The talk will present a crash course in aircraft design and in particular the aerodynamic analysis viewed through the various mathematical models. The discretization of these into coupled systems will present some challenges. The impact of HPC on industrial processes, including aircraft certification, will be discussed. Some case studies will demonstrate the need for ever increased software and hardware developments as exemplified by NASA’s current vision for 2030.

To that end, contributions from Polytechnique Montreal will show the impact of the Chapel language for large-scale problems (billions of unknowns), in its performance and also its ease of use.

However, dealing with scalability implicitly induces the heterogeneity issue, which means that different programming models/languages, runtimes and libraries need to be employed ensemble for efficiently exploiting all levels of parallelism of large-scale systems. As a consequence, efforts towards productivity are crucial for harnessing the future generation of supercomputers. In this talk proposal, we present our efforts towards productivity-aware ultra-scale tree search using the Chapel language. Four topics are covered in this document: the design and implementation of tree-search using Chapel, improving intra-node efficiency, the use of GPUs and future perspectives.

This paper addresses some of the key limitations of past approaches on mapping Chapel on to GPUs as follows. We introduce the GPUAPI module, which provides multi-level abstractions of existing low-level GPU API such as CUDA runtime API. This module allows Chapel programmers to have the option of explicitly manipulating device memory (de)allocation and data transfer API at the Chapel level while maintaining good performance and productivity. The GPUAPI module is useful particularly when they dive into lower-level details to incrementally evolve their GPU implementations for improved performance on multiple heterogeneous nodes. We provide two tiers of GPU API: the MID-LOW-level API and the MID-level API. The MID-LOW-level API offers thin wrappers for raw GPU API routines, whereas the MID-level API provides Chapel programmer-friendly interface - i.e., allocating device memory using the 'new' keyword. Also, the module allows the coexistence of different levels of API even with the prototype GPU code generator in Chapel 1.24.

Our preliminary performance and productivity evaluations show that the use of the GPUAPI module significantly simplifies the manipulation of GPU API in Chapel for multiple CPUs+GPUs nodes while achieving the same performance.