PPT-Efficient Parallelization of Path Planning Workload

on Singlechip Sharedmemory Multicores Masab Ahmad Kartik Lakhsminrarsimhan Omer Khan University of Connecticut Agenda Motivation Characterization Methodology Characterization Results. Why Automated Operations, now more than ever?. What We Are Hearing From You. Configuration & Compliance. Performance & Capacity. “I need a more . integrated, simpler approach to ensure the performance, capacity, and health of our virtual environment” . Dell Advanced Infrastructure . Manager. Presenter Name. Title. . Non-strategic tasks burn most cycles. Variable workloads = sub-optimized capacity . We have more physical than virtual servers. Provisioning infrastructure takes too much time. Wenting . Wang. Le Xu. Indranil Gupta. Department of Computer Science, University of Illinois, Urbana Champaign . 1. Scale up VS. Scale out. A dilemma for cloud application users: scale up or scale out? . Wesley Chu. With slides taken from Armin . Hornung. Humanoid Path Planning. Humanoids have large number of DOF. Planning full body movements not computationally feasible. Alternative: plan for footstep locations, and use predefined motions to execute on these footsteps. EMbedded. . db. . BENCHmark. (ITEMBENCH). Team . CodeBlooded. Taxonomy. RFID (HOPE AMD Dataset). Logging of Positions. Authentication. Notification. Smart Notification. Room Allocation. Crowd Sensing. Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. Konstantinos Theodorakos. January 2015. Modern Processor Design. “Free lunch is over”. Lower Power consumption is favored on multi-core/processor architectures. CBD Parallelization . intention. Why Parallelize CBD models?. ECE 751, Fall 2015. Peng . Liu. 1. Overview. What? JavaScript . Engine optimization. How? Light-weight . software speculation mechanism. 2. [1] Heine. , David, et al. Software and hardware for exploiting speculative parallelism with a multiprocessor. Computer Systems Laboratory, Stanford University, 1997. Timing. Background and context. 5 minutes. How do we communicate?. 40 minutes. Action for change. 15. minutes. Reviewing and streamlining how we communicate. Background and Context. We want to evaluate our practice so our communications are useful and meaningful, but with a low workload. . Why measure it?. Performance limits. Predict top performance. Tasks. Cognitive/perceptual. multiple. What kind of tasks have High mental workload?. High. Air traffic control. Pilot. Military command & control. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. A*. It . applies to . path-planning problems on known finite graphs whose edge costs increase or . decrease over . time. (Such cost changes can also be used to model edges or vertices that are . added or . 19 August 2015. Intelligent Workload Management, Ritika Nevatia. 2. Ritika Nevatia. Under the supervision of . Prasanth Kothuri. One DB,. One client. 19 August 2015. Intelligent Workload Management, Ritika Nevatia.