We are happy to announce the latest functionality available in the NAG Library at Mark 27.1. New additions expanding the Optimization Modelling Suite include a novel nonlinear least squares solver for unconstrained and bound-constrained data fitting (calibration) problems, and functionality to simplify building and solving quadratically constrained quadratic programming (QCQP) problems.
Joining the major Optimization updates are more Adjoint solvers – available in the NAG AD Library which ships with the NAG Library. When used with NAG's AD tool dco/c++, the NAG AD Library offers a smooth workflow which makes it easy to incorporate NAG solvers into a user's AD codes.
Of specific interest to finance industry professionals is a new solver for computing the implied volatility of a European option contract. This new NAG Library solver achieves very high accuracy even for the most difficult of volatility surfaces.
How to access the new NAG Library functionality
As with all new releases, we encourage NAG Library users upgrade to the latest Mark to access all the new content and performance improvements. NAG Library downloads are available here (look for product codes NLW6I271EL and NLL6I271BL). The new Mark 27.1 functionality is also available in the NAG Library for Python.
If you don’t have access to the NAG Library and you’d like to try the new functionality, we offer full product trials. If you have any questions or need help, do get in touch with our Technical Support team.
NAG announce the latest NAG Fortran Compiler for the new Apple platform running natively, without emulation, on Apple Silicon, an Arm v8 processor. 29 years after developing the world’s first Fortran-90 compiler NAG has released the first commercially available Fortran compiler for Apple Silicon Macs.
The Compiler offers complete coverage of Fortran 2003, most of Fortran 2008 - including coarrays and submodules - and significant features from Fortran 2018. Validated by a world-class test suite the Compiler is engineered for correctness, portability, robustness, and generated code performance. Independent evaluations regularly score it highest for its standard-checking and error detection features.
NAMD is a popular choice of HPC benchmark, and AMD, Intel and Nvidia have all made recent claims of high performance. Intel recently contributed patches to accelerate NAMD using AVX-512 and claim these optimizations can improve performance up to 1.8x and outperform the latest AMD hardware. Intel have produced the 1.8x performance improvements by implementing a “tile” algorithm which makes highly efficient use of the AVX-512 vector units and large cache of latest generation Intel Xeon CPUs. This is a port of the same tiling algorithm used by the CUDA-enabled version of NAMD.
We were curious to try these patches to verify the performance for ourselves and compare the performance gains with both AMD and Nvidia hardware. In particular, we wanted to see what this means when running NAMD in a cloud environment, where the question is often not only “how fast will it run?” but also “how much will it cost?”.
To explore this we ran benchmarks on Microsoft Azure using both AMD and Intel powered HPC-class VMs.
NAG offers a Cloud HPC Migration Service and HPC Consulting to help organisations optimize their numerical applications for the cloud and HPC. For impartial, vendor-agnostic advice including choosing your cloud, navigating the transition, and optimizing for performance and cost do see how we help.
Kudos to Fouzhan Hosseini - NAG HPC Application Analyst and Chair of our Women in HPC Chapter - on her election to trustee at the Society of Research Software Engineering. Speaking about her role Fouzhan said,
“I’m honoured, excited and looking forward to serving our growing RSE community”.