Generic programming is widely practiced in the C++ community as a means of providing efficient, reusable libraries. This style of programming is characterized by the use of templates to define data structures and algorithms in terms of template parameters that can be replaced by user-supplied types and values later. Today, C++ templates are unconstrained, meaning that any type or value could potentially be substituted for a template parameter. This is clearly not the case, since for example, taking the arithmetic mean of a sequence of strings is almost certainly meaningless. The Concepts Lite Technical Specification extends the C++ programming language with features that support the specification and checking of constraints on template arguments. This talk focuses on principles of generic programming using concepts.
Numerical Weather Prediction (NWP) is a field that is highly data-intensive. Meteorological data is produced and processed in enormous amounts with very tight, time-critical, constraints. To produce a global forecast, millions of world-wide measurements and satellite images must be analysed and then fed into a model of the atmosphere to compute raw solution fields that are subsequently post-processed to generate millions of specialised user-driven products.
Historically, the tools and models that make up this complex chain have been hand-tuned and parallelised with MPI/OpenMP and run on special, purpose-built supercomputers. Written in monolithic Fortran, the code base for these models has become hard to maintain and difficult to adapt to rapidly evolving concurrent hardware like GPGPUs and many-core CPUs. The NWP community has started to recognise the need to embrace modern software techniques for the many-core era, with special emphasis on high-performance languages like C++ and the use of DSL's.
In a way, H. Sutter's "free-lunch" has been paid for in advance by NWP: its software models have been concurrent and distributed since the ‘90s. While there is still much work to do in model scalability, the challenges ahead also include: electrical power limits, data I/O rates, exponential volume growth, etc.
We will show how we are addressing these challenges, and present some solutions to modernise our software stack. In particular we will present a C++ DSEL inspired in functional programming, for building pipelines for parallel processing of meteorological data. Scientists with little or no knowledge of C++, can extend this DSEL to pre-process meteorological observations and post-process model output to generate meteorological products.
Slides
