Understanding Chimera: A Pipeline Model Parallelism Scheme
Chimera is a model parallelism scheme designed to train large-scale models efficiently. Its unique feature is the combination of bidirectional pipelines, namely down and up pipelines, to accomplish the task. The aim is to execute a large number of micro-batches by each worker within a training iteration with the minimum of four pipeline stages.
How Chimera Pipeline Works?
Chimera pipeline, as shown in the figure, consists of four pip
GPipe is a distributed model parallel method for neural networks that allows for faster and more efficient training of deep learning models.
What is GPipe?
GPipe is a distributed model parallel method for neural networks that was developed by Google to improve the efficiency and speed of training deep learning models. It works by dividing the layers of a model into cells, which can then be distributed across multiple accelerators. By doing this, GPipe allows for batch splitting, which divides
Have you ever been frustrated by slow or inefficient neural network computations? If so, you may be interested in GShard, a new method for improving the performance of deep learning models.
What is GShard?
GShard is an intra-layer parallel distributed method developed by researchers at Google. Simply put, it allows for the parallelization of computations within a single layer of a neural network. This can drastically improve the speed and efficiency of model training and inference.
One of th
Overview of Mesh-TensorFlow
Mesh-TensorFlow is a programming language used to distribute tensor computations. Like data-parallelism that splits tensors and operations along the "batch" dimension, Mesh-TensorFlow can split any dimensions of a multi-dimensional mesh of processors. This allows users to specify the exact dimensions to be split across any dimensions of the mesh of processors.
What is Tensor Computation?
Tensor computation is a concept in which matrices and higher-dimensional arra
PipeDream-2BW: A Powerful Method for Parallelizing Deep Learning Models
If you're at all involved in the world of deep learning, you know that training a large neural network can take hours or even days. The reason for this is that neural networks require a lot of computation, and even with specialized hardware like GPUs or TPUs, it can be difficult to get the job done quickly. That's where parallelization comes in - by breaking up the work and distributing it across multiple machines, we can s
What is PipeDream?
PipeDream is a parallel strategy used for training large neural networks. It is an asynchronous pipeline parallel strategy that helps improve the parallel training throughput, by adding inter-batch pipelining to intra-batch parallelism. This strategy helps reduce the amount of communication needed during training, while also better overlapping computation with communication.
How does PipeDream work?
PipeDream was developed to help with the training of very large neural net
Pipelined Backpropagation is a special technique used in machine learning to train neural networks. It is a computational algorithm that helps in weight updates and makes the process faster and more efficient. The main objective of this algorithm is to reduce overhead by updating weights without draining the pipeline first.
What is Pipelined Backpropagation?
Pipelined Backpropagation is an asynchronous pipeline parallel training algorithm that was first introduced by Petrowski et al in 1993.
What is PipeMare?
PipeMare is a method for training large neural networks that use two distinct techniques to optimize their performance. The first technique is called learning rate rescheduling, and the second technique is called discrepancy correction. Together, these two techniques help to create an asynchronous (bubble-free) pipeline parallel method for training large neural networks.
How Does PipeMare Work?
PipeMare works by optimizing the training of large neural networks through a com
Overview of Tofu
Tofu is a system designed to partition large deep neural network (DNN) models across multiple GPU devices, reducing the memory footprint for each GPU. The system is specially designed to partition a dataflow graph used by platforms like TensorFlow and MXNet, which are frameworks used for building and training DNN models.
Tofu makes use of a recursive search algorithm to partition different operators in a dataflow graph in a way that minimizes the total communication cost. This