Background: Nuclear pasta matter, emerging due to the competition between the long-range Coulomb force and the short-range strong force, is believed to be present in astrophysical scenarios, such as neutron stars and corecollapse supernovae. Its structure can have a high impact e.g. on neutrino transport or the tidal deformability of neutron stars.Purpose: We investigate the impact of nuclear pasta on neutrino interactions and compare the results to uniform matter.Method: We calculate the elastic and inelastic static structure factors for nuclear pasta matter using density functional theory (DFT), which contain the main nuclear input for neutrino scattering.Results: Each pasta structure leaves a unique imprint in the elastic structure factor and it is largely enhanced. The inelastic structure factors are very similar for all configurations.Conclusion: Nuclear pasta has a noticeable impact on neutrino neutral-current scattering opacities. While for inelastic reactions the cross section is reduced, the elastic coherent scattering increases dramatically. The effect can be of importance for the cooling of neutron stars as well as for core-collapse supernova models.

Abstract-As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. We consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We present techniques to significantly improve the SpMM and the transpose operation SpMM T by using the compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.