Distributed Training of Deep Neural Networks - ORNL Computational
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Distributed Training of Deep Neural Networks - ORNL Computational
I Overview: distributed parallel training of DNNs. II Experimental ... Minimize Loss-Function via gradient descent (high dimensional and NON CONVEX!) Computed via Back ... Training Deep Neural Networks. Underlying .... Compression .... Network bandwidth is already exceeded by the SGD communication. â Worst ...
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability Janis Keuper Itwm.fraunhofer.de/ml Competence Center High Performance Computing Fraunhofer ITWM, Kaiserslautern, Germany
Outline I
Overview: distributed parallel training of DNNs
II Experimental Evaluation III Limitation I: Communication Bounds IV Limitation II: Skinny Matrix Multiplication V Limitation III: Data I/O
Training Deep Neural Networks Underlying Optimization Problem Computed via Back Propagation Algorithm: 1. feed forward and compute activation 2. error by layer 3. compute derivative by layer
Minimize Loss-Function via gradient descent (high dimensional and NON CONVEX!)
Optimization Problem By Stochastic Gradient Descent (SGD) forward 1. 2. 3. 4. 5. 6. 7.
Initialize weights W at random Take small random subset X (=batch) of the train data Run X through network (forward feed) Compute Loss Compute Gradient Propagate backwards through the network Update W
Repeat 2-8 until convergence
backward Layer 1 Layer 2
... Layer n-1 Layer n
Parallelization Common approaches to parallelize SGD for DL Parallelization of SGD is very hard: it is an inherently sequential algorithm 1. Start at some state t (point in a billion dimensional space) 2. Introduce t to data batch d1 3. Compute an update (based on the objective function) 4. Apply the update →t+1
d1
d2
d3
How to gain Speedup ? Make faster updates Make larger updates
State t+2 State t
State t+1
Parallelization Common approaches to parallelize SGD for DL Internal parallelization ●
●
Dense matrix multiplication: ● standard blas sgemm ● MKL, Open-Blas ● CuBlas Task parallelization for special Layers ● Cuda-CNN for fast convolutions
forward
backward Layer 1 Layer 2
... Layer n-1 Layer n
Parallelization Common approaches to parallelize SGD for DL External: data parallelization over the data batch Master 1. Split batch and send to workers
Layer 1
Layer 1
Layer 1
Layer 2
Layer 2
Layer 2
... Layer n-1 Layer n forward
... Layer n-1 Layer n forward
... Layer n-1 Layer n forward
Parallelization Common approaches to parallelize SGD for DL External: data parallelization over the data batch Master 1. Split batch and send to workers 2. Workers compute forward+backward and send gradients to master
Layer 1
Layer 1
Layer 1
Layer 2
Layer 2
Layer 2
... Layer n-1 Layer n forward backward
... Layer n-1 Layer n forward backward
... Layer n-1 Layer n forward backward
Parallelization Common approaches to parallelize SGD for DL External: data parallelization over the data batch Master 1. Split batch and send to workers 2. Workers compute forward+backward and send gradients to master 3. Master combines gradients and computes updates of W. Sends new W' to workers
Layer 1
Layer 1
Layer 1
Layer 2
Layer 2
Layer 2
... Layer n-1 Layer n forward backward
... Layer n-1 Layer n forward backward
... Layer n-1 Layer n forward backward
Parallelization Common approaches to parallelize SGD for DL External: data parallelization over the data batch Master 1. Split batch and send to workers 2. Workers compute forward+backward and send gradients to master 3. Master combines gradients and computes updates of W. Sends new W' to workers
Speedup
Experimental Evaluation Scaling Distributed Parallel Synchronous SGD Training 128
Experimental Setup: HPC Cluster with FDR Infiniband Interconnect K80 GPUs / Xeon E5 CPUs Intel Caffe Distribution
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