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Understanding Multiprocessors: Unlocking the Power of Concurrent Computing

This chapter explores the concept of multiprocessors, which combine multiple smaller computers to create powerful systems, offering advantages over traditional supercomputers for tasks like time-sharing and vector processing. It delves into how parallel processors share and coordinate data through techniques such as SMP, NUMA, message passing, and synchronization mechanisms like locks and semaphores. We also discuss the challenges of writing concurrent programs, implementation methods, and critical issues like cache coherency and network topology, emphasizing the need for collaboration between hardware and software professionals.

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Understanding Multiprocessors: Unlocking the Power of Concurrent Computing

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Presentation Transcript

  1. Chapter 9 Multiprocessors

  2. Multiprocessors • Idea: create powerful computers by connecting many smaller ones good news: works for timesharing (better than supercomputer) vector processing may be coming back bad news: its really hard to write good concurrent programs many commercial failures

  3. Questions • How do parallel processors share data? — single address space (SMP vs. NUMA) — message passing • How do parallel processors coordinate? — synchronization (locks, semaphores) — built into send / receive primitives — operating system protocols • How are they implemented? — connected by a single bus — connected by a network

  4. Some Interesting Problems • Cache Coherency • Synchronization — provide special atomic instructions (test-and-set, swap, etc.) • Network Topology

  5. Concluding Remarks • Evolution vs. Revolution“More often the expense of innovation comes from being too disruptive to computer users” “Acceptance of hardware ideas requires acceptance by software people; therefore hardware people should learn about software. And if software people want good machines, they must learn more about hardware to be able to communicate with and thereby influence hardware engineers.”

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