Factorization of a dense symmetric indefinite matrix is a key computational kernel in many scientific and engineering simulations. However, there is no scalable factorization algorithm that takes advantage of the symmetry and guarantees numerical stability through pivoting at the same time. This is because such an algorithm exhibits many of the fundamental challenges in parallel programming like irregular data accesses and irregular task dependencies. In this paper, we address these challenges in a tiled implementation of a blocked Aasen’s algorithm using a dynamic scheduler. To fully exploit the limited parallelism in this left-looking algorithm, we study several performance enhancing techniques; e.g., parallel reduction to update a panel, tall-skinny LU factorization algorithms to factorize the panel, and a parallel implementation of symmetric pivoting. Our performance results on up to 48 AMD Opteron processors demonstrate that our implementation obtains speedups of up to 2.8 over MKL, while losing only one or two digits in the computed residual norms.
2D Accelerators Algorithms Architectures Arrays Big Data Bootstrapping C++ Cache Partitioning Cancer Careers Chisel Communication Computer Architecture CTF DIABLO Efficiency Energy FPGA GAP Gaussian Elimination Genomics GPU Hardware HLS Lower Bounds LU Matrix Multiplication Memory Multicore Oblivious Open Space OS Parallelism Parallel Reduction Performance PHANTOM Processors Python Research Centers RISC-V SEJITS Tall-Skinny QR Technical Report Test generation