  Pairwise association measure is an important operation in data analytics.
Kendall's tau coefficient is one widely used correlation coefficient
identifying non-linear relationships between ordinal variables. In this paper,
we investigated a parallel algorithm accelerating all-pairs Kendall's tau
coefficient computation via single instruction multiple data (SIMD) vectorized
sorting on Intel Xeon Phis by taking advantage of many processing cores and
512-bit SIMD vector instructions. To facilitate workload balancing and overcome
on-chip memory limitation, we proposed a generic framework for symmetric
all-pairs computation by building provable bijective functions between job
identifier and coordinate space. Performance evaluation demonstrated that our
algorithm on one 5110P Phi achieves two orders-of-magnitude speedups over
16-threaded MATLAB and three orders-of-magnitude speedups over sequential R,
both running on high-end CPUs. Besides, our algorithm exhibited rather good
distributed computing scalability with respect to number of Phis. Source code
and datasets are publicly available at http://lightpcc.sourceforge.net.
