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GTC ON-DEMAND

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Abstract:
Training machine and deep learning architectures remains challenging and resource-demanding. Much of the difficulty arises from stochastic mini-batch sampling, which results in noisy cost functions for which well-known deterministic approaches fail. Strategies proposed to overcome these challenges include basic fixed learning rates (or sub-gradient approaches), a priori selected learning-rate schedules, learning-rate cycling, and grid search to probabilistic line searches. Our talk will untangle available information used for training such as noisy function values and noisy gradients. We'll demonstrate the training benefits of exploiting noisy gradient information in more advanced training strategies for both Tensorflow and Pytorch on NVIDIA GeForce GTX 1080Ti and RTX 2080 Ti GPUs. We'll show a simulated dataset of the comminution process of an industrial ball mill, which we generated using a multi-GPU discrete element code.
Training machine and deep learning architectures remains challenging and resource-demanding. Much of the difficulty arises from stochastic mini-batch sampling, which results in noisy cost functions for which well-known deterministic approaches fail. Strategies proposed to overcome these challenges include basic fixed learning rates (or sub-gradient approaches), a priori selected learning-rate schedules, learning-rate cycling, and grid search to probabilistic line searches. Our talk will untangle available information used for training such as noisy function values and noisy gradients. We'll demonstrate the training benefits of exploiting noisy gradient information in more advanced training strategies for both Tensorflow and Pytorch on NVIDIA GeForce GTX 1080Ti and RTX 2080 Ti GPUs. We'll show a simulated dataset of the comminution process of an industrial ball mill, which we generated using a multi-GPU discrete element code.  Back
 
Topics:
Deep Learning & AI Frameworks, Algorithms & Numerical Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9647
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Abstract:
In this talk we will look at advances in the simulation of particulate systems in Computer Aided Engineering (CAE) applications. We will in particular be focusing on the Discrete Element Method (DEM) and the strides made in terms of the number of particles and particle shape using the GPU based code Blaze-DEM. A variety of industrial applications ranging from mining, agriculture, civil engineering to pharmaceuticals will be discussed. We will also touch on how we can leverage the next wave of GPU computing namely, half precession and tensor cores in scientific computing which is still predominantly double precision based. Finally we look at the work been done by various groups to create a multi-physics GPU based platform using Blaze-DEM.
In this talk we will look at advances in the simulation of particulate systems in Computer Aided Engineering (CAE) applications. We will in particular be focusing on the Discrete Element Method (DEM) and the strides made in terms of the number of particles and particle shape using the GPU based code Blaze-DEM. A variety of industrial applications ranging from mining, agriculture, civil engineering to pharmaceuticals will be discussed. We will also touch on how we can leverage the next wave of GPU computing namely, half precession and tensor cores in scientific computing which is still predominantly double precision based. Finally we look at the work been done by various groups to create a multi-physics GPU based platform using Blaze-DEM.  Back
 
Topics:
Computational Fluid Dynamics, Computer Aided Engineering
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8348
Streaming:
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