Toward Scalable Non-Cartesian Computing

迈向可扩展的非笛卡尔计算

• 批准号: RGPIN-2019-05303
• 负责人: Alim, Usman
• 金额: $ 1.68万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738183
• 关键词: Toward; Scalable; Non; Cartesian; Computing

The processing, analysis and visualization of large datasets are key challenges faced by the scientific computing community. Be it acquired imaging data or simulated computational fluid dynamics datasets, the scale and volume of these data are on the rise; experts are now planning for the era of exascale computing where supercomputers will be generating datasets in the order of exabytes (1 exabyte = 1 billion gigabytes). While computing performance continues to improve, our cognitive ability to make sense of this data explosion is certainly constant. There are also environmental factors we need to be wary of since large-scale computing applications consume massive amounts of energy. As we go forward, we will need to revisit our mathematical models and data management strategies so as to ensure that resources are optimally utilized. We will need to find a good balance between the data we can generate and the data we should generate so as to ensure that we can make sense of the data using limited computational and cognitive resources. The goal of this proposal is to tackle large data analysis and visualization challenges from the perspective of non-Cartesian computing. Non-Cartesian computing is inspired by nature and attempts to seek efficient data representations and algorithms that describe static and time-varying spatial phenomena. Nature is replete with instances of such representations; photoreceptors in the primate retina are arranged in a hexagonal fashion, sunflower seeds are packed in a spiral Fibonacci formation and our skin cells resemble a 14-sided polytope that tightly packs space. Yet, much of large-scale computing is done using the Cartesian grid that consists of cubic cells which are very inefficient at filling space. This inefficiency only worsens as problems are scaled up to larger sizes and higher dimensions. Switching to non-Cartesian representations offers a viable and attractive alternative as these representations promise the same fidelity as their Cartesian counterparts -- with significantly less computational cost. However, there are scalability challenges that need to be addressed before these representations can be used at peta and exa scales. This proposal therefore aims to investigate non-Cartesian data management and visualization algorithms that are specially designed for large-scale heterogeneous computing environments. Following the "do more with less" mantra, the ultimate goal is to offer simple and efficient data representation solutions that can be integrated into resource-constrained high performance computing and visualization workflows without sacrificing accuracy or performance.

大型数据集的处理，分析和可视化是科学计算社区面临的关键挑战。无论是获得成像数据还是模拟计算流体动力学数据集，这些数据的规模和体积都在上升。专家现在正在计划Exascale计算时代，在该时代，超级计算机将按照exabytes的顺序生成数据集（1个exabyte = 10亿GB）。尽管计算性能不断提高，但我们理解这一数据爆炸的认知能力肯定是不变的。由于大规模计算应用消耗大量能量，因此我们还需要谨慎的环境因素。随着我们的前进，我们将需要重新审视我们的数学模型和数据管理策略，以确保资源最佳利用。我们将需要在可以生成的数据与应该生成的数据之间找到一个良好的平衡，以确保我们可以使用有限的计算和认知资源来理解数据。 该提案的目的是从非艺术计算的角度应对大型数据分析和可视化挑战。非科学家计算受自然的启发，并试图寻求有效的数据表示和算法来描述静态和随时间变化的空间现象。大自然充满了这种表示的情况；灵长类动物视网膜中的光感受器以六角形的方式排列，葵花籽堆积在螺旋斐波那契的形成中，我们的皮肤细胞类似于14面的多型，可紧密包装空间。但是，大部分大规模计算都是使用笛卡尔网格完成的，该网格由立方细胞组成，这些细胞在填充空间方面效率非常低。这种低效率只会随着问题扩展到更大的尺寸和更高的维度而恶化。切换到非科学家表示提供了可行且有吸引力的替代方案，因为这些表示的忠诚度与他们的笛卡尔同行相同 - 计算成本少得多。但是，在PETA和EXA量表上使用这些表示形式之前，需要解决一些可伸缩性挑战。因此，该建议旨在研究专门为大规模异构计算环境设计的非现行数据管理和可视化算法。遵循“多少”咒语，最终目标是提供简单有效的数据表示解决方案，这些解决方案可以将其集成到资源约束的高性能计算和可视化工作流程中，而无需牺牲准确性或性能。