Accomplisment Based Renewal (ABR) to the award Flight-Worthy Condor: Enabling Scientific Discovery

在可预见的未来，技术、用户和应用需求以及提供计算能力的商业模式的变化将继续对高吞吐量计算技术的有效性提出新的挑战。 为了应对这些挑战，正在进行的研发工作将设计新的策略驱动功能，通过有效管理在云服务提供的同构机器上运行的同构作业的超大工作负载，在规定的预算内提高吞吐量。这些功能将通过有效的服务器处理器来增强，这些服务器具有多个执行核心，许多磁盘，可能有几个GPU，每个GPU具有不同的功能，以及多个网络接口。新社区将通过定制、易于部署和安全的软件引入HTC的力量。用于分析科学应用的要求和依赖关系的新工具将扩大分布式计算基础设施的覆盖范围，这些基础设施利用先进的网络跨越机构和国家边界。HTC技术的进步将通过广泛采用的HTCondor软件工具来实现。超过150所国内大学，越来越多的国家和国际科学界以及广泛的商业组织采用HTCondor来提高其计算和数据密集型应用程序的吞吐量。该项目将维持一个软件工程过程，使翻译工作能够以过渡的方式进行，建立在上一代软件的基础上，同时继续提供和支持适合处理不断增长的实验和模拟科学数据的可靠软件。

In the foreseen future a mix of changes in technologies, user and application requirements and the business model of delivering computing capacity will continue to pose new challenges to the effectiveness of high throughput computing (HTC) technologies. To address these challenges, this ongoing research and development effort will devise new policy-driven capabilities to increase throughput within a defined budget by effectively managing extremely large workloads of homogenous jobs running on homogenous machines provisioned by cloud services. These capabilities will be augmented with effective schedulers for servers that have multiple cores of execution, many disks, perhaps several GPUs each with different capabilities, and multiple networking interfaces. New communities will be introduced to the power of HTC through customized, easy to deploy and secure software. Novel tools for profiling the requirements and dependencies of scientific applications will expand the reach of distributed computing infrastructures that leverage advanced networks to cross institutional and national boundaries. The advances in HTC technologies will be delivered through the widely adopted HTCondor software tools. More than 150 domestic universities, a growing number of national and international science communities and a wide spectrum of commercial organizations employ HTCondor to improve the throughput of their compute and data intensive applications. This project will sustain a software engineering process that enables translational work to occur in a transitional manner, building upon the previous generation of software while simultaneously continuing to offer and support dependable software that is suitable to handle the ever growing amounts of experimental and simulated scientific data.