SHF: Medium: A DSL for Data Visualization and Analysis in Imaging-Based Science and Scientific Computing

该研究项目的重点是设计，实现和应用狄德罗，一个高级的特定领域的编程语言，用于分析和可视化3D和4D（时空）数据，如通过成像方式和有限元网格上的计算模拟产生。 现在，许多科学领域都需要使用正在研究的对象和系统的数字表示进行计算，但是创建新的研究软件来可视化和理解数据是一个主要的瓶颈。 随着体积扫描试样的新方法和越来越细致入微的动态系统模型，研究软件变得越来越复杂，运行软件的计算费用显着增加。 狄德罗通过简化创建新的并行软件来可视化和处理复杂的科学数据的过程来解决这个紧迫的问题。 其智力价值在于狄德罗将如何加速在工作代码中表达数学思想，扩展狄德罗理解的计算表示类型，利用大型超级计算机的能力，并帮助程序员调试程序。 该项目的广泛意义和重要性部分来自狄德罗将处理的数据类型，例如由阿贡国家实验室的微型CT扫描仪产生的高分辨率图像（来自世界各地的科学家每天使用），或者用于发育生物学研究细胞如何组织成组织的基本问题的最新一代光片显微镜。 该项目的重要性还来自于使用狄德罗的人的范围，从编写专业有限元方法程序的数学家到通过使用捕捉鱼胚胎神经系统形成的真实显微镜数据来首次体验科学计算的大学生。计算是科学越来越重要的工具，但可用的计算工具和科学推理之间的语义差距往往很大。 计算机科学中一个紧迫的研究课题是如何创建编程工具，以跟踪并行计算硬件从传统的通用CPU到具有高性能加速器（如GPU）的异构处理器的转变。 该提案旨在通过利用特定领域语言（DSL）技术来弥合这一语义鸿沟，并改变科学家使用软件理解测量和模拟数据的方式，从而加速计算和科学交叉领域的研究。 PI将建立在他们设计的并行DSL狄德罗之上。 狄德罗的初步经验表明，它是可以写可视化和分析算法在一个非常数学的编程符号，具有高性能的并行实现各种并行硬件。 该项目将以几种方式建立在这些初步结果的基础上：PI将扩展Diderot，以支持更广泛的数据模型，并为分析数据提供更丰富的计算工具; PI将致力于扩展Diderot，以处理更大的数据集和更大的并行平台; PI将探索技术和工具，以更好地支持特定领域的软件开发。这些研究重点将涉及编程语言和图像分析领域之间的密切合作。语言功能的设计将由图像分析算法的需求以及编程语言的基本原理驱动。狄德罗的设计和实施将使用最新的图像分析算法从文献中进行评估，以及被用来原型新的算法。

This research project is focused on the design, implementation, and application of Diderot, a high-level domain-specific programming language for analyzing and visualizing 3D and 4D (spatio-temporal) data, such as produced by imaging modalities and computational simulations on finite-element meshes. Many areas of science now require computing with digital representations of the objects and systems being studied, but creating new research software to visualize and understand the data is a major bottleneck. With new ways of volumetrically scanning specimens and increasingly nuanced models of dynamic systems, research software is becoming more complex, and the computational expense of running the software is significantly increasing. Diderot addresses this urgent problem by simplifying the process of creating new parallel software to visualize and process complex scientific data. The intellectual merits are how Diderot will accelerate expressing mathematical ideas in working code, expand the kinds of computational representations that Diderot understands, leverage the power of large supercomputers, and help programmers debug their programs. The project's broader significance and importance are derived in part from the kinds of data that Diderot will handle, such as the high-resolution images produced by a microCT scanner at Argonne National Lab (used daily by scientists from all over the world), or the latest generation of light-sheet microscopes used in developmental biology to investigate fundamental questions about how cells organize themselves into tissues. The project's significance also derives from the range of people who will use Diderot, from mathematicians writing specialized finite element method programs, to college students getting their first taste of scientific computation by working with real-world microscope data capturing the formation of the nervous system in a fish embryo.Computation is an increasingly important tool for science, but the semantic gap between the available computational tools and scientific reasoning is often large. One pressing research topic within computer science is how to create programming tools that can track the shift in parallel computing hardware from traditional general-purpose CPUs to heterogeneous processors with high-performance accelerators like GPUs. This proposal seeks to accelerate research at the intersection of computation and science by exploiting domain-specific language (DSL) technology to bridge this semantic gap and to transform how scientists use software to understand data from measurement and simulation. The PIs will build on the parallel DSL Diderot that they have designed. Preliminary experience with Diderot demonstrates that it is possible to write visualization and analysis algorithms in a very mathematical programming notation that has performant parallel implementations on a variety of parallel hardware. The project will build on these preliminary results in several ways: the PIs will extend Diderot to support a wider range of data models and to provide a richer set of computation tools for analyzing data; the PIs will work on scaling Diderot to handle larger data sets and larger parallel platforms; and the PIs will explore techniques and tools to better support domain-specific software development. These research thrusts will involve close collaboration between the areas of programming languages and image analysis. The design of language features will be driven by the needs of image analysis algorithms, as well as the foundational principles of programming languages. The design and implementation of Diderot will be evaluated using the latest image-analysis algorithms from the literature, as well as being used to prototype new algorithms.