SBIR Phase I: Scaling Operational Intelligence Graphs with GPU Clouds

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将最直接地为银行和国防机构等机构提供以图形为中心的安全和运营数据的可见性，从而提高其鲁棒性和弹性。更进一步说，实现更大图形的可视化也将有助于非生物信息技术的使用，例如帮助金融分析师了解市场，营销和销售团队了解客户，以及精准医学研究人员了解基因相互作用。此外，通用化底层GPU云基础设施以扩展图形之外的交互式可视化，将有助于更多类型的分析师从更广泛的数据源中理解数据。同样，推广GPU云基础设施也将有助于非可视化分析任务，例如大数据机器学习。这个小型企业创新研究第一阶段项目的目标是将可视化图形分析扩展到企业级安全分析。现代图形可视化工具最多可处理50，000个节点，但许多企业管理着超过100万台设备和服务。所提出的创新将图形可视化扩展到1/2 - 2个数量级的数据。可视化与GPU集群交互以进行分析（例如，交叉过滤），视觉布局（例如，ForceRightas 2和边绑定），以及渲染。第一阶段的关键是确定构建和部署分布式GPU云架构的技术可行性。该架构利用新颖的GPU组件和优化：（i）GPU分析;（ii）GPU加速的视觉布局;（iii）云GPU资源管理;以及（iv）流渲染器，以在保持感知质量和响应能力的同时绘制更多内容。与试点客户合作，将确定和原型化最小可行安全分析使用所需的视图和工作流程。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will, most directly, be to provide institutions such as banks and national defense agencies with visibility into their graph-­centric security and operations data, and thereby improve their robustness and resilience. Farther out, enabling visualization of larger graphs will also aid non-­IT uses, such as to help financial analysts understand markets, marketing and sales teams understand customers, and precision medicine researchers understand gene interactions. Furthermore, generalizing the underlying GPU cloud infrastructure to scale interactive visualizations beyond graphs will help even more types of analysts comprehend data from an even wider variety of data sources. Likewise, generalizing the proposed GPU cloud infrastructure will also aid non­-visual analytic tasks, such as machine learning over big data.This Small Business Innovation Research Phase I project's goal is to scale visual graph analysis to enterprise level security analytics. Modern graph visualization tools handle at most 50,000 nodes, but many enterprises manage over a million devices and services. The proposed innovation scales graph visualizations to 1­-2 magnitudes more data. The visualizations interact with a GPU cluster for analysis (e.g., cross-filtering), visual layout (e.g., ForceAtlas2 and edge bundling), and rendering. Key to Phase I is establishing the technical feasibility of building and deploying the distributed GPU cloud architecture. The architecture utilizes novel GPU components and optimizations for: (i) GPU analytics (ii) GPU-accelerated visual layouts; (iii) cloud GPU resource management; and (iv) a streaming renderer to draw more while maintaining perceived quality and responsiveness. In collaboration with pilot customers, the views and workflows necessary for minimal viable security analytics usage will be identified and prototyped.