and algorithmic modifications to aid faster processing on the many-core architecture and make use of the uniquely wide vector capabilities of the device, with accompanying performance results using multiple Xeon Phi. Finally we compare performance against results achieved with the Graphics Processing Unit (GPU) based implementation of Splotch.

scholarly journals Accelerating Molecular Dynamics Simulation Using Graphics Processing Unit

2010 ◽  
Vol 31 (12) ◽  
pp. 3639-3643 ◽  
Author(s):  
Hun-Joo Myung ◽  
Ryuji Sakamaki ◽  
Kwang-Jin Oh ◽  
Tetsu Narumi ◽  
Kenji Yasuoka ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Graphics Processing Unit ◽  
Dynamics Simulation ◽  
Processing Unit ◽  
Graphics Processing

Data Streaming Processing Window Joined With Graphics Processing Units (GPUs)

2021 ◽  
pp. 602-623
Author(s):  
Shen Lu ◽  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Graphics Processing Units ◽  
Data Stream ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Data Streaming ◽  
Large Scale Data ◽  
Graphics Processing

Big data is large-scale data and can be either discrete or continuous. This article entails research that discusses the continuous case of big data often called “data streaming.” More and more businesses will depend on being able to process and make decisions on streams of data. This article utilizes the algorithmic side of data stream processing often called “stream analytics” or “stream mining.” Data streaming Windows Join can be improved by using graphics processing unit (GPU) for higher performance computing. Data streams are generated by two independent threads: one thread can be used to generate Data Stream A, and the other thread can be used to generate Data Stream B. One would use a Windows Join thread to merge the two data streams, which is also the process of “Data Stream Window Join.” The Window Join process can be implemented in parallel that can efficiently improve the computing speed. Experiments are provided for Data Stream Window Joins using both static and dynamic data.

scholarly journals Molecular dynamics simulation of macromolecules using graphics processing unit

2010 ◽  
Vol 36 (14) ◽  
pp. 1131-1140 ◽  
Author(s):  
Ji Xu ◽  
Ying Ren ◽  
Wei Ge ◽  
Xiang Yu ◽  
Xiaozhen Yang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Graphics Processing Unit ◽  
Dynamics Simulation ◽  
Processing Unit ◽  
Graphics Processing

scholarly journals Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Congying Han ◽  
Tingting Feng ◽  
Guoping He ◽  
Tiande Guo
Keyword(s):  
Constrained Optimization ◽  
Large Scale ◽  
Line Search ◽  
Optimization Problems ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Sqp Method ◽  
Nonmonotone Technique ◽  
Variable Distribution ◽  
Graphics Processing

A modified parallel variable distribution (PVD) algorithm for solving large-scale constrained optimization problems is developed, which modifies quadratic subproblemQPlat each iteration instead of theQPl0of the SQP-type PVD algorithm proposed by C. A. Sagastizábal and M. V. Solodov in 2002. The algorithm can circumvent the difficulties associated with the possible inconsistency ofQPl0subproblem of the original SQP method. Moreover, we introduce a nonmonotone technique instead of the penalty function to carry out the line search procedure with more flexibly. Under appropriate conditions, the global convergence of the method is established. In the final part, parallel numerical experiments are implemented on CUDA based on GPU (Graphics Processing unit).

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