24 Distributed Computing

24 Distributed Computing - Computer Science Computer...

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1 © Copyright Azer Bestavros / Al rights reserved. Computer Science CS-350 Distributed Computing Azer Bestavros Computer Science Department Boston University Computer Science Computer Speedup Moore’s Law: “ The density of transistors on a chip doubles every 18 months, for the same cost” (1965) Image: Tom’s Hardware Computer Science Scope of problems What can you do with 1 computer? What can you do with 100 computers? What can you do with an entire data center? Computer Science Distributed problems Rendering multiple frames of high-quality animation Image: DreamWorks Animation Computer Science Distributed problems Simulating several hundred or thousand characters Happy Feet © Kingdom Feature Productions; Lord of the Rings © New Line Cinema Computer Science Distributed problems Indexing the web (Google) Simulating an Internet-sized network for networking experiments (PlanetLab) Speeding up content delivery (Akamai) Computing clouds as a utility (Amazon) What is the key attribute that all these examples have in common?
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2 Computer Science Parallel vs. Distributed Parallel computing can mean: ± Vector processing of data (SIMD) ± Multiple CPUs in a single computer (MIMD) Distributed computing is multiple CPUs across many computers (MIMD) Computer Science A Brief History… 1975-85 Parallel computing was favored in the early years Primarily vector-based at first Gradually more thread- based parallelism was introduced Cray 2 supercomputer (Wikipedia) Computer Science “Massively parallel architectures” start rising in prominence – CM-2, CM-5, … Message Passing Interface (MPI) and other libraries developed Bandwidth was a big problem A Brief History… 1985-95 Computer Science A Brief History… 1995-Today Cluster/grid architecture increasingly dominant Special node machines eschewed in favor of COTS technologies Web-wide cluster software Companies like Google take this to the extreme (thousands of nodes/cluster) Parallel computing again fashionable due to the necessity of using multi-core Computer Science Parallelization Idea Parallelization is “easy” if processing can be cleanly split into n units: Computer Science Parallelization Idea (2) In a parallel computation, we would like to have as many threads as we have processors. e.g., a four-processor computer would be able to run four threads at the same time.
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3 Computer Science Parallelization Idea (3) Computer Science Parallelization Idea (4) Computer Science Parallelization Pitfalls But this model is too simple! How do we assign work units to worker threads?
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24 Distributed Computing - Computer Science Computer...

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