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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749775
• 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界面临的关键挑战。无论是获取的成像数据还是模拟的计算流体动力学数据集，这些数据的规模和数量都在增加;专家们现在正在规划艾级计算的时代，超级计算机将生成艾字节（1艾字节= 10亿千兆字节）数量级的数据集。虽然计算性能不断提高，但我们理解这种数据爆炸的认知能力肯定是恒定的。我们还需要警惕环境因素，因为大规模计算应用程序消耗大量能源。随着我们向前迈进，我们将需要重新审视我们的数学模型和数据管理战略，以确保资源得到最佳利用。我们需要在我们可以生成的数据和我们应该生成的数据之间找到一个很好的平衡，以确保我们可以使用有限的计算和认知资源来理解数据。 该提案的目标是从非笛卡尔计算的角度解决大数据分析和可视化挑战。非笛卡尔计算的灵感来自自然，并试图寻求有效的数据表示和算法，描述静态和时变的空间现象。自然界充满了这样的代表性的例子;灵长类动物视网膜中的光感受器以六边形的方式排列，向日葵种子以螺旋斐波那契形式排列，我们的皮肤细胞类似于一个14边多面体，紧密地包装空间。然而，大部分大规模计算都是使用笛卡尔网格完成的，笛卡尔网格由立方体单元组成，在填充空间时效率非常低。这种低效率只会在问题被放大到更大的尺寸和更高的维度时被忽略。切换到非笛卡尔表示提供了一个可行的和有吸引力的替代方案，因为这些表示承诺与笛卡尔对应物相同的保真度-具有显着更少的计算成本。然而，在这些表示可以在peta和exa尺度上使用之前，需要解决可扩展性的挑战。因此，该建议的目的是调查非笛卡尔数据管理和可视化算法，专门设计用于大规模异构计算环境。遵循“少花钱多办事”的原则，最终目标是提供简单高效的数据表示解决方案，这些解决方案可以集成到资源受限的高性能计算和可视化工作流中，而不会牺牲准确性或性能。