Optimization and parallelization of B-spline based orbital evaluations in QMC on multi/many-core shared memory processors

TL;DR

This paper presents node-level optimizations and parallelization strategies for B-spline orbital evaluations in Quantum Monte Carlo simulations, significantly improving performance on various modern multi-core architectures.

Contribution

It introduces data layout transformation, cache-aware blocking, and nested threading techniques to enhance B-spline evaluations in QMC, enabling better scalability and efficiency.

Findings

Up to 10x performance improvement on CPUs and accelerators.

Nearly ideal parallel efficiency up to 16 threads on KNL.

Over 4.5x overall speedup of miniQMC application.

Abstract

B-spline based orbital representations are widely used in Quantum Monte Carlo (QMC) simulations of solids, historically taking as much as 50% of the total run time. Random accesses to a large four-dimensional array make it challenging to efficiently utilize caches and wide vector units of modern CPUs. We present node-level optimizations of B-spline evaluations on multi/many-core shared memory processors. To increase SIMD efficiency and bandwidth utilization, we first apply data layout transformation from array-of-structures to structure-of-arrays (SoA). Then by blocking SoA objects, we optimize cache reuse and get sustained throughput for a range of problem sizes. We implement efficient nested threading in B-spline orbital evaluation kernels, paving the way towards enabling strong scaling of QMC simulations. These optimizations are portable on four distinct cache-coherent architectures and result in up to 5.6x performance enhancements on Intel Xeon Phi processor 7250P (KNL), 5.7x on Intel Xeon Phi coprocessor 7120P, 10x on an Intel Xeon processor E5v4 CPU and 9.5x on BlueGene/Q processor. Our nested threading implementation shows nearly ideal parallel efficiency on KNL up to 16 threads. We employ roofline performance analysis to model the impacts of our optimizations. This work combined with our current efforts of optimizing other QMC kernels, result in greater than 4.5x speedup of miniQMC on KNL.