PPT-Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Review student Fan Bai Instructor Dr Sushil Prasad 20120321 Andrew Nere AtifHashmi and MikkoLipasti University of Wisconsin Madison IPDPS 2011 Purpose Background Why can be . Tom O’Regan. September . 2014. HIQA: Risk Profiles. Our Risk . P. rofile . tool . is . core to responsive regulation and in large part informs how the Authority responds to risk in designated . centres. Dr Kat . Hadjimatheou. Research . Fellow. Dept. . o. f Philosophy. University of Birmingham. Outline. Is . ethnic profiling harder to justify than behavioural profiling . in principle or for contingent . Youngho Kim. CIS665: GPU Programming. Building a Million Particle System: Lutz Latta. UberFlow - A GPU-based Particle Engine: Peter Kipfer et al.. Real-Time Particle Systems on the GPU in Dynamic Environments: Shannon Drone. Chihao. Li. , Department of Mathematics, National Taiwan University. 2011/7/7. What is profiling?. Profiling. 2. What is Profiling?. A good program need?. Accuracy?. Effeciency. ?. Readability?. Rewritability. with Two Supportive Search Schemes (TSSS) and. Artificial Entanglement (AE). Chee Ken Choy (Kenny). Intelligent Computer Entertainment [ICE] Lab. History of Quantum-inspired Algorithms. The idea of first . Profile antibodies to over 8000 human proteins using Luminex xMAP technology. Autoantibodies as Biomarkers. The immune system functions as a an early warning system.. Malignant transformation, infections, and many diseases can trigger an immune response. Cambridge. enabling meaningful venture . creation out . of Cambridge. Entrepreneurship, . . a discovery. . E. Events. Network. What is entrepreneurship?. Courses. Events. Learn about entrepreneurship. Summer 2011. PRE-SEASON CAMP. Performance Profiling. Performance profiling raises your awareness about the required attributes to be successful in your position. . It also allows you to identify your strengths and some areas to work on. . Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Objective: develop technologies to improve computer performance. . . 1. Processor. Generation. Max. Clock. Speed (GHz). Max. Numberof Cores. Max. RAM. Bandwidth (GB/s). Max. Peak Floating Point (Gflop/s). Jeff . Ward. | . jward@watermark.org. 1) . Take. . out. . of. . wrapper. 2) . Put. in . microwave. 3) . Heat. . for. 1 . minute. 4) Eat!. THE BIG IDEA. Workplace. = . Discipleship. Leaders. . 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/ 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats Dept. of Computer Science and Engineering. University of South Carolina. Dr. Jason D. . Bakos. Assistant Professor. Heterogeneous and Reconfigurable Computing Lab (HeRC). This material is based upon work supported by the National Science Foundation under Grant Nos. CCF-0844951 and CCF-0915608..