1. Multiple Processor Systems
Operating Systems: Internals and Design Principles, 6/E William Stallings
Multiple Processor Systems
Bits of Chapters 4, 10, 16 1

2. Parallel Processor Architectures2

3. Multiprocessor Systems
Continuous need for faster computers
shared memory model
message passing multiprocessor
wide area distributed system

4. Multiprocessors
Definition: A computer system in which two or more CPUs share full access to a common RAM

5. Multiprocessor Hardware
Bus-based multiprocessors

6. UMA Multiprocessor using a crossbar switch
Non-blocking network
UMA Multiprocessor using a crossbar switch

7. Omega Switching Network
Blocking network
Omega Switching Network

8. Master-Slave multiprocessors
Bus

9. Symmetric Multiprocessors
Bus

10. Symmetric Multiprocessor Organization10

11. Multiprocessor Operating Systems Design Considerations
Simultaneous concurrent processes or threads: reentrant routines, IPC
Scheduling: on which processor a process should run
Synchronization: locks
Memory management: e.g. shared pages
Reliability and fault tolerance: graceful degradation 11

12. Multiprocessor Synchronization
TSL fails if bus is not locked

13. Spinning versus Switching
In some cases CPU must wait
waits to acquire ready list
In other cases a choice exists
spinning wastes CPU cycles
switching uses up CPU cycles also
possible to make separate decision each time locked mutex encountered (e.g. using history)

14. Scheduling Design Issues
Assignment of processes to processors
Use of multiprogramming on individual processors
Actual dispatching of a process 14

15. Assignment of Processes to Processors
Treat processors as a pooled resource and assign process to processors on demand
Static assignment: Permanently assign process to a processor
Dedicate short-term queue for each processor
Less overhead
Processor could be idle while another processor has a backlog 15

16. Assignment of Processes to Processors (2)
Dynamic assignment: Global queue
Schedule to any available processor
Process migration, cf. local cache 16

17. Master/slave architecture
Key kernel functions always run on a particular processor
Master is responsible for scheduling
Slave sends service request to the master
Disadvantages
Failure of master brings down whole system
Master can become a performance bottleneck 17

18. Peer architecture Kernel can execute on any processor
Each processor does self-scheduling
Complicates the operating system
Make sure two processors do not choose the same process 18

19. Traditional Process Scheduling
Single queue for all processes
Multiple queues are used for priorities
All queues feed to the common pool of processors 19

20. Thread Scheduling
An application can be a set of threads that cooperate and execute concurrently in the same address space
True parallelism 20

21. Comparison One and Two Processors21

22. Comparison One and Two Processors (2)22

23. Multiprocessor Thread Scheduling
Load sharing
Threads are not assigned to a particular processor
Gang scheduling
A set of related threads is scheduled to run on a set of processors at the same time
Dedicated processor assignment
Threads are assigned to a specific processor 23

24. Load Sharing Load is distributed evenly across the processors
No centralized scheduler required
Use global queues 24

25. Disadvantages of Load Sharing
Central queue needs mutual exclusion
Preemptive threads are unlikely to resume execution on the same processor
If all threads are in the global queue, not all threads of a program will gain access to the processors at the same time 25

26. Multiprocessor Scheduling (3)
Problem with communication between two threads
both belong to process A
both running out of phase

27. Gang Scheduling
Simultaneous scheduling of threads that make up a single process
Useful for applications where performance severely degrades when any part of the application is not running
Threads often need to synchronize with each other 27

28. Dedicated Processor Assignment
When application is scheduled, its threads are assigned to a processor
Some processors may be idle
No multiprogramming of processors 28

29. Application Speedup 29

30. Client/Server Computing
Client machines are generally single-user PCs or workstations that provide a highly user-friendly interface to the end user
Each server provides a set of shared services to the clients
The server enables many clients to share access to the same database and enables the use of a high-performance computer system to manage the database 30

31. Generic Client/Server Environment31

32. Client/Server Applications
Basic software is an operating system running on the hardware platform
Platforms and the operating systems of client and server may differ
These lower-level differences are irrelevant as long as a client and server share the same communications protocols and support the same applications 32

33. Generic Client/Server Architecture33

34. Middleware
Set of tools that provide a uniform means and style of access to system resources across all platforms
Enable programmers to build applications that look and feel the same
Enable programmers to use the same method to access data 34

35. Role of Middleware in Client/Server Architecture35

36. Distributed Message Passing36

37. Basic Message-Passing Primitives37

38. Reliability versus Unreliability
Reliable message-passing guarantees delivery if possible
Not necessary to let the sending process know that the message was delivered
Send the message out into the communication network without reporting success or failure
Reduces complexity and overhead 38

39. Blocking versus Nonblocking
Process is not suspended as a result of issuing a Send or Receive
Efficient and flexible
Difficult to debug 39

40. Blocking versus Nonblocking
Send does not return control to the sending process until the message has been transmitted
OR does not return control until an acknowledgment is received
Receive does not return until a message has been placed in the allocated buffer 40

41. Remote Procedure Calls
Allow programs on different machines to interact using simple procedure call/return semantics
Widely accepted
Standardized
Client and server modules can be moved among computers and operating systems easily 41

42. Remote Procedure Call 42

43. Remote Procedure Call Mechanism43

44. Client/Server Binding
Binding specifies the relationship between remote procedure and calling program
Nonpersistent binding
Logical connection established during remote procedure call
Persistent binding
Connection is sustained after the procedure returns 44

45. Synchronous versus Asynchronous
Synchronous RPC
Behaves much like a subroutine call
Asynchronous RPC
Does not block the caller
Enable a client execution to proceed locally in parallel with server invocation 45

46. Clusters Alternative to symmetric multiprocessing (SMP)
Group of interconnected, whole computers (nodes) working together as a unified computing resource
Illusion is one machine 46

47. No Shared Disks 47

48. Shared Disk 48

49. Clustering Methods: Benefits and Limitations49

50. Clustering Methods: Benefits and Limitations50

51. Operating System Design Issues
Failure management
Highly available cluster offers a high probability that all resources will be in service
No guarantee about the state of partially executed transactions if failure occurs
Fault-tolerant cluster ensures that all resources are always available 51

52. Operating System Design Issues (2)
Load balancing
When new computer added to the cluster, the load-balancing facility should automatically include this computer in scheduling applications 52

53. Multicomputer Scheduling - Load Balancing (1)
Process
Graph-theoretic deterministic algorithm

54. Load Balancing (2) Sender-initiated distributed heuristic algorithm
overloaded sender

55. Load Balancing (3) Receiver-initiated distributed heuristic algorithm
under loaded receiver

56. Operating System Design Issues (3)
Parallelizing Computation
Parallelizing compiler: determines at compile time which parts can be executed parallel
Parallelized application: by programmer, message passing for moving data
Parametric computing: several runs with different settings – simulation model 56

57. Clusters Compared to SMP
SMP is easier to manage and configure
SMP takes up less space and draws less power
SMP products are well established and stable 57

58. Clusters Compared to SMP
Clusters are better for incremental and absolute scalability
Clusters are superior in terms of availability 58