Fine-grained bulge-chasing kernels for strongly scalable parallel QR algorithms

Abstract

The bulge-chasing kernel in the small-bulge multi-shift QR algorithm for the non-symmetric dense eigenvalue problem becomes a sequential bottleneck when the QR algorithm is run in parallel on a multicore platform with shared memory. The duration of each kernel invocation is short, but the critical path of the QR algorithm contains a long sequence of calls to the bulge-chasing kernel. We study the problem of parallelizing the bulge-chasing kernel itself across a handful of processor cores in order to reduce the execution time of the critical path. We propose and evaluate a sequence of four algorithms with varying degrees of complexity and verify that a pipelined algorithm with a slowly shifting block column distribution of the Hessenberg matrix is superior. The load-balancing problem is non-trivial and computational experiments show that the load-balancing scheme has a large impact on the overall performance. We propose two heuristics for the load-balancing problem and also an effective optimization method based on local search. Numerical experiments show that speed-ups are obtained for problems as small as 40x40 on two different multicore architectures.