In distributed computing systems slow-working nodes, known as stragglers, can greatly extend the finishing time. Coded computing is a technique that enables straggler-resistant computation. This thesis first develops a conceptual framework that unifies existing coded matrix multiplication techniques. In this framework the division of work amongst workers in different coding techniques is presented as a cuboid partitioning problem. Building on this framework, we then propose three methods of hierarchical coded computing: Bit-Interleaved Coded Computing (BICC), Multilevel Coded Computing (MLCC), and Hybrid Hierarchical Coded Computing (HHCC). In hierarchical coding the workers process a sequence (a hierarchy) of ordered subtasks and transmit ...
We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set ...
In distributed computing systems, it is well recognized that worker nodes that are slow (called stra...
The current BigData era routinely requires the processing of large scale data on massive distributed...
The performance of distributed computing is restricted by the slowest worker nodes, known as straggl...
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...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...
Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed...
Coded computation techniques provide robustness against straggling workers in distributed computing....
Coded computation techniques provide robustness against straggling workers in distributed computing....
The overall execution time of distributed matrix computations is often dominated by slow worker node...
We consider the distributed computing problem of multiplying a set of vectors with a matrix. For thi...
Coded computing is an effective technique to mitigate “stragglers” in large-scale and distributed ma...
As an increasing number of modern big data systems utilize horizontal scaling,the general trend in t...
We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set ...
In distributed computing systems, it is well recognized that worker nodes that are slow (called stra...
The current BigData era routinely requires the processing of large scale data on massive distributed...
The performance of distributed computing is restricted by the slowest worker nodes, known as straggl...
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...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...
Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed...
Coded computation techniques provide robustness against straggling workers in distributed computing....
Coded computation techniques provide robustness against straggling workers in distributed computing....
The overall execution time of distributed matrix computations is often dominated by slow worker node...
We consider the distributed computing problem of multiplying a set of vectors with a matrix. For thi...
Coded computing is an effective technique to mitigate “stragglers” in large-scale and distributed ma...
As an increasing number of modern big data systems utilize horizontal scaling,the general trend in t...
We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set ...
In distributed computing systems, it is well recognized that worker nodes that are slow (called stra...
The current BigData era routinely requires the processing of large scale data on massive distributed...