Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
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Rook Coding for Batch Matrix Multiplication
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
Coded Matrix Chain Multiplication
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single se...
Straggler-free Coding for Concurrent Matrix Multiplications
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to compute the multiplication of large matrices, ...
Leveraging Stragglers in Coded Computing with Heterogeneous Servers
With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are i...