Multiple Processor Systems

1
Multiple Processor Systems
Operating SystemsInternals and Design
Principles, 6/EWilliam Stallings

• Bits of Chapters 4, 10, 16

2
Parallel Processor Architectures
3
Multiprocessor Systems

• Continuous need for faster computers
• shared memory model
• message passing multiprocessor
• wide area distributed system

4
Multiprocessors

• DefinitionA computer system in which two or
  more CPUs share full access to a common RAM

5
Multiprocessor Hardware

• Bus-based multiprocessors

6
Non-blocking network

• UMA Multiprocessor using a crossbar switch

7
Blocking network

• Omega Switching Network

8
Master-Slave multiprocessors
Bus
9
Symmetric Multiprocessors
Bus
10
Symmetric Multiprocessor Organization
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

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

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

16
Assignment of Processes to Processors (2)

• Dynamic assignment Global queue
• Schedule to any available processor
• Process migration, cf. local cache

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

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

19
Traditional Process Scheduling

• Single queue for all processes
• Multiple queues are used for priorities
• All queues feed to the common pool of processors

20
Thread Scheduling

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

21
Comparison One and Two Processors
22
Comparison One and Two Processors (2)
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

24
Load Sharing

• Load is distributed evenly across the processors
• No centralized scheduler required
• Use global queues

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

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

28
Dedicated Processor Assignment

• When application is scheduled, its threads are
  assigned to a processor
• Some processors may be idle
• No multiprogramming of processors

29
Application Speedup
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

31
Generic Client/Server Environment
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

33
Generic Client/Server Architecture
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

35
Role of Middleware in Client/Server Architecture
36
Distributed Message Passing
37
Basic Message-Passing Primitives
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

39
Blocking versus Nonblocking

• Nonblocking
• Process is not suspended as a result of issuing a
  Send or Receive
• Efficient and flexible
• Difficult to debug

40
Blocking versus Nonblocking

• Blocking
• 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

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

42
Remote Procedure Call
43
Remote Procedure Call Mechanism
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

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

46
Clusters

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

47
No Shared Disks
48
Shared Disk
49
Clustering Methods Benefits and Limitations
50
Clustering Methods Benefits and Limitations
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

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

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

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

58
Clusters Compared to SMP

• Clusters are better for incremental and absolute
  scalability
• Clusters are superior in terms of availability