2013-05-17
CS 134:
Operating Systems
Multiprocessing
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CS34
CS 134:
Operating Systems
Multiprocessing
2013-05-17
Overview
Multiprocessing Designs
OS Implications
Programming Models
Other Issues
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CS34
Overview
Multiprocessing Designs
OS Implications
Overview
Programming Models
Other Issues
Multiprocessing Designs
2013-05-17
SIMD and MIMD
CS34
Multiprocessing Designs
SIMD and MIMD
Multiple CPUs come in several flavors:
SIMD: Single Instruction, Multiple Data
SIMD and MIMD
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Also called vector processor
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Sample instruction: a[i] = b[i] + c[i] for i in small
range (e.g., 0-3)
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Canonical example: GPUs
MIMD: Multiple Instruction, Multiple Data
I.e., 2 or more (semi-)independent CPUS
Multiple CPUs come in several flavors:
We won’t talk further about SIMD; from an OS point of view it’s just
another CPU.
SIMD: Single Instruction, Multiple Data
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Also called vector processor
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Sample instruction: a[i] = b[i] + c[i] for i in small
range (e.g., 0-3)
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Canonical example: GPUs
MIMD: Multiple Instruction, Multiple Data
I.e., 2 or more (semi-)independent CPUS
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Multiprocessing Designs
2013-05-17
MIMD Approaches
MIMD can be:
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Several chips or cores, (semi-)private memories, able to
access each other’s memory (NUMA—Non-Uniform Memory
Access)
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Several chips or cores, one memory (SMP—Symmetric
Multiprocessing)
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Several boxes (possibly each SMP or NUMA) connected by
network (distributed system)
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CS34
Multiprocessing Designs
MIMD Approaches
MIMD Approaches
MIMD can be:
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Several chips or cores, (semi-)private memories, able to
access each other’s memory (NUMA—Non-Uniform Memory
Access)
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Several chips or cores, one memory (SMP—Symmetric
Multiprocessing)
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Several boxes (possibly each SMP or NUMA) connected by
network (distributed system)
OS Implications
2013-05-17
NUMA Issues
NUMA means processes access local memory faster
⇒ Allocate process memory on local CPU
⇒ Processes should have “CPU affinity”
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OS Implications
NUMA Issues
NUMA means processes access local memory faster
⇒ Allocate process memory on local CPU
NUMA Issues
⇒ Processes should have “CPU affinity”
OS Implications
2013-05-17
SMP Issues
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OS Implications
SMP Issues
SMPs still have caches
Introduces cache coherency problems:
SMP Issues
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Processor 0 uses compare-and-swap to set a lock nonzero
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Write goes into local cache for speed
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Processor 1 reads lock from own cache, sees it’s still zero. . .
Cure: hardware coherency guarantees
. . . but spinlocks now have super-high costs
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May be better to do thread switch
SMPs still have caches
Thread switch is high cost, but may be cheaper than spinlock.
Introduces cache coherency problems:
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Processor 0 uses compare-and-swap to set a lock nonzero
I
Write goes into local cache for speed
I
Processor 1 reads lock from own cache, sees it’s still zero. . .
Cure: hardware coherency guarantees
. . . but spinlocks now have super-high costs
I
May be better to do thread switch
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OS Implications
2013-05-17
SMP Scheduling
Threads are often related
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Schedule independently or together?
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Completely independent: job completion is slowest thread
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Together: some CPUs may be wasted on waiting for events
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Always good to keep thread x on same CPU (because cache
is filled)
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OS Implications
SMP Scheduling
Threads are often related
SMP Scheduling
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Schedule independently or together?
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Completely independent: job completion is slowest thread
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Together: some CPUs may be wasted on waiting for events
I
Always good to keep thread x on same CPU (because cache
is filled)
OS Implications
2013-05-17
Distributed Systems
Many ways to communicate
Most important modern approach is. . .
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OS Implications
Distributed Systems
Many ways to communicate
Most important modern approach is. . .
Distributed Systems
OS Implications
2013-05-17
Distributed Systems
Many ways to communicate
Most important modern approach is. . . the Internet!
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CS34
OS Implications
Distributed Systems
Many ways to communicate
Most important modern approach is. . . the Internet!
Distributed Systems
OS Implications
2013-05-17
Distributed Systems
Many ways to communicate
Most important modern approach is. . . the Internet!
Communicating with skinny wires introduces new problems:
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Can’t move process to other machine (or must work hard)
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Locking becomes really hard
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Programming multiprocessor systems is much harder
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OS Implications
Distributed Systems
Many ways to communicate
Most important modern approach is. . . the Internet!
Communicating with skinny wires introduces new problems:
Distributed Systems
I
Can’t move process to other machine (or must work hard)
I
Locking becomes really hard
I
Programming multiprocessor systems is much harder
OS Implications
2013-05-17
Distributed Systems
Many ways to communicate
Most important modern approach is. . . the Internet!
Communicating with skinny wires introduces new problems:
I
Can’t move process to other machine (or must work hard)
I
Locking becomes really hard
I
Programming multiprocessor systems is much harder
I
. . . and what if network connection goes down?
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CS34
OS Implications
Distributed Systems
Many ways to communicate
Most important modern approach is. . . the Internet!
Communicating with skinny wires introduces new problems:
Distributed Systems
I
Can’t move process to other machine (or must work hard)
I
Locking becomes really hard
I
Programming multiprocessor systems is much harder
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. . . and what if network connection goes down?
Programming Models
2013-05-17
RPC
Programming is hard, so need abstractions that simplify things
Remote Procedure Call (RPC) makes distant system look like
normal function
1. Marshal arguments (i.e., pack up and serialize)
2. Send procedure ID and arguments to remote system
3. Wait for response
4. Deserialize return value
Class Exercise
What are the advantages and disadvantages?
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Programming Models
RPC
Programming is hard, so need abstractions that simplify things
Remote Procedure Call (RPC) makes distant system look like
normal function
1. Marshal arguments (i.e., pack up and serialize)
RPC
2. Send procedure ID and arguments to remote system
3. Wait for response
4. Deserialize return value
Class Exercise
What are the advantages and disadvantages?
Programming Models
2013-05-17
DSM
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Programming Models
RPC is nice, but limits parallelism
SMPs can do cool things because memory is shared
DSM
“Hard” is a gross understatement.
RPC is nice, but limits parallelism
SMPs can do cool things because memory is shared
So why not simulate shared memory across the network?
Teeny problem: hard to make it work fasta
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DSM
So why not simulate shared memory across the network?
Teeny problem: hard to make it work fasta
Other Issues
2013-05-17
Load Balancing
Suppose you have servers A, B, C, and D
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Other Issues
A notices the situation and sends excess work to C and D
Simultaneously, B does the same! Now C and D are overloaded
Result can be thrashing
Common solution: have one front-end machine whose sole job is
allocating load to others
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Suppose you have servers A, B, C, and D
A and B are currently overloaded, C and D underloaded
A notices the situation and sends excess work to C and D
Load Balancing
Simultaneously, B does the same! Now C and D are overloaded
Result can be thrashing
Common solution: have one front-end machine whose sole job is
allocating load to others
Random assignment works surprisingly well.
A and B are currently overloaded, C and D underloaded
Load Balancing
Other Issues
2013-05-17
How Does Google Work?
Well, it’s a secret. . .
But basically they use the front-end approach
Obvious problem: one front end can’t handle millions of requests
per second even if it does almost nothing
Solution: DNS Round Robin tricks you into picking one of many
dozens of front ends (roughly at random) to talk to
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CS34
Other Issues
How Does Google Work?
Well, it’s a secret. . .
But basically they use the front-end approach
How Does Google Work?
Obvious problem: one front end can’t handle millions of requests
per second even if it does almost nothing
Solution: DNS Round Robin tricks you into picking one of many
dozens of front ends (roughly at random) to talk to
Other Issues
2013-05-17
Example of Google’s DNS tricks
These commands were run within 15 seconds of each other:
bow:2:877> host www.google.com
www.google.com has address 74.125.224.241
www.google.com has address 74.125.224.242
www.google.com has address 74.125.224.243
www.google.com has address 74.125.224.244
www.google.com has address 74.125.224.240
bow:2:878> ssh
www.google.com
www.google.com
www.google.com
www.google.com
www.google.com
lever.cs.ucla.edu host www.google.com
has address 74.125.239.19
has address 74.125.239.20
has address 74.125.239.17
has address 74.125.239.18
has address 74.125.239.16
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CS34
Other Issues
Example of Google’s DNS tricks
Example of Google’s DNS tricks
These commands were run within 15 seconds of each other:
bow:2:877> host www.google.com
www.google.com has address 74.125.224.241
www.google.com has address 74.125.224.242
www.google.com has address 74.125.224.243
www.google.com has address 74.125.224.244
www.google.com has address 74.125.224.240
bow:2:878> ssh
www.google.com
www.google.com
www.google.com
www.google.com
www.google.com
lever.cs.ucla.edu host www.google.com
has address 74.125.239.19
has address 74.125.239.20
has address 74.125.239.17
has address 74.125.239.18
has address 74.125.239.16