Accelerated Sparse Matrix-Based Computation of Electromagnetic Transients

Algorithms for Distributed Computation of Electromagnetic Transients towards PC Cluster Based Real-Time Simulations

This paper describes algorithms for the distributed computation of electromagnetic transients in a power system. Conventional simulators based on parallel computing utilize traveling-time delays due to Bergeron’s representation of transmission lines in order to divide the network of interest into subnetworks, and each subnetwork is assigned to a single CPU to solve the decoupled sets of the cir...

GPU-Accelerated Sparse Matrix-Matrix Multiplication by Iterative Row Merging

We present an algorithm for general sparse matrix-matrix multiplication (SpGEMM) on many-core architectures, such as GPUs. SpGEMM is implemented by iterative row merging, similar to merge sort, except that elements with duplicate column indices are aggregated on the fly. The main kernel merges small numbers of sparse rows at once using sub-warps of threads to realize an early compression effect...

GDAGsim: Sparse matrix algorithms for Bayesian computation

GDAGsim is a software library which can be used to carry out conditional sampling of linear Gaussian directed acyclic graph models, and hence can be used for the implementation of efficient block MCMC samplers for such models. This paper examines the software library and its design, and how it can be applied to problems in Bayesian inference.

Data Sparse Matrix Computation - Lecture 11

2 Randomized algorithms 4 2.1 Randomized low-rank factorization . . . . . . . . . . . . . . . . . 4 2.2 How to find such a Q . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 How to construct Q with randomness . . . . . . . . . . . . . . . . 5 2.4 An adaptive randomized range finder algorithm . . . . . . . . . . 6 2.5 Example of implementation of the adaptive range approximation method . ...

Data Sparse Matrix Computation - Lecture 20