Accelerating biomedical image processing using massively parallel processors
Accelerating biomedical image processing using massively parallel processors
批准号:
9138396
负责人:
John Melonakos
金额:
$14.64万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2017-02-28
关键词:
AccelerationAlgorithmsCaregiversCodeComputer softwareCountryDataDevelopmentGenerationsHealthcareHigh Performance ComputingHospitalsHousingImageImage AnalysisInternationalKnowledgeLibrariesMagnetic Resonance ImagingMedical ImagingMemoryPerformancePersonsPhaseProcessRecording of previous eventsRoentgen RaysScienceSeriesSoftware ToolsSpeedStructureStudentsSurveysTechniquesTechnologyTimeWorkWritingbioimagingdata managementimage processingimaging softwareinsightmigrationnovel strategiesopen sourceoperationparallel computerpublic health relevancesupercomputertoolweb site
中文摘要
英文摘要
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.
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GPU-enhanced Neuroscience Software Tools
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批准号:8315527
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项目类别:
-
资助金额:$49.96万
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财政年份:2010
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负责人:John Melonakos
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依托单位:
GPU-based Computational Advancements for Neuroscience MATLAB Programs
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批准号:8003884
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项目类别:
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资助金额:$23.64万
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财政年份:2010
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负责人:John Melonakos
-
依托单位:
GPU-enhanced Neuroscience Software Tools
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批准号:8444396
-
项目类别:
-
资助金额:$49.96万
-
财政年份:2010
-
负责人:John Melonakos
-
依托单位:
GPU-enhanced Neuroscience Software Tools
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批准号:8628180
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项目类别:
-
资助金额:$49.96万
-
财政年份:2010
-
负责人:John Melonakos
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依托单位:
海外基金