Codes for Distributed Machine Learning

The problem considered is that of distributing machine learning operations of matrix multiplication and multivariate polynomial evaluation among computer nodes a.k.a worker nodes some of whom don’t return their outputs or return erroneous outputs. The thesis can be divided into three major parts. In the first part of the thesis, a fault tolerant setup where t worker nodes return erroneous values is considered. For an additive random Gaussian error model, it is shown that for all t < N − K, errors can be corrected with probability 1 for polynomial codes. In the second part of the thesis, a class of codes called random Khatri-Rao-Product (RKRP) codes for distributed matrix multiplication in the presence of stragglers is proposed. The main advantage of the proposed codes is that decoding of RKRP codes is highly numerically stable in comparison to decoding of Polynomial codes [67] and decoding of the recently proposed OrthoPoly codes [18]. It is shown that RKRP codes are maximum distance separable with probability 1. In the third part of the thesis, the problem of distributed multivariate polynomial evaluation (DPME) is considered, where Lagrange Coded Computing (LCC) [66] was proposed as a coded computation scheme to provide resilience against stragglers for the DPME problem. A variant of the LCC scheme, termed Product Lagrange Coded Computing (PLCC) is proposed by combining ideas from classical product codes and LCC. The main advantage of PLCC is that they are more numerically stable than LCC

Codes for Distributed Machine Learning

The problem considered is that of distributing machine learning operations of matrix multiplication and multivariate polynomial evaluation among computer nodes a.k.a worker nodes some of whom don’t return their outputs or return erroneous outputs. The thesis can be divided into three major parts. In the first part of the thesis, a fault tolerant setup where t worker nodes return erroneous values is considered. For an additive random Gaussian error model, it is shown that for all t < N − K, errors can be corrected with probability 1 for polynomial codes. In the second part of the thesis, a class of codes called random Khatri-Rao-Product (RKRP) codes for distributed matrix multiplication in the presence of stragglers is proposed. The main advantage of the proposed codes is that decoding of RKRP codes is highly numerically stable in comparison to decoding of Polynomial codes [67] and decoding of the recently proposed OrthoPoly codes [18]. It is shown that RKRP codes are maximum distance separable with probability 1. In the third part of the thesis, the problem of distributed multivariate polynomial evaluation (DPME) is considered, where Lagrange Coded Computing (LCC) [66] was proposed as a coded computation scheme to provide resilience against stragglers for the DPME problem. A variant of the LCC scheme, termed Product Lagrange Coded Computing (PLCC) is proposed by combining ideas from classical product codes and LCC. The main advantage of PLCC is that they are more numerically stable than LCC

Codes for Distributed Machine Learning

The problem considered is that of distributing machine learning operations of matrix multiplication and multivariate polynomial evaluation among computer nodes a.k.a worker nodes some of whom don’t return their outputs or return erroneous outputs. The thesis can be divided into three major parts. In the first part of the thesis, a fault tolerant setup where t worker nodes return erroneous values is considered. For an additive random Gaussian error model, it is shown that for all t < N − K, errors can be corrected with probability 1 for polynomial codes. In the second part of the thesis, a class of codes called random Khatri-Rao-Product (RKRP) codes for distributed matrix multiplication in the presence of stragglers is proposed. The main advantage of the proposed codes is that decoding of RKRP codes is highly numerically stable in comparison to decoding of Polynomial codes [67] and decoding of the recently proposed OrthoPoly codes [18]. It is shown that RKRP codes are maximum distance separable with probability 1. In the third part of the thesis, the problem of distributed multivariate polynomial evaluation (DPME) is considered, where Lagrange Coded Computing (LCC) [66] was proposed as a coded computation scheme to provide resilience against stragglers for the DPME problem. A variant of the LCC scheme, termed Product Lagrange Coded Computing (PLCC) is proposed by combining ideas from classical product codes and LCC. The main advantage of PLCC is that they are more numerically stable than LCC

Codes for Distributed Machine Learning

The problem considered is that of distributing machine learning operations of matrix multiplication and multivariate polynomial evaluation among computer nodes a.k.a worker nodes some of whom don’t return their outputs or return erroneous outputs. The thesis can be divided into three major parts. In the first part of the thesis, a fault tolerant setup where t worker nodes return erroneous values is considered. For an additive random Gaussian error model, it is shown that for all t < N − K, errors can be corrected with probability 1 for polynomial codes. In the second part of the thesis, a class of codes called random Khatri-Rao-Product (RKRP) codes for distributed matrix multiplication in the presence of stragglers is proposed. The main advantage of the proposed codes is that decoding of RKRP codes is highly numerically stable in comparison to decoding of Polynomial codes [67] and decoding of the recently proposed OrthoPoly codes [18]. It is shown that RKRP codes are maximum distance separable with probability 1. In the third part of the thesis, the problem of distributed multivariate polynomial evaluation (DPME) is considered, where Lagrange Coded Computing (LCC) [66] was proposed as a coded computation scheme to provide resilience against stragglers for the DPME problem. A variant of the LCC scheme, termed Product Lagrange Coded Computing (PLCC) is proposed by combining ideas from classical product codes and LCC. The main advantage of PLCC is that they are more numerically stable than LCC