Geforce gtx titan x gpu

Manufactured by NVIDIA

The GeForce GTX TITAN X is a high-performance graphics processing unit (GPU) developed by NVIDIA. It is equipped with the latest GPU architecture and is designed to deliver exceptional performance for demanding applications, such as video editing, 3D rendering, and high-end gaming. The GeForce GTX TITAN X features a large number of CUDA cores, high-speed memory, and advanced power management technologies, making it a powerful tool for professional and enthusiast users.

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Lab products found in correlation

Tesla k80 gpu, by NVIDIA (1 mentions) Matlab, by MathWorks (1 mentions)

Close spelling variants of this product

Titan X GPU (39 citations) Titan X (33 citations) GeForce GTX Titan X (19 citations) GeForce GTX TITAN (6 citations) GTX TITAN X GPU (2 citations) GTX Titan X (2 citations) TITAN GPUs (2 citations)

10 protocols using geforce gtx titan x gpu

Optimizing Solubility Prediction with ANN

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Additionally, the log₁₀[S_∞] prediction results for the optimization set were compared to the COSMO-RS predictions from [25 (link)] COSMO-RS log₁₀[S_∞] was created by choosing predicted IDAC data from column IDAC (calcd) of Table 4 in ESI of Paduszyński article [25 (link)]. Then, log₁₀[S_∞] was calculated in the same manner as SelinfDB data.
A Linux (Centos 6) cluster with SLURM was used for the ANN development, optimization and external test set prediction, as well as prediction of aniline + dodecane breakers. The nodes were Intel Xeon E5-2630 CPUs and NVIDIA GeForce GTX TITAN X GPUs. NVIDIA CUDA libraries, that were needed for running keras and tensorflow, are version 10.1 (Fig. 1).Fig. 1

Workflow scheme

Klimenko K, & Carrera G.V. (2021). QSPR modeling of selectivity at infinite dilution of ionic liquids. Journal of Cheminformatics, 13, 83.

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Lattice Microbes v2.3a GPU-accelerated Simulations

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All simulations were performed using Lattice Microbes v2.3a on a local cluster consisting of three Cirrascale GB5600 Multi-GPU nodes, two equipped with 8 NVIDIA GeForce GTX TITAN X GPUs, and one equipped with 4 NVIDIA Tesla K80 GPUs. Analysis of simulation data was performed in the Jupyter environment⁵⁵ using the SciPy Stack⁵⁶.
Lattice Microbes 2.3a expands the capability of the GPU-based MPD-RDME algorithm^{43 (link)} by adding support for extended capacity lattices where sixteen different particles may occupy each lattice site. Previous versions allowed up to eight particles per site. When more particles occupy a lattice site than capacity allows, the extra particles are said to have “overflowed” and special handling is required to rectify the situation. A procedure on the CPU locates candidate neighboring lattice sites and moves the excess particles into them. This is costly, as the lattice must be copied to host memory and then back to the GPU after overflows are corrected. Additionally, a higher capacity lattice incurs a cost as well, as the diffusion and reaction operators must access a larger amount of memory to account for the greater number of particles. However, simulations that experience overflows on a frequent basis benefit from the greater capacity, as the cost of accessing more memory is offset by the savings gained from not needing to perform overflow handling.

Earnest T.M., Cole J.A., Peterson J.R., Hallock M.J., Kuhlman T.E, & Luthey-Schulten Z. (2016). Ribosome biogenesis in replicating cells: integration of experiment and theory. Biopolymers, 105(10), 735-751.

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Molecular Dynamics Simulation of GTP-Protein Interactions

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In the molecular dynamics simulations, the
atomic interactions were described by the AMBER99SB-ILDN^{25 (link)} force field, extended with optimized parameters
for the triphosphate chain of GTP.^{26 (link)} Long-range
electrostatic interactions were treated via the particle mesh Ewald
method.^{27 (link)} The short-range nonbonded interactions
(e.g., electrostatics and van der Waals interactions) were cut off
at 1.1 nm.
All of the equilibration was performed with GROMACS
v.4.6.5.^{28 (link)} The leap-frog integrator was
used with a time step of 2 fs. Temperature was kept constant at 310
K using the v-rescale thermostat^{29 (link)} using
two temperature coupling groups: the first group consisted of the
protein, GTP and Mg²⁺, while the second group consisted
of water, Na⁺, and Cl^–. The pressure
was kept constant using the Parrinello–Rahman barostat^{30 (link)} at a pressure of 1 bar. All bond lengths were
constrained using the LINCS algorithm.^{31 (link)}The 100 ns production runs were performed with OpenMM (7.1.0.dev-5e53567).^{32 (link)} The constraints were changed to only affect
bonds including a hydrogen atom, using SHAKE,^{33 (link)} the integrator was the Velocity Verlet with velocity randomization
(VVVR) integrator^{34 (link)} from OpenMMTools v.0.14^{35 (link)} and the barostat was the Monte Carlo barostat.^{36 (link)} The production simulations were run using the
CUDA platform of OpenMM on NVIDIA GeForce GTX TITAN X GPUs.

Roet S., Hooft F., Bolhuis P.G., Swenson D.W, & Vreede J. (2022). Path Sampling Simulations Reveal How the Q61L Mutation Alters the Dynamics of KRas. The Journal of Physical Chemistry. B, 126(48), 10034-10044.

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Transformer-based Reagent and Product Prediction

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For both reagent and product prediction, we used the same transformer settings and hyperparameters used by Schwaller et al.:^{5 (link)} Adam optimizer,^{50 (link)} Noam learning rate schedule²⁵ with 8000 warmup steps, a batch size of around 4096 tokens, accumulation count 4 and dropout rate 0.1. We did not conduct weight averaging across checkpoints. All the models were trained on an Nvidia GeForce GTX TITAN X GPU with 12 GB memory.

Andronov M., Voinarovska V., Andronova N., Wand M., Clevert D.A, & Schmidhuber J. (2023). Reagent prediction with a molecular transformer improves reaction data quality. Chemical Science, 14(12), 3235-3246.

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Multi-Core and GPU-Accelerated STEP Reconstruction

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Scanner RAW format multichannel k-space data were exported to a separate workstation for offline processing. STEP reconstruction was carried out using Matlab (Mathworks Inc, Natick, USA) on a 64-bit Windows workstation fitted with a multi-core CPU and a GPU video-card. Workstation specifications are as follows: Intel Xeon Processor 2.4 GHZ eight cores, 32 GB RAM, 1 GB hard drive, and NVIDIA GeForce GTX TITAN X GPU with 3584 cores. The Matlab Parallel Computing Toolbox was employed to recruit all eight CPU cores for parallel processing. The two images from SNAP (inversion-recovery and reference) were jointly reconstructed and SNAP-corrected real images were calculated.

Balu N., Zhou Z., Hippe D.S., Hatsukami T., Mossa-Basha M, & Yuan C. (2019). Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy. Magma (New York, N.y.), 32(3), 343-357.

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3D CNN for MRI Classification

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A new 3D CNN was trained from scratch using the MRI volumes from the two considered datasets.
Our 3D CNN architecture was composed of the following 3D layers: (1) convolutional layer, (2) Rectified Linear Unit (ReLU) layer, (3) max-pooling layer, (4) fully-connected layer, (5) soft-max layer, and (6) final classification layer.
The size of the 3D input layer was set equal to 28 by 28 by 121. The stride of convolutional layers and the stride of the max-pooling layers were set equal to 4.
In order to perform the training and optimization of the network, MRI scans were then split up into patches of size 28 by 28 by 121, without overlap, and the final classification was obtained by merging (via sum rule) the classification output of all the patches that compose a given MRI volume.
Training and optimization of the network were performed using Stochastic Gradient Descent with Momentum (SGDM) optimization, initial learning rate equal to 0.0001. Analyses were performed on a computing system with a dedicated Nvidia GeForce GTX Titan X GPU (12 GB memory size).

Nanni L., Interlenghi M., Brahnam S., Salvatore C., Papa S., Nemni R, & Castiglioni I. (2020). Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease. Frontiers in Neurology, 11, 576194.

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Automated Leaf Image-based Egg Quantification

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The acquired leaf images were analyzed using an automatic egg quantification algorithm, as illustrated in Fig. 2. The egg quantification algorithm was developed using Python 3.8.3 programming language, with the support of OpenCV image processing library [5 ], PyTorch deep learning library [24 ] and MLFlow machine learning tracking library [6 ]. All computations were performed using a desktop computer running under Ubuntu 22.04 operating system, with an Intel Xeon E5-1650 processor, NVIDIA GeForce GTX Titan X GPU, and 16 GB RAM. This section discusses the methods and theoretical considerations in developing the algorithm.Fig. 2

Automated egg quantification algorithm

Devi M.G., Rustia D.J., Braat L., Swinkels K., Espinosa F.F., van Marrewijk B.M., Hemming J, & Caarls L. (2023). Eggsplorer: a rapid plant–insect resistance determination tool using an automated whitefly egg quantification algorithm. Plant Methods, 19, 49.

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Deep Reinforcement Learning on Atari Games

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The test-bed in our paper is the Atari Learning Environment (Bellemare et al., 2013 (link)). Four DRL agents were trained on the games MsPacman (simplified to Pac-Man in this work), Space Invaders, Frostbite, and Breakout using the Deep Q-Network (DQN) (Mnih et al., 2015 (link)) implementation of the OpenAI Baselines Framework (Dhariwal et al., 2017 ). We chose the DQN because it is the most basic DRL architecture which many other DRL agents build upon. The games were selected because the DQN performs very well on Breakout and Space Invaders but performs badly on Frostbite and Pac-Man. The agent observes the last 4 frames of the game and then chooses an action a from a pool of possible actions

A

. Hereby, each frame is down-sampled and greyscaled resulting in 84 × 84 × 4 input images. The reward is given by the change in in-game score since the last state, which we scaled such that the minimal possible reward is 1. All experiments were done on the same machine with an Nvidia GeForce GTX TITAN X GPU to ensure comparability of the results. Our code is available online¹.

Huber T., Limmer B, & André E. (2022). Benchmarking Perturbation-Based Saliency Maps for Explaining Atari Agents. Frontiers in Artificial Intelligence, 5, 903875.

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Nested Cross-Validation for Deep Learning

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All models were trained using nested 5-fold cross-validation for 200 epochs with early stopping, monitoring the validation loss. Adam optimizer was used, with a learning rate of 0.001. The code was developed in Python 3.7; the neural networks were designed using Pytorch 1.11 and the models were trained on an NVIDIA^® GeForce GTX TITAN X GPU.

Montemurro A., Jessen L.E, & Nielsen M. (2022). NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Frontiers in Immunology, 13, 1055151.

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DeepBiome: Deep Learning for Microbiome Analysis

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DeepBiome is implemented in Python 3.6 based the TensorFlow (Abadi et al., 2015 (Abadi et al., , 2016) ) and Keras (Chollet et al., 2015) framework. It can be built on Python 3.4, 3.5, and 3.6. All simulations are performed using a workstation equipped with Intel(R) Xeon(R) CPU E5-2650 v4 processor with 24 cores @ 2.20GHz and one NVIDIA GeForce GTX TITAN X GPU with 3072 CUDA cores @ 1 GHz and 12GB memory. DeepBiome required 290 ± 69 seconds to fully train the network for one replicate with 1000 samples, 50 mini-batches and 5000 epochs. For the same data, DNN took 282 ± 67 second and ℓ 1 -DNN took 282 ± 67 second. DeepBiome and all other deep learning approaches took less than 0.004 seconds for prediction. All real data analysis is performed on a MacBook Pro with 2.8GHz Intel Core i7 processor and 16GB 2133MHz LPDDR3 memory.

Zhai J., Choi Y., Chen Y., Knox K.S., Twigg H.L., Won J., Zhou H., & Zhou J. (2020). DeepBiome: a phylogenetic tree informed deep neural network for microbiome data analysis.

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