Shared Memory

1
Shared Memory Consistency of Shared Variables

• The ideal picture of
• shared memory

The actual architecture of shared memory systems
2
The Million s QuestionHow/When Does One
ProcessRead Other Processs Writes?
Assumption Initial value of shared variables is
always 0.
Why is this a question? Because temporal order
relations like before/after do not necessarily
hold in a distributed system.
3
Non-Atomic writes/reads(also called loads/stores)

• A read by Pi is considered performed with
  respect to Pk at a point in time when the issuing
  of a write to the same address by Pk cannot
  affect the value returned by the read.
• A write by Pi is considered performed with
  respect to Pk at a point in time when an issued
  read to the same address by Pk returns the value
  defined by this write (or a subsequent write to
  the same location).
• An access is performed when it is performed with
  respect to all processors.
• A read is globally performed if it is performed
  and if the write that is the source of the
  returned value has been performed.
• In what follows, we will think of atomic
  read/write but these definitions can be used to
  generalize.

4
Why Memory Model?
Answers the question Which writes by a process
are seen by which reads of the other processes?
5
Memory Consistency Models
Pi R V W V,7 R V R V Pj R V W V,13 R V R V
Example program
A consistency/memory model is an agreement
between the execution environment (H/W, OS,
middleware) and the processes. Runtime guarantees
to the application certain properties on the way
values written to shared variables become visible
to reads. This determines the memory model,
whats valid, whats not.
6
Memory Model Coherence

• Coherence is the memory model in which (the
  runtime guarantees to the program that) writes
  performed by the processes for every specific
  variable are viewed by all processes in the same
  full order.

Example program
All valid executions under Coherence
Note the view of a process consists of the
values it sees in its reads, and the writes it
performs. Thus, if a R V in P which is later than
a W V,x in P sees a value different than x, then
a later R V cannot see x.
7
Formal definition of Coherence

• Program Order The order in which instructions
  appear in each process. This is a partial order
  on all the instructions in the program.
• A serialization A full order on all the
  instructions (reads/writes) of all the processes,
  which is consistent with the program order.
• A legal serialization A serialization in which
  each read X returns the value written by the
  latest write X in the full order.
• Let P be a program let PX be the sub-program
  of P which contains all the read X/write X
  operations on X only.
• Coherence P is said to be coherent if for every
  variable X there exists a legal serialization of
  PX. (Note a process cannot distinguish one such
  serialization from another for a given execution)

8
Examples
Process 2 read y,1 write x,1
Process 2 read y,1 write x,1
Coherent. Serializations x write x,1, read
x,1 y write y,1, read y,1
Process 1 read x,1 write x,2
Process 2 read x,2 write x,1
Not Coherent. Cycle of dependencies. Cannot be
serialized.
Not Coherent. Cannot be serialized.
9
Sequential Consistency Lamport 1979

• Sequential Consistency is the memory model in
  which all reads/writes performed by the processes
  are viewed by all processes in the same full
  order.

Coherent. Not Sequentially consistent.
Coherent. Not Sequentially consistent.
10
Strict (Strong) Memory Models
Sequential Consistency Given an execution, there
exists an order of reads/writes which is
consistent with all program orders.
Coherence For any variable x, there exists an
order of read x/write x consistent with all p.o.s.
11
Formal definition of Sequential Consistency

• Let P be a program.
• Sequential Consistency P is said to be
  sequentially consistent if there exists a legal
  serialization of all reads/writes in P.

Observation Every program which is sequentially
consistent is also coherent. Conclusion Sequentia
l Consistency has stronger requirements and we
thus say that it is stronger than Coherence. In
general A consistency model A is said to be
(strictly) stronger than B if all executions
which are valid under A are also valid under B.
12
The problem of strong consistency models

• The runtime system should ensure the existence of
  legal serialization, and the same consistent view
  for all processes.
• This requires lots of expensive coordination ?
  degrades performance!

SC Hardware cannot reorder locally in each
thread for this will result in a possible
printing 1,1. HW may reorder anyway and postpone
writes, but then why reorder in the first place?
13
Coherence Forbids Reordering
q.x is aliased to p.x. Reordering may make
assignment to B early (seeing 0) and that to A
late (seeing 1). The right thread see order of
writes different from left thread.

• Once thread sees an update cannot forget it
  has seen it.
• Cannot reorder two reads of the same memory
  location.

14
Coherence makes reads preventcommon compiler
optimizations
15
Causal Consistency

• If event B is caused or influenced by an earlier
  event A, Causal Consistency requires that all the
  processes first see A, then see B.
• Causally related events Events A and B are
  causally related if A causes or influences B.
• Concurrent events Events A and B are
  concurrent/independent if they are not causally
  related.
• Formally
• Write After Read When a Read operation is
  followed in program order by a Write operation,
  then the two events are potentially causally
  related.
• Read After Write A Read(x) operation is causally
  related to the Write(x) operation that provided
  the data the Read got.
• Transitive closure when there is a C so A is
  potentially causally related to C and C is
  potentially causally related to B then A is
  causally related to B.
• Casual Consistency An execution obeys causal
  consistency if Writes that are potentially
  casually related are seen by all processes in the
  same order. Concurrent writes may be seen in a
  different order on different machines.

16
Example Causally Consistent
P1 W1(x),1 W1(x),3
P2 R2(x),1 W2(x),2
P3 R3(x),1 R3(x),3 R3(x),2
P4 R4(x),1 R4(x),2 R4(x),3

• W2(x),2 and W1(x),3 are concurrent events (so it
  is not required that all
• processes see them in the same order).
• The only writes that are potentially causally
  related are W1(x),1 and W2(x),2.
• The above sequence of events is Causally
  Consistent but not Sequentially
• Consistent (the readings of P3 R3(x),3
  and R3(x),2 are not allowed in a
• sequentially consistent memory model).

17
Example Causally Inconsistent
P1 W1(x),1
P2 R2(x),1 W2(x),2
P3 R3(x),2 R3(x),1
P4 R4(x),1 R4(x),2

• W2(x),2 is potentially causally related to
  W1(x),1 (because the writing of 2
• may result from the value read by
  R2(x),1).
• Since the two writes are potentially causally
  related all processes must see
• them in the same order, however, P3 and
  P4 see them in a different order.

18
Example Causally Consistent
P1 W1(x),1
P2 W2(x),2
P3 R3(x),2 R3(x),1
P4 R4(x),1 R4(x),2

• W2(x),2 and W1(x),1 are concurrent events (the
  read R2(x),1 was removed).
• Since the two writes are concurrent they may be
  seen in a different order by
• different processes.