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Background Computer System Architectures Computer System Software Computer System Architectures Centralized (Tightly Coupled) Distributed (Loosely Coupled) Centralized v Distributed • Centralized systems consist of a single computer – Possibly multiple processors – Shared memory • A distributed system consists of multiple independent computers that “appear to its user as a single coherent system” Tanenbaum, p. 2 Centralized Architectures with Multiple Processors (Tightly Coupled) • All processors share same physical memory. • Processes (or threads) running on separate processors can communicate and synchronize by reading and writing variables in the shared memory. • SMP: shared memory multiprocessor/ symmetric multiprocessor Tightly-Coupled Architectures CPUs are connected at the bus level M M M Interconnection Network – Bus-based or Switched (all accesses to memory go through the network) P0 P1 P2 Drawback • Scalability based on adding processors. • Memory and interconnection network become bottlenecks. • Caching improves bandwidth and access times (latency) up to a point. • Shared memory multiprocessors are not practical if large numbers of processors are desired. UMA: Uniform Memory Access (tightly coupled/shared memory) • Based on processor access time to system memory. • All processors can directly access any address in the same amount of time. • Symmetric Multiprocessors are UMA machines. NUMA: Non-Uniform Memory Access • Still one physical address space • A memory module is attached to a specific CPU (or small set of CPUs) = node • A processor can access any memory location transparently, but can access its own local memory faster. • NUMA machines were designed to address the scalability issues of SMPs Dual (Multi) Core Processors • Two (or a few more) CPUs on a single die • Somewhat slower than a traditional multiprocessor but faster than a single processor machine • To take advantage, the OS must support multiple threads and the software must be multi-threaded Contention and Coherence • In shared memory multiprocessors the hardware must deal with – Memory contention: two processors try to access the same block of memory at the same time – Cache coherence: If one processor changes data in its cache other processors must be notified that their cached copy is out of date Distributed Architectures (Loosely Coupled) • Memory is partitioned – Each processor has its own private address space. – Accesses to address N by two different processors will refer to two different locations. • Processes must use message passing (maybe hardware-based) to communicate and synchronize • Memory contention and cache coherence are not problems. Loosely-Coupled Architectures M M M P0 P1 P2 Interconnection Network – Bus-based or Switched (all accesses to memory are local; the network is used to send messages between processes running on different processors) Multiprocessors • We will usually refer to tightly-coupled machines as multiprocessors (or shared memory multiprocessors). • Loosely-coupled machines will be called distributed systems or multicomputers. Examples of Distributed Systems • • • • Massively Parallel Processors (MPPs) Cluster Grid Network of Workstations MPP • Many (maybe tens of thousands) of separate computers, running in parallel, connected by a high-speed network • Communication between processors: message passing • Supercomputers • Usually one-of-a-kind; individually tuned for high performance Clusters • The computers in a cluster are usually connected by a high-speed LAN. • Commodity processors and commodity operating systems (Linux/UNIX) • Homogeneous • Server farms, high-performance applications • MPPs versus clusters: scale, amount of individualization, no clear dividing line. Grid Computing • Grid computing distributes work across several computers to solve large parallel problems. • Grids versus MPP/cluster model – Geographic distribution – Different administrative domains – Heterogeneous processors – More loosely coupled • Compare to the electrical grid. Networks of Workstations • Separate workstations, usually in the same administrative domain. • Usually function in a stand-alone fashion but may also cooperate on some projects • Primarily designed to utilize idle CPU cycles. Computer System Software Operating Systems Middleware System Software • The operating system itself • Compilers, interpreters, language run-time systems, various utilities • Middleware – Runs on top of the OS – Connects applications running on separate machines – Communication packages, web servers, … Operating Systems • General purpose operating systems • Real time operating systems • Embedded systems General Purpose Operating Systems • Manage a diverse set of applications with varying and unpredictable requirements • Implement resource-sharing policies for CPU time, memory, disk storage, and other system resources • Provide high-level abstractions of system resources; e.g., virtual memory, files • Applications (usually) are built on top of the abstractions Kernel • The part of the OS that is always in memory – lowest level of abstraction • Monolithic kernels versus microkernels – Monolithic: all OS code is in kernel space, runs in kernel mode – Hybrid: minimal functionality in kernel space, some OS functions (servers) execute in user space • Hybrid kernels: a mixture of the two System Architecture and the OS • Shared memory architectures have one or more CPUs • Multiprocessor OS is more complex – Master-slave operating systems – SMP operating systems • Distributed systems run a local OS and typically various kinds of middleware to support distributed applications • Pure distributed operating systems are rare. Non-traditional Kernel Architectures • Traditional: UNIX/Linux, Windows, Mac … • Non-traditional: – Microkernels – Extensible operating systems – Virtual machine monitors • Non-traditional kernels experiment with various approaches to improving the performance of more traditional systems.