The NAG Library for SMP & Multicore has been extended and now includes even more parallelised algorithms enabling users of shared memory systems to solve their numerical problems faster.
Numerical Algorithms Group (NAG) says newly parallelised routines are applicable to problems in the areas of global optimisation, matrix functions and statistics, including Gaussian mixture model, Brownian bridge and univariate inhomogeneous time series.
This update to the NAG Library for SMP & Multicore will greatly benefit software developers wishing to easily exploit the performance potential of multicore systems, the company says, without having to learn the intricacies of parallel programming.
Speaking of the latest release Reinhold Bader of Leibniz Supercomputing Centre (LRZ) in Garching, Germany, said: 'The NAG Library for SMP & Multicore has been deployed on the flagship HPC systems at LRZ for more than two decades and we welcome the added functionality in the Mark 24 release. The superior scaling of the provided computational kernels in shared memory can provide a significant performance advantage to HPC applications that use hybrid parallelism.
'Furthermore, we intend to test a specially tuned version of this Library also on an upcoming Intel Xeon Phi installation later this year.'