GALAMOST: GPU-accelerated large-scale molecular simulation toolkit
نویسندگان
چکیده
منابع مشابه
GPU-Accelerated Large Scale Analytics
In this paper, we report our research on using GPUs as accelerators for Business Intelligence(BI) analytics. We are particularly interested in analytics on very large data sets, which are common in today's real world BI applications. While many published works have shown that GPUs can be used to accelerate various general purpose applications with respectable performance gains, few attempts hav...
متن کامل‘GPU-accelerated Multiphysics Simulation’
In recent technology developments General Purpose computation on Graphics Processor Units (GPGPU) has been recognized a viable HPC technique. In this context, GPUacceleration is rooted in high-order Single Instruction Multiple Data (SIMD)/Single Instruction Multiple Thread (SIMT) vector-processing capability, combined with highspeed asynchronous I/O and sophisticated parallel cache memory archi...
متن کاملGPU Accelerated Stochastic Simulation
Through computational methods, biologists are able learn more about molecular biology by building accurate models. These models represent and predict the reactions among species populations within a system. One popular method to develop predictive models is to use a stochastic, Monte Carlo method developed by Gillespie called the stochastic simulation algorithm (SSA). Since this algorithm is ba...
متن کاملGPU-accelerated Chemical Similarity Assessment for Large Scale Databases
The assessment of chemical similarity between molecules is a basic operation in chemoinformatics, a computational area concerning with the manipulation of chemical structural information. Comparing molecules is the basis for a wide range of applications such as searching in chemical databases, training prediction models for virtual screening or aggregating clusters of similar compounds. However...
متن کاملGPU-accelerated and parallelized ELM ensembles for large-scale regression
The paper presents an approach for performing regression on large data sets in reasonable time, using an ensemble of extreme learning machines (ELMs). The main purpose and contribution of this paper are to explore how the evaluation of this ensemble of ELMs can be accelerated in three distinct ways: (1) training and model structure selection of the individual ELMs are accelerated by performing ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational Chemistry
سال: 2013
ISSN: 0192-8651
DOI: 10.1002/jcc.23365