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dc.contributor.authorGreen, Oded
dc.date.accessioned2019-05-03T16:49:01Z
dc.date.available2019-05-03T16:49:01Z
dc.date.issued2019-04-16
dc.identifier.urihttp://hdl.handle.net/1853/61021
dc.descriptionPresented on April 16, 2019 at 3:00 p.m. in the Jesse W. Mason Building, Room 2117.en_US
dc.descriptionOded Green is a Senior Graph Software Engineer in NVIDIA's RAPIDS team where he works on implementing high performance data structures and algorithms for big data analytics. Oded's research primarily focuses on improving the performance and scalability of large-scale analytics, with an emphasis on graph analytics, using a wide range of high-performance computational platforms. Oded also focuses on architecture-algorithm co-design.en_US
dc.descriptionRuntime: 59:24 minutesen_US
dc.description.abstractSparse data computations are ubiquitous in science and engineering. Two widely used applications requiring sparse data computations are graph algorithms and linear algebra operations such as Sparse Matrix-Vector Multiplication (SpMV). In contrast to their dense data counterparts, sparse-data computations have less locality and more irregularity in their execution - making them significantly more challenging to optimize. This is especially true for accelerators and many core systems. In today's talk, I will cover NVIDIA's and the graph community's effort to overcome these challenges and to create a simple to use framework that will enable both programmers and data scientists to get high performance graph algorithms, with high productivity, and an easy to use API that does not require broad HPC knowledge.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesCHAI Seminar Seriesen_US
dc.subjectScalable graph analyticsen_US
dc.subjectSparse Matrix-Vector Multiplication (SpMV)en_US
dc.titleScalable Graph Analytics on GPU Acceleratorsen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Heath Analytics and Informaticsen_US
dc.contributor.corporatenameNVIDIA Corporationen_US


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