Accelerating biomedical image processing using massively parallel processors

使用大规模并行处理器加速生物医学图像处理

• 批准号: 9138396
• 负责人: John Melonakos
• 金额: $ 14.64万
• 依托单位: ACCELEREYES, LLC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9138396
• 关键词: Acceleration; Algorithms; Caregivers; Code; Computer software; Country; Data; Development; Generations; Healthcare; High Performance Computing; Hospitals; Housing; Image; Image Analysis; International; Knowledge; Libraries; Magnetic Resonance Imaging; Medical Imaging; Memory; Performance; Persons; Phase; Process; Recording of previous events; Roentgen Rays; Science; Series; Software Tools; Speed; Structure; Students; Surveys; Techniques; Technology; Time; Work; Writing; bioimaging; data management; image processing; imaging software; insight; migration; novel strategies; open source; operation; parallel computer; public health relevance; supercomputer; tool; web site

 DESCRIPTION (provided by applicant): During the last decade the quantity of bioimaging data has grown tremendously. Current estimates indicate that the average hospital in the USA houses some 665 TB of data of which approximately 80% is composed of unstructured image data from CT, MRI, and X­ray machines. This huge quantity of data is expected to grow at a rate of 20­40% annually, meaning hospitals could generate a total of one exabyte of new biomedical imaging data this year. The last decade has also seen the development of several new computing platforms. In particular, multi­core and massively parallel processors are ubiquitous. Of these new platforms, the sheer computational power in modern Graphical Processing Units (GPUs) have created a computing era where it is feasible for a developer to purchase a personal supercomputer with 10+ teraflops of processing ability for less than $20,000. One of the most popular components of modern biomedical imaging software, the Insight ToolKit (ITK), could benefit greatly from GPU computing. There have been two attempts to implement ITK's functionality on the GPU and although there were impressive results (accelerations between 5 ­ 800x); both projects were ultimately abandoned. As it stands, our GPU accelerated ArrayFire library already contains about 26% of ITK's core functionality, more than any competing software. Within the context of this proposal we seek to expand ArrayFire's support of ITK's functionality and create tools that will help developers use ArrayFire to leverage the massively parallel computing capabilities of GPUs from their ITK applications.

 描述（由申请人提供）：在过去的十年中，生物成像数据的数量有了巨大的增长。目前的估计表明，美国的平均医院拥有约665 TB的数据，其中约80%由来自CT、MRI和X射线机的非结构化图像数据组成。这一庞大的数据量预计将以每年20 - 40%的速度增长，这意味着医院今年可以产生总计1 EB的新生物医学成像数据。在过去的十年中，还出现了几种新的计算平台。特别是，多核和大规模并行处理器无处不在。在这些新平台中，现代图形处理单元（GPU）的绝对计算能力创造了一个计算时代，在这个时代，开发人员可以以不到20，000美元的价格购买具有10万亿次浮点运算能力的个人超级计算机。Insight ToolKit（ITK）是现代生物医学成像软件中最受欢迎的组件之一，它可以从GPU计算中受益匪浅。在GPU上实现ITK的功能有两次尝试，虽然有令人印象深刻的结果（加速在5到800倍之间），但这两个项目最终都被放弃了。目前，我们的GPU加速ArrayFire库已经包含了ITK约26%的核心功能，超过了任何竞争软件。在本提案的背景下，我们寻求扩展ArrayFire对ITK功能的支持，并创建帮助开发人员使用ArrayFire来利用的工具 GPU的大规模并行计算能力。