Multiple Processor Systems

Bits of Chapters 4, 10, 16

Operating Systems:Internals and Design Principles, 6/E

William Stallings

Parallel Processor Architectures

Multiprocessor Systems

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

Multiprocessors

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

Multiprocessor Hardware

Bus-based multiprocessors

Non-blocking network

UMA Multiprocessor using a crossbar switch

Blocking network

Omega Switching Network

Master-Slave multiprocessors

Bus

Symmetric Multiprocessors

Bus

Symmetric Multiprocessor Organization

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

Multiprocessor Synchronization

TSL fails if bus is not locked

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)

Scheduling Design Issues

• Assignment of processes to processors

• Use of multiprogramming on individual processors

• Actual dispatching of a process

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

Assignment of Processes to Processors (2)

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

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

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

Traditional Process Scheduling

• Single queue for all processes

• Multiple queues are used for priorities

• All queues feed to the common pool of processors

Thread Scheduling

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

• True parallelism

Comparison One and Two Processors

Comparison One and Two Processors (2)

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

Load Sharing

• Load is distributed evenly across the processors

• No centralized scheduler required

• Use global queues

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

Multiprocessor Scheduling (3)

• Problem with communication between two threads– both belong to process A– both running out of phase

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

Dedicated Processor Assignment

• When application is scheduled, its threads are assigned to a processor

• Some processors may be idle

• No multiprogramming of processors

Application Speedup

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

Generic Client/Server Environment

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

Generic Client/Server Architecture

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

Role of Middleware in Client/Server Architecture

Distributed Message Passing

Basic Message-Passing Primitives

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

Blocking versus Nonblocking

• Nonblocking– Process is not suspended as a result of

issuing a Send or Receive– Efficient and flexible– Difficult to debug

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

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

Remote Procedure Call

Remote Procedure Call Mechanism

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

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

Clusters

• Alternative to symmetric multiprocessing (SMP)

• Group of interconnected, whole computers (nodes) working together as a unified computing resource– Illusion is one machine

No Shared Disks

Shared Disk

Clustering Methods: Benefits and Limitations

Clustering Methods: Benefits and Limitations

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

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

Multicomputer Scheduling - Load Balancing (1)

• Graph-theoretic deterministic algorithm

Process

Load Balancing (2)

• Sender-initiated distributed heuristic algorithm– overloaded sender

Load Balancing (3)

• Receiver-initiated distributed heuristic algorithm– under loaded receiver

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

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

Clusters Compared to SMP

• Clusters are better for incremental and absolute scalability

• Clusters are superior in terms of availability