Coded distributed computing is an effective framework to improve the speed of distributed computing systems by mitigating stragglers (temporarily slow workers). In essence, coded computing allows replacing the computation assigned to a straggling worker by that at a faster worker by assigning redundant computations. Coded computing techniques proposed so far are mostly based on univariate polynomial coding. These codes are not very effective if storage and computation capacity across workers are heterogeneous and lose completely the work done by the straggling workers. For the particular problem of distributed matrix-matrix multiplication, we show how bivariate polynomial coding addresses these two issues
In distributed computing systems slow-working nodes, known as stragglers, can greatly extend the fin...
The problem considered is that of distributing machine learning operations of matrix multiplication ...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed...
Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed...
Coded computing is an effective technique to mitigate “stragglers” in large-scale and distributed ma...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
We consider the distributed computing problem of multiplying a set of vectors with a matrix. For thi...
In this paper, due to the important value in practical applications, we consider the coded distribut...
In distributed computing systems slow-working nodes, known as stragglers, can greatly extend the fin...
The problem considered is that of distributing machine learning operations of matrix multiplication ...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed...
Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed...
Coded computing is an effective technique to mitigate “stragglers” in large-scale and distributed ma...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
We consider the distributed computing problem of multiplying a set of vectors with a matrix. For thi...
In this paper, due to the important value in practical applications, we consider the coded distribut...
In distributed computing systems slow-working nodes, known as stragglers, can greatly extend the fin...
The problem considered is that of distributing machine learning operations of matrix multiplication ...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...