We can, for example, help with setting up scripts for running your jobs and help with trouble shooting if.
NSC can help you with how to run your Gaussian jobs and to some extend help you with how to set up your Gaussian jobs. Gaussian is a versatile program for electronic structure modelling. We won't judge on it, because this "slightly" is really marginal difference. Using Gaussian & GaussView on Tetralith and Sigma. However, the results generally became slightly worse (for almost all jobs). Depending on the difference, apply more or less of this blur to the output image. We have also tried to use specific proprietary CPU firmware on Linux (Debian package i3fw). The idea is simple: compare the lightness of the original image with the lightness of a blur.
Also, parallelization improves results much more on Windows than on Linux (but they are still worse). A technical conclusion is that we see clearly that not so much of computation can be parallelized, actually. Therefore, third section of the first table and second section in the second table are not corresponding to reality.Ī practical conclusion is that on Unix™ Gaussian™ runs faster than on Windows™. This was confirmed just by comparing time of creation and the last modify time for each file: on Linux this time span was far shorter than it would be if we sum up all "Job cpu times". My personal conclusion is that on Unix™, Gaussian™ returns calculation time as if single CPU core was used on Unix™, but on Windows™, the actual computation time is returned.
run the module spider command with the module’s full version label. For detailed information, visit the Gaussian website. At least, these numbers are comparable.Ĭalculation with 4 CPU cores, norming to single core Gaussian is a suite of electronic-structure codes. But if we divide "Job cpu time" values by the number of CPU treads for Linux values, the situation looks more logical. GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. Various calculation types were tested for some of our molecules.Īt first glance, there is a big inconsistency in results: for single-core computations Gaussian 09 on Linux performs significantly better than on Windows™, but for multiple-core test the situation seems to be opposite. The Scikit-learn provides to implement the Gaussian Naïve Bayes algorithm for classification. Usually people test computational software on a single CPU core, but we ran both variants. As the name suggest, Gaussian Naïve Bayes classifier assumes that the data from each label is drawn from a simple Gaussian distribution. Note: Please, check which revision you are running in the program output. See Official Gaussian Citation for instructions.
It has Intel ® i3 2-core CPU (4 threads), 4 GB of RAM and Samsung ST500 hard drive – standard desktop PC, actually. Gaussian is available to users at HPC2N under the condition that published work include citation of the program.
3 It has been continuously updated since then.
Label,Score predict (Mdl,X) also returns classification scores for both classes. Total running time of the script: ( 0 minutes 0.As our Institute has licenses for Gaussian™ 09, Revision D.01 on both Windows™ and Linux ®, it was interesting to compare performance on a single machine where both operating systems are installed. Gaussian is a computer program for computational chemistry initially released in 1970 by John Pople 1 2 and his research group at Carnegie-Mellon University as Gaussian 70. Label predict (Mdl,X) returns a vector of predicted class labels for the predictor data in the matrix or table X, based on the binary Gaussian kernel classification model Mdl. draw_networkx_labels ( G, pos, labels, font_size = 22, font_color = "whitesmoke" ) plt. spring_layout ( G, seed = 3113794652 ) # positions for all nodes # nodes options = labels = r "$a$" labels = r "$b$" labels = r "$c$" labels = r "$d$" labels = r "$\alpha$" labels = r "$\beta$" labels = r "$\gamma$" labels = r "$\delta$" nx. Import matplotlib.pyplot as plt import networkx as nx G = nx.