33 citations · 139 across the 42 of their papers we have counts for
42 papers · 1 filter
Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privac…
Coded matrix computation with gradient coding
Kyungrak Son, Aditya Ramamoorthy
Polynomial based approaches, such as the Mat-Dot and entangled polynomial codes (EPC) have been used extensively within coded matrix computations to obtain schemes with good recove…
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible…
Distributed Matrix Computations with Low-weight Encodings
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such str…
A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation
Anindya Bijoy Das, Aditya Ramamoorthy
The overall execution time of distributed matrix computations is often dominated by slow worker nodes (stragglers) within the clusters. Recently, different coding techniques have b…
Coded sparse matrix computation schemes that leverage partial stragglers
Anindya Bijoy Das, Aditya Ramamoorthy
Distributed matrix computations over large clusters can suffer from the problem of slow or failed worker nodes (called stragglers) which can dominate the overall job execution time…
Straggler-resistant distributed matrix computation via coding theory
Aditya Ramamoorthy, Anindya Bijoy Das, Li Tang
The current BigData era routinely requires the processing of large scale data on massive distributed computing clusters. Such large scale clusters often suffer from the problem of…
Numerically stable coded matrix computations via circulant and rotation matrix embeddings
Aditya Ramamoorthy, Li Tang
Polynomial based methods have recently been used in several works for mitigating the effect of stragglers (slow or failed nodes) in distributed matrix computations. For a system wi…
Efficient and Robust Distributed Matrix Computations via Convolutional Coding
Anindya B. Das, Aditya Ramamoorthy, Namrata Vaswani
Distributed matrix computations -- matrix-matrix or matrix-vector multiplications -- are well-recognized to suffer from the problem of stragglers (slow or failed worker nodes). Muc…
Asynchronous Coded Caching with Uncoded Prefetching
Hooshang Ghasemi, Aditya Ramamoorthy
Coded caching is a technique that promises huge reductions in network traffic in content-delivery networks. However, the original formulation and several subsequent contributions i…
Universally Decodable Matrices for Distributed Matrix-Vector Multiplication
Aditya Ramamoorthy, Li Tang, Pascal O. Vontobel
Coded computation is an emerging research area that leverages concepts from erasure coding to mitigate the effect of stragglers (slow nodes) in distributed computation clusters, es…
Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
Anindya Bijoy Das, Aditya Ramamoorthy
Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix c…
: Codes for Coded Computation that Leverage Stragglers
Anindya B. Das, Li Tang, Aditya Ramamoorthy
In distributed computing systems, it is well recognized that worker nodes that are slow (called stragglers) tend to dominate the overall job execution time. Coded computation utili…
Zero-error Function Computation on a Directed Acyclic Network
Ardhendu Tripathy, Aditya Ramamoorthy
We study the rate region of variable-length source-network codes that are used to compute a function of messages observed over a network. The particular network considered here is…
Leveraging Coding Techniques for Speeding up Distributed Computing
Konstantinos Konstantinidis, Aditya Ramamoorthy
Large scale clusters leveraging distributed computing frameworks such as MapReduce routinely process data that are on the orders of petabytes or more. The sheer size of the data pr…
Coded Caching Schemes with Reduced Subpacketization from Linear Block Codes
Li Tang, Aditya Ramamoorthy
Coded caching is a technique that generalizes conventional caching and promises significant reductions in traffic over caching networks. However, the basic coded caching scheme req…
Sum-networks from undirected graphs: construction and capacity analysis
Ardhendu Tripathy, Aditya Ramamoorthy
We consider a directed acyclic network with multiple sources and multiple terminals where each terminal is interested in decoding the sum of independent sources generated at the so…
Sum-networks from incidence structures: construction and capacity analysis
Ardhendu Tripathy, Aditya Ramamoorthy
A sum-network is an instance of a network coding problem over a directed acyclic network in which each terminal node wants to compute the sum over a finite field of the information…
Coded Caching with Low Subpacketization Levels
Li Tang, Aditya Ramamoorthy
Caching is popular technique in content delivery networks that allows for reductions in transmission rates from the content-hosting server to the end users. Coded caching is a gene…
Coded Caching for Networks with the Resolvability Property
Li Tang, Aditya Ramamoorthy
Coded caching is a recently proposed technique for dealing with large scale content distribution over the Internet. As in conventional caching, it leverages the presence of local c…
On Computation Rates for Arithmetic Sum
Ardhendu Tripathy, Aditya Ramamoorthy
For zero-error function computation over directed acyclic networks, existing upper and lower bounds on the computation capacity are known to be loose. In this work we consider the…
Capacity of Sum-networks for Different Message Alphabets
Ardhendu Tripathy, Aditya Ramamoorthy
A sum-network is a directed acyclic network in which all terminal nodes demand the `sum' of the independent information observed at the source nodes. Many characteristics of the we…
Improved Lower Bounds for Coded Caching
Hooshang Ghasemi, Aditya Ramamoorthy
Content delivery networks often employ caching to reduce transmission rates from the central server to the end users. Recently, the technique of coded caching was introduced whereb…
Fractional repetition codes with flexible repair from combinatorial designs
Oktay Olmez, Aditya Ramamoorthy
Fractional repetition (FR) codes are a class of regenerating codes for distributed storage systems with an exact (table-based) repair process that is also uncoded, i.e., upon failu…