NAG Enables Multicore Performance Improvements on HECToR

By DE Editors

The Numerical Algorithms Group (NAG) has announced new HPC performance milestones including up to four times better performance with multicore optimization for materials science and quantum Monte Carlo applications, and reductions of up to 25 percent in runtimes with I/O tuning for an ocean modeling application. These are the early results of NAG’s distributed Computational Science and Engineering (dCSE)  support program for HECToR, the UK’s national supercomputing facility.

In the first project to complete, CASTEP was enhanced with band-parallelism to allow the code to scale to more than 1,000 cores. The speed of CASTEP on a fixed number of cores was also improved by up to four times on the original, according to the company. The CASTEP project showed the collaborative nature of the dCSE program, with the University of York undertaking the core development in conjunction with NAG HPC staff and the Science and Technology Facilities Council.

In another project, an ocean modeling application known as NEMO (Nucleus for European Modelling of the Ocean) underwent optimization including I/O techniques and variable resolution approaches to run 25 percent faster on relevant use cases. This represents a $600,000 saving in computing resources for that project with potentially multi-million dollar savings across all NEMO users, according to NAG.

A third project optimized a quantum Monte Carlo code (CASINO) for better performance on multicore nodes by introducing shared memory techniques and hierarchical parallelism. This resulted in performance gains of up to four times on quad-core nodes and further performance gains from I/O optimizations for simulations using more than 10,000 cores. Following NAG’s work, the scientists were able to run on 40,000 cores of the Jaguar Petaflops supercomputer at Oak Ridge National Laboratory.

For more information, visit NAG and the HECToR web site.

Sources:  Press materials received from the company and additional information gleaned from the company’s website.