XPS: EXPL: DSD: Portal: A Language and Compiler for Parallel N-body Computations

标题：XPS：EXPL：DSD：PORTAL：并行N-Body计算的语言和编译器现代机器正变得越来越复杂，导致即使是最先进的编译器也无法生成最佳的优化代码。因此，在纸上设计的算法和在10亿核系统上高效运行的代码之间存在着巨大的差距。本研究项目旨在为N-Body问题领域开发一种新的高性能领域专用语言(DSL)和编译器，以弥补这一差距。这些问题在各个领域都有应用，从分子动力学、天体物理学、声学、流体动力学中的科学计算模拟，一直到大数据问题。在门户中，领域科学家可以编写高水平的程序，同时获得专家在现代大规模并行机上编写的高度调优的低级代码的性能。智能的优点是展示了具有高级公式的DSL如何直接导致渐近快速的算法及其在各种不同体系结构上的高效并行实现。该项目更广泛的意义和重要性是免费提供的软件，使领域的科学家能够利用并行计算的性能力量，使科学发现不仅在科学计算和机器学习方面，而且在统计学、计算机图形学、计算几何和应用数学等领域的一些相关问题上。门户的目标有三个：(A)实现可扩展的、快速的、渐近最优的基于树的N-Body算法；(B)设计直观的语言和API以实现各种算法的快速实现；(C)使并行大规模问题能够在当前和未来的亿级机器上运行。更重要的是，语言和中间算法表示与体系结构无关，这使得该方法可移植并易于扩展到从ARM/x86 CPU到GPU的不同平台。该项目旨在解决重要的软件问题，以便在海量数据集上实现N-Body问题的互操作性和可扩展性。

Title: XPS: EXPL: DSD: Portal: A Language and Compiler for Parallel N-body ComputationsModern machines are becoming increasingly more complex resulting in even the most advanced compilers failing to generate the best optimized code. As a result, there is a big gap between the algorithm one designs on paper and the code that runs efficiently on a billion-core system. This research project aims to bridge this gap by developing Portal, a new high-performance domain-specific language(DSL) and compiler, for the domain of N-body problems. Such problems have applications in various areas ranging from scientific computing simulations in molecular dynamics, astrophysics, acoustics, fluid dynamics all the way to big data problems. In Portal, domain scientists can write programs at a high level while obtaining the performance of highly tuned and optimized low level code written by experts on modern massively parallel machines. The intellectual merit is to show how a DSL with a high-level formulation can lead directly to both asymptotically fast algorithms and their efficient parallel implementations on a variety of distinct architectures. The project's broader significance and importance are freely available software to enable domain scientists to harness the performance power of parallel computing and enabling scientific discovery not only in scientific computing and machine learning but also in a number of related problems in domains such as statistics, computer graphics, computational geometry, and applied mathematics. These problems can be expressed in Portal to obtain an out-of-the-box parallel optimized implementation.The goals of Portal are three-fold: (a) to implement scalable, fast, and asymptotically optimal tree-based N-body algorithms, (b) to design an intuitive language and API to enable rapid implementations of a variety of algorithms, and (c) to enable parallel large-scale problems to run on current and future exascale machines. More importantly, the language and intermediate algorithm representation are independent of the architecture, making this approach portable and easily extensible to different platforms from ARM/x86 CPUs to GPUs. The project aims to solve important software issues that will allow for interoperability and scalability of N-body problems on massive datasets.