PPT-NAPEL: Near-Memory Computing Application Performance Prediction

Author : nersonvisa | Published Date : 2020-07-01

via Ensemble Learning Gagandeep Singh Juan GomezLuna Giovanni Mariani Geraldo F Oliveira Stefano Corda Sander Stuijk Onur Mutlu Henk Corporaal 56 th Design Automation

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NAPEL: Near-Memory Computing Application Performance Prediction: Transcript

via Ensemble Learning Gagandeep Singh Juan GomezLuna Giovanni Mariani Geraldo F Oliveira Stefano Corda Sander Stuijk Onur Mutlu Henk Corporaal 56 th Design Automation Conference DAC Las Vegas. “Computing . is changing more rapidly than ever before, and scientists have the unprecedented opportunity to change computing . directions” . “In . 20 or 30 years, you'll be able to hold in your hand as much computing knowledge as exists now in the whole city, or even the whole world. Computing Paradigms for Pleasingly Parallel Biomedical Applications . Authors. : . Thilina. . Gunarathne. , . Tak. -Lon . Wu, . Judy . Qiu. , . Geoffrey . Fox. Publish: . HPDC'10, June 20–25, 2010, Chicago, Illinois, USA. . at Low Cost. Lavanya Subramanian. , . Donghyuk. Lee, . Vivek. . Seshadri. , . Harsha. . Rastogi. , . Onur. . Mutlu. 1. The Blacklisting Memory Scheduler. Main Memory Interference Problem. Causes interference between applications’ requests. Intel. ® . performance tools.. Andrei Anufrienko. Intel Compiler Group. The objectives of this course. :. Get a basic understanding of. : . the main factors of the processor performance,. base performance improvement techniques,. Yongin. Kwon, . Sangmin. Lee, . Hayoon. Yi, . Donghyun. Kwon, . Seungjun. Yang, . Byung. -. Gon. Chun,. Ling Huang, . Petros. . Maniatis. , . Mayur. . Naik. , . Yunheung. . Paek. USENIX ATC’13. Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . Multi-Application Execution. Adwait Jog. 1. Onur Kayiran. 2. , Tuba Kesten. 3. , Ashutosh Pattnaik. 3. , . Evgeny Bolotin. 4. , Nil Chatterjee. 4. , Steve Keckler. 4,5. , . Mahmut Kandemir. 3. , Chita Das. ITS Research Computing. Mark Reed. Overview – Research Computing. Resources. Services. Projects. ReCo Resources. Computational Resources. compute clusters: Longleaf, Killdevil, . Dogwood. Special purpose servers: . A Tale of Two Cities: GPU Computing and Machine Learning Dr. Xiaowen Chu Department of Computer Science, Hong Kong Baptist University Outline 2 Some Stories of “Two Cities” Evolution of CPUs/ Achieving High Performance and Fairness at Low Cost Lavanya Subramanian , Donghyuk Lee, Vivek Seshadri , Harsha Rastogi , Onur Mutlu 1 The Blacklisting Memory Scheduler Main Memory Interference Problem Providing High and Predictable Performance in Multicore Systems Through Shared Resource Management Thesis Defense Lavanya Subramanian 1 Committee: Advisor: Onur Mutlu Greg Ganger James Hoe Ravi Konsep. Aloysius Airlangga Bajuadji, S.Kom, M.Eng. aabayuadji@yahoo.com. | . aabayuadji@gmail.com. Bayu R Langga. 1. High Performance Computing | . 2013. ... Sistem Perkuliahan. Ada 14 Tatap Muka. Prosentase Penilaian. with Approximate Loads. Georgia Institute of Technology. Carnegie Mellon University. Hadi. . Esmaeilzadeh. . Onur. . Mutlu. Todd . Mowry. Bradley. . Thwaites. Gennady. . Pekhimenko. Amir . Yazdanbakhsh. Enhancing Application Performance. Presented by: Steve . Seaney. & Chris . Shaffer. Agenda. Survey. Why Performance is Important. Improving Performance. Monitoring Performance. Real-world Cases.

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