DC: SMALL - Data-Intensive Computing Solutions for Neuroimage Analysis

一些应用领域需要处理太/千万亿字节的数据。使用如此庞大的数据集开发数据密集型应用程序在数据管理、处理和资源分配方面提出了若干挑战。此外，产生稳健和接近最佳结果的算法及其相关参数通常是通过迭代过程确定的，其中交互式响应时间至关重要。因此，需要能够快速创建各种数据分析算法的可扩展和并行实现。需要大规模数据分析的新兴和关键领域是医学成像。需要复杂的算法和新颖的工具才能分析这些数据。 在该项目中，我们考虑从fMRI（功能性MRI）获得的数据。 在此领域的推动下，本项目重点研究如何将算法设计、API设计和运行时系统开发结合起来，为时空数据分析提供有效的数据密集型解决方案。具体而言，我们针对以下四个问题：1）我们如何利用地图减少范式，以加速在神经影像学和相关医学领域的进步？. 2)在使用映射-归约范式进行神经图像分析时，有哪些挑战？3)当前的map-reduce API可以提供什么替代接口，仍然提供数据分析算法的轻松表达，同时实现更好的性能？4)我们可以从高级语言（如Matlab）开始使用map-reduce和类似的范例吗？该项目还将对教学、人力资源开发和增加多样性作出重大贡献。 申请的大部分资金将用于支持博士生参与该项目。PI Agrawal希望他目前的三名女博士生中至少有一名参与这个项目。PI Machiraju还与俄勒冈州立大学医学院的几名本科生进行了接触。

Several application domains require processing of tera/peta-bytes of data. Developing data-intensive applications with such massive datasets poses several challenges, with respect to data management, processing, and resource allocation. Furthermore, algorithms and their pertinent parameters that yield robust and near-optimal results are often determined through an iterative process for which interactive response times are essential. Thus, there is a need for being able to rapidly create scalable and parallel implementations of a variety of data analysis algorithms. An emerging and critical area requiring large-scale data analysis is medical imaging. Complex algorithms and novel tools are required to be able to analyze such data. In the project, we consider data obtained from fMRI (functional MRI). Driven by this domain, this project focuses on how algorithm design, API design, and runtime system development can be combined to provide effective data-intensive solutions for spatio-temporal data analysis. Particularly, we target the following four questions: 1) How can we exploit the map-reduce paradigm to accelerate advances in neuroimaging and related medical fields? . 2) What are some of the challenges in using map-reduce paradigm for neuroimage analysis?3) What alternative interface to the current map-reduce API can provide still provide ease of expression of data analysis algorithms, while enabling better performance? 4) Can we use the map-reduce and similar paradigms starting from high-level languages, such as Matlab? This project will also make substantial contributions towards teaching, human resource development, and increasing diversity. Most of the requested funds will be used for supporting Ph.D students on this project. PI Agrawal expects to involve at least one of his three current female Ph.D students in this project. PI Machiraju also engages with several undergraduate students at the medical campus of OSU.