Add to ORKGWe consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on $n$ workers each having a subset of the data. Distributed SGD may suffer from the effect of stragglers, i.e., slow or unresponsive workers who cause delays. One solution studied in the literature is to wait at each iteration for the responses of the fastest $k<n$ workers before updating the model, where $k$ is a fixed parameter. The choice of the value of $k$ presents a trade-off between the runtime (i.e., convergence rate) of SGD and the error of the model. Towards optimizing the error-runtime trade-off, we investigate distributed SGD with adaptive $k$. We first design an adaptive policy for varying $k$ that optimizes this trade...
We consider the distributed stochastic gradient descent problem, where a main node distributes gradi...
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the o...
We consider the distributed SGD problem, where a main node distributes gradient calculations among $...
As the size of models and datasets grows, it has become increasingly common to train models in paral...
SOTA decentralized SGD algorithms can overcome the bandwidth bottleneck at the parameter server by u...
International audienceOne of the most widely used methods for solving large-scale stochastic optimiz...
We study the asynchronous stochastic gradient descent algorithm for distributed training over n work...
Distributed implementations are crucial in speeding up large scale machine learning applications. Di...
When gradient descent (GD) is scaled to many parallel computing servers (workers) for large scale ma...
Stochastic optimization algorithms implemented on distributed computing architectures are increasing...
In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC...
In large-scale optimization problems, distributed asynchronous stochastic gradient descent (DASGD) i...
Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training...
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distribu...
Training Deep Neural Network is a computation-intensive and time-consuming task. Asynchronous Stocha...
We consider the distributed stochastic gradient descent problem, where a main node distributes gradi...
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the o...
We consider the distributed SGD problem, where a main node distributes gradient calculations among $...
As the size of models and datasets grows, it has become increasingly common to train models in paral...
SOTA decentralized SGD algorithms can overcome the bandwidth bottleneck at the parameter server by u...
International audienceOne of the most widely used methods for solving large-scale stochastic optimiz...
We study the asynchronous stochastic gradient descent algorithm for distributed training over n work...
Distributed implementations are crucial in speeding up large scale machine learning applications. Di...
When gradient descent (GD) is scaled to many parallel computing servers (workers) for large scale ma...
Stochastic optimization algorithms implemented on distributed computing architectures are increasing...
In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC...
In large-scale optimization problems, distributed asynchronous stochastic gradient descent (DASGD) i...
Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training...
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distribu...
Training Deep Neural Network is a computation-intensive and time-consuming task. Asynchronous Stocha...
We consider the distributed stochastic gradient descent problem, where a main node distributes gradi...
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the o...
We consider the distributed SGD problem, where a main node distributes gradient calculations among $...