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Advances in Parallel Databases and Volunteer Computing at HKUST

This work by Qiong Luo from the Hong Kong University of Science and Technology explores the realm of parallel databases and volunteer computing. It discusses the evolution of parallel architectures, including shared-memory and shared-nothing systems, and strategies like pipelined and partitioned parallelism. Furthermore, it highlights notable distributed computing projects such as SETI@home and folding@home, which leverage volunteer resources. The paper also addresses current trends in parallel processors and the implications for query processing within evolving computing paradigms.

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Advances in Parallel Databases and Volunteer Computing at HKUST

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  1. Parallel Databases @ Home Qiong Luo Hong Kong University of Science & Technology http://www.cse.ust.hk/~luo

  2. Parallel Databases • Future of high performance computing [DeWitt and Gray, CACM 1992] • Parallelism metrics: scaleup and speedup • Parallel architectures: Shared-memory, shared-disk, shared-nothing • Pipelined and partitioned parallelism • Intra-operator parallelism: split and merge • Specialized parallel operators All systems in this era ran in (super-)computer labs. Qiong Luo @ CIDR 2007

  3. Volunteer Computing @ Home • A bunch of distributed computing projects utilizing home PCs over the Internet (2000-) • SETI@home, 3 million users, TFLOPS-PFLOPS • folding@home • Einstein@home • LHC@home • Predictor@home • Rosetta@home • … All tasks are running on private computers at volunteers’ homes. Qiong Luo @ CIDR 2007

  4. Current Parallel Processors @ Home • CUDA • NVDIA GeForce 8800 video cards (Nov 06) • 16 SIMD multiprocessors, each of eight processors • Over 300 GFLOPS (10 X Intel 3.0GHz Core 2 Duo) • CBEA (The Cell Architecture by STI) • Sony Playstation3 game console (Oct 06) • One Power Processing Element (PPE) • Eight Synergistic Processing Elements (SPEs) Commodity processors with massive parallel processing power Qiong Luo @ CIDR 2007

  5. Parallel Query Processing @ Home ? • There are probably applications for it. • We might or might not need a full-fledged parallel database system. • There will be a learning curve for the emerging hardware architectures. • The @home computing paradigm requires us to rethink many issues. • There is a wealth of literature on parallelDB. Comments are welcome! Qiong Luo @ CIDR 2007

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