1. Background Computer System Architectures Computer System Software

2. Computer System Architectures Centralized (Tightly Coupled) Distributed (Loosely Coupled)

3. 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

4. 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

5. M MM Interconnection Network – Bus-based or Switched (all accesses to memory go through the network) P0 P1 P2 Tightly-Coupled Architectures CPUs are connected at the bus level

6. 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.

7. 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.

8. 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

9. 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

10. 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coherence: If one processor changes data in its cache other processors must be notified that their cached copy is out of date

11. 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.

12. M M M 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) P0 P1 P2 Loosely-Coupled Architectures

13. 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.

14. Examples of Distributed Systems Massively Parallel Processors (MPPs) Cluster Grid Network of Workstations

15. 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

16. 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.

17. 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.

18. 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.

19. Computer System Software Operating Systems Middleware

20. 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, …

21. Operating Systems General purpose operating systems Real time operating systems Embedded systems

22. 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

23. 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

24. 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.

25. 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.