01/31/2005CS267 Lecture 41 CS 267: Shared Memory Machines Programming Example: Sharks and Fish James...
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Transcript of 01/31/2005CS267 Lecture 41 CS 267: Shared Memory Machines Programming Example: Sharks and Fish James...
01/31/2005 CS267 Lecture 4 1
CS 267: Shared Memory
MachinesProgramming
Example: Sharks and Fish
James [email protected]
www.cs.berkeley.edu/~demmel/cs267_Spr05
01/31/2005 CS267 Lecture 4 2
Basic Shared Memory Architecture
• Processors all connected to a large shared memory• Where are caches?
• Now take a closer look at structure, costs, limits, programming
P1
interconnect
memory
P2 Pn
01/31/2005 CS267 Lecture 4 3
Outline
• Evolution of Hardware and Software• CPUs getting exponentially faster than memory they share• Hardware evolves to try to match speeds
• Program semantics evolve too• Programs change from correct to buggy, unless programmed
carefully
• Performance evolves as well• Well tuned programs today may be inefficient tomorrow
• Goal: teach a programming style likely to stay correct, if not always as efficient as possible
• Use locks to avoid race conditions• Current research seeks best of both worlds
• Example: Sharks and Fish (part of next homework)
01/31/2005 CS267 Lecture 4 4
Processor-DRAM Gap (latency)
µProc60%/yr.
DRAM7%/yr.
1
10
100
1000
1980
1981
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
DRAM
CPU1982
Processor-MemoryPerformance Gap:(grows 50% / year)
Per
form
ance
Time
“Moore’s Law”
01/31/2005 CS267 Lecture 4 5
Shared Memory Code for Computing a Sums = f(A[0]) + f(A[1])
Thread 0
s = s + f(A[0])
Thread 1
s = s + f(A[1])
static int s = 0;
• Might get f(A[0]) + f(A[1]) or f(A[0]) or f(A[1])• Problem is a race condition on variable s in the program• A race condition or data race occurs when:
- two processors (or two threads) access the same variable, and at least one does a write.
- The accesses are concurrent (not synchronized) so they could happen simultaneously
01/31/2005 CS267 Lecture 4 6
Approaches to Building Parallel Machines
P1
Switch
Main memory
Pn
(Interleaved)
(Interleaved)
First-level $
P1
$
Interconnection network
$
Pn
Mem Mem
P1
$
Interconnection network
$
Pn
Mem MemShared Cache
Centralized MemoryUMA = Uniform Memory Access
Distributed Memory (NUMA = Non-UMA)
Scale
01/31/2005 CS267 Lecture 4 7
Shared Cache: Advantages and Disadvantages
Advantages• Cache placement identical to single cache
• Only one copy of any cached block• Can’t have values of same memory location in different caches
• Fine-grain sharing is possible• Good Interference
• One processor may prefetch data for another• Can share data within a line without moving line
Disadvantages• Bandwidth limitation• Bad Interference
• One processor may flush another processors data
01/31/2005 CS267 Lecture 4 9
Evolution of Shared Cache• Alliant FX-8 (early 1980s)
• eight 68020s with x-bar to 512 KB interleaved cache
• Encore & Sequent (1980s)• first 32-bit micros (N32032)• two to a board with a shared cache
• Disappeared for a while, and then …• Cray X1 shares L3 cache• IBM Power 4 and Power 5 share L2 cache• If switch and cache on chip, may have enough bandwidth again
01/31/2005 CS267 Lecture 4 10
Approaches to Building Parallel Machines
P1
Switch
Main memory
Pn
(Interleaved)
(Interleaved)
First-level $
P1
$
Interconnection network
$
Pn
Mem Mem
P1
$
Interconnection network
$
Pn
Mem MemShared Cache
Centralized MemoryUMA = Uniform Memory Access
Distributed Memory (NUMA = Non-UMA)
Scale
11CS267 Lecture 401/31/2005
Intuitive Memory Model
• Reading an address should return the last value written to that address
• Easy in uniprocessors• except for I/O
• Cache coherence problem in MPs is more pervasive and more performance critical
• More formally, this is called sequential consistency:“A multiprocessor is sequentially consistent if the result of any execution is the same as if the operations of all the processors were executed in some sequential order, and the operations of each individual processor appear in this sequence in the order specified by its program.” [Lamport, 1979]
01/31/2005 CS267 Lecture 4 12
Sequential Consistency Intuition
• Sequential consistency says the machine behaves as if it does the following
memory
P0 P1 P2 P3
01/31/2005 CS267 Lecture 4 13
Memory Consistency Semantics
What does this imply about program behavior?• No process ever sees “garbage” values, I.e., ½ of 2 values• Processors always see values written by some some processor• The value seen is constrained by program order on all
processors• Time always moves forward
• Example: spin lock• P1 writes data=1, then writes flag=1• P2 waits until flag=1, then reads data
initially: flag=0 data=0
data = 1flag = 1
10: if flag=0, goto 10…= data
If P2 sees the new value of flag (=1), it must see the new value of data (=1)
P1 P2
If P2 reads flag
Then P2 may read data
0 1
0 0
1 1
01/31/2005 CS267 Lecture 4 14
If Caches are Not “Coherent”• Coherence means different copies of same location have same
value• p1 and p2 both have cached copies of data (as 0)• p1 writes data=1
• May “write through” to memory• p2 reads data, but gets the “stale” cached copy
• This may happen even if it read an updated value of another variable, flag, that came from memory
data 0 data 0
data = 0
p1 p2
data 1
01/31/2005 CS267 Lecture 4 15
Snoopy Cache-Coherence Protocols
• Memory bus is a broadcast medium• Caches contain information on which addresses they store• Cache Controller “snoops” all transactions on the bus
• A transaction is a relevant transaction if it involves a cache block currently contained in this cache
• Take action to ensure coherence• invalidate, update, or supply value
• Many possible designs (see CS252 or CS258)
StateAddressData
P0
$ $
Pn
Mem Mem
memory busmemory op from Pn
bus snoop
01/31/2005 CS267 Lecture 4
Limits of Bus-Based Shared Memory
I/O MEM MEM° ° °
PROC
cache
PROC
cache
° ° °
Assume:1 GHz processor w/o cache
=> 4 GB/s inst BW per processor (32-bit)=> 1.2 GB/s data BW at 30% load-store
Suppose 98% inst hit rate and 95% data hit rate
=> 80 MB/s inst BW per processor=> 60 MB/s data BW per processor140 MB/s combined BW
Assuming 1 GB/s bus bandwidth 8 processors will saturate bus
5.2 GB/s
140 MB/s
01/31/2005 CS267 Lecture 4 17
Sample Machines
• Intel Pentium Pro Quad• Coherent• 4 processors
• Sun Enterprise server • Coherent• Up to 16 processor and/or
memory-I/O cards
• IBM Blue Gene/L• L1 not coherent, L2 shared
P-Pro bus (64-bit data, 36-bit addr ess, 66 MHz)
CPU
Bus interface
MIU
P-Promodule
P-Promodule
P-Promodule256-KB
L2 $Interruptcontroller
PCIbridge
PCIbridge
Memorycontroller
1-, 2-, or 4-wayinterleaved
DRAM
PC
I bus
PC
I busPCI
I/Ocards
Gigaplane bus (256 data, 41 addr ess, 83 MHz)
SB
US
SB
US
SB
US
2 F
iber
Cha
nnel
100b
T, S
CS
I
Bus interface
CPU/memcardsP
$2
$
P
$2
$
Mem ctrl
Bus interface/switch
I/O cards
01/31/2005 CS267 Lecture 4 18
Approaches to Building Parallel Machines
P1
Switch
Main memory
Pn
(Interleaved)
(Interleaved)
First-level $
P1
$
Interconnection network
$
Pn
Mem Mem
P1
$
Interconnection network
$
Pn
Mem MemShared Cache
Centralized MemoryUMA = Uniform Memory Access
Distributed Memory (NUMA = Non-UMA))
Scale
01/31/2005 CS267 Lecture 4 19
Basic Choices in Memory/Cache Coherence
• Keep Directory to keep track of which memory stores latest copy of data
• Directory, like cache, may keep information such as:• Valid/invalid• Dirty (inconsistent with memory)• Shared (in another caches)
• When a processor executes a write operation to shared data, basic design choices are:• With respect to memory:
• Write through cache: do the write in memory as well as cache• Write back cache: wait and do the write later, when the item is flushed
• With respect to other cached copies• Update: give all other processors the new value• Invalidate: all other processors remove from cache
• See CS252 or CS258 for details
01/31/2005 CS267 Lecture 4
SGI Altix 3000
• A node contains up to 4 Itanium 2 processors and 32GB of memory• Network is SGI’s NUMAlink, the NUMAflex interconnect technology. • Uses a mixture of snoopy and directory-based coherence• Up to 512 processors that are cache coherent (global address space
is possible for larger machines)
01/31/2005 CS267 Lecture 4 21
Cache Coherence and Sequential Consistency
• There is a lot of hardware/work to ensure coherent caches• Never more than 1 version of data for a given address in caches• Data is always a value written by some processor
• But other HW/SW features may break sequential consistency (SC):• The compiler reorders/removes code (e.g., your spin lock)• The compiler allocates a register for flag on Processor 2 and spins on that
register value without every completing• Write buffers (place to store writes while waiting to complete)
• Processors may reorder writes to merge addresses (not FIFO)• Write X=1, Y=1, X=2 (second write to X may happen before Y’s)
• Prefetch instructions cause read reordering (read data before flag)• The network reorders the two write messages. • The write to flag is nearby, whereas data is far away.• Some of these can be prevented by declaring variables volatile
• Most current commercial SMPs give up SC
01/31/2005 CS267 Lecture 4 22
Programming with Weaker Memory Models than SC
• Possible to reason about machines with fewer properties, but difficult
• Some rules for programming with these models• Avoid race conditions• Use system-provided synchronization primitives• If you have race conditions on variables, make them
volatile • At the assembly level, may use fences (or analog)
directly• The high level language support for these differs
• Built-in synchronization primitives normally include the necessary fence operations
• lock (), … only one thread at a time allowed here…. unlock()• Region between lock/unlock called critical region
• For performance, need to keep critical region short
01/31/2005 CS267 Lecture 4 23
Improved Code for Computing a Sums = f(A[0]) + … + f(A[n-1])
Thread 1
local_s1= 0 for i = 0, n/2-1 local_s1 = local_s1 + f(A[i]) s = s + local_s1
Thread 2
local_s2 = 0 for i = n/2, n-1 local_s2= local_s2 + f(A[i]) s = s +local_s2
static int s = 0;
• Since addition is associative, it’s OK to rearrange order
static lock lk;
lock(lk); lock(lk);
unlock(lk);unlock(lk);
01/31/2005 CS267 Lecture 4 24
Improved Code for Computing a Sums = f(A[0]) + … + f(A[n-1])
Thread 1
local_s1= 0 for i = 0, n/2-1 local_s1 = local_s1 + f(A[i]) s = s + local_s1
Thread 2
local_s2 = 0 for i = n/2, n-1 local_s2= local_s2 + f(A[i]) s = s +local_s2
static int s = 0;
• Since addition is associative, it’s OK to rearrange order• Critical section smaller
• Most work outside it
static lock lk;
lock(lk);
unlock(lk);
lock(lk);
unlock(lk);
01/31/2005 CS267 Lecture 4
Caches and Scientific Computing
• Caches tend to perform worst on demanding applications that operate on large data sets
• transaction processing• operating systems• sparse matrices
• Modern scientific codes use tiling/blocking to become cache friendly
• easier for dense matrix codes (eg matmul) than for sparse• tiling and parallelism are similar transformations
01/31/2005 CS267 Lecture 4
Sharing: A Performance Problem
• True sharing• Frequent writes to a variable can create a bottleneck• OK for read-only or infrequently written data• Technique: make copies of the value, one per processor, if this
is possible in the algorithm• Example problem: the data structure that stores the
freelist/heap for malloc/free
• False sharing• Cache block may also introduce artifacts• Two distinct variables in the same cache block• Technique: allocate data used by each processor contiguously,
or at least avoid interleaving• Example problem: an array of ints, one written frequently by
each processor
01/31/2005 CS267 Lecture 4 27
What to Take Away?
• Programming shared memory machines• May allocate data in large shared region without too many
worries about where• Memory hierarchy is critical to performance
• Even more so than on uniprocs, due to coherence traffic
• For performance tuning, watch sharing (both true and false)
• Semantics• Need to lock access to shared variable for read-modify-write• Sequential consistency is the natural semantics• Architects worked hard to make this work
• Caches are coherent with buses or directories• No caching of remote data on shared address space machines
• But compiler and processor may still get in the way• Non-blocking writes, read prefetching, code motion…• Avoid races or use machine-specific fences carefully
01/31/2005 CS267 Lecture 4 28
Creating Parallelism with
Threads
01/31/2005 CS267 Lecture 4 29
Programming with Threads
Several Thread Libraries• PTHREADS is the Posix Standard
• Solaris threads are very similar• Relatively low level• Portable but possibly slow
• OpenMP is newer standard• Support for scientific programming on shared memory• http://www.openMP.org
• P4 (Parmacs) is another portable package• Higher level than Pthreads• http://www.netlib.org/p4/index.html
01/31/2005 CS267 Lecture 4 30
Language Notions of Thread Creation
• cobegin/coend
• fork/join
• cobegin cleaner, but fork is more general
cobegin job1(a1); job2(a2);coend
• Statements in block may run in parallel
• cobegins may be nested
• Scoped, so you cannot have a missing coend
tid1 = fork(job1, a1);job2(a2);join tid1; • Forked function runs in parallel with current
• join waits for completion (may be in different function)
01/31/2005 CS267 Lecture 4 31
Forking Posix Threads
• thread_id is the thread id or handle (used to halt, etc.)• thread_attribute various attributes
• standard default values obtained by passing a NULL pointer
• thread_fun the function to be run (takes and returns void*)• fun_arg an argument can be passed to thread_fun when it starts• errorcode will be set nonzero if the create operation fails
Signature: int pthread_create(pthread_t *, const pthread_attr_t *, void * (*)(void *), void *);
Example call: errcode = pthread_create(&thread_id; &thread_attribute &thread_fun; &fun_arg);
Signature:
01/31/2005 CS267 Lecture 4 32
Posix Thread Example#include <pthread.h>void print_fun( void *message ) { printf("%s \n", message);} main() { pthread_t thread1, thread2; char *message1 = "Hello"; char *message2 = "World"; pthread_create( &thread1,
NULL, (void*)&print_fun, (void*) message1);
pthread_create(&thread2, NULL, (void*)&print_fun, (void*) message2);
return(0);}
Compile using gcc –lpthreadSee Millennium/Seaborg docs for paths/modules
Note: There is a race condition in the print statements
01/31/2005 CS267 Lecture 4 33
Loop Level Parallelism
• Many scientific application have parallelism in loops• With threads: … my_stuff [n][n]; for (int i = 0; i < n; i++) for (int j = 0; j < n; j++) … pthread_create (update_cell, …, my_stuff[i][j]);
• But overhead of thread creation is nontrivial
Also need i & j
01/31/2005 CS267 Lecture 4 34
Shared Data and Threads
• Variables declared outside of main are shared• Object allocated on the heap may be shared (if pointer is
passed)• Variables on the stack are private: passing pointer to
these around to other threads can cause problems
• Often done by creating a large “thread data” struct• Passed into all threads as argument
01/31/2005 CS267 Lecture 4 35
Barrier -- global synchronization• fork multiple copies of the same function “work”
• SPMD “Single Program Multiple Data”
• simple use of barriers -- all threads hit the same one work_on_my_subgrid(); barrier; read_neighboring_values(); barrier;
• more complicated -- barriers on branches (or loops) if (tid % 2 == 0) { work1(); barrier } else { barrier }
• barriers are not provided in many thread libraries
Basic Types of Synchronization: Barrier
01/31/2005 CS267 Lecture 4 36
Basic Types of Synchronization: Mutexes
Mutexes -- mutual exclusion aka locks• threads are working mostly independently• need to access common data structure
lock *l = alloc_and_init(); /* shared */ acquire(l); access data release(l);
• Java and other languages have lexically scoped synchronization
• similar to cobegin/coend vs. fork and join
• Semaphores give guarantees on “fairness” in getting the lock, but the same idea of mutual exclusion
• Locks only affect processors using them: • pair-wise synchronization
01/31/2005 CS267 Lecture 4 37
A Model Problem: Sharks and Fish
• Illustration of parallel programming• Original version (discrete event only) proposed by
Geoffrey Fox• Called WATOR
• Sharks and fish living in a 2D toroidal ocean
• We can imagine several variation to show different physical phenomenon
• Basic idea: sharks and fish living in an ocean• rules for movement• breeding, eating, and death• forces in the ocean• forces between sea creatures
01/31/2005 CS267 Lecture 4 38
Particle Systems
• A particle system has • a finite number of particles.• moving in space according to Newton’s Laws (i.e. F = ma).• time is continuous.
• Examples:• stars in space with laws of gravity.• electron beam and ion beam semiconductor manufacturing.• atoms in a molecule with electrostatic forces.• neutrons in a fission reactor.• cars on a freeway with Newton’s laws plus model of driver and
engine.
• Many simulations combine particle simulation techniques with some discrete event techniques (e.g., Sharks and Fish).
01/31/2005 CS267 Lecture 4 39
Forces in Particle Systems
• Force on each particle decomposed into near and far:
force = external_force + nearby_force + far_field_force
• External force• ocean current to sharks and fish world• externally imposed electric field in electron beam.
• Nearby force• sharks attracted to eat nearby fish balls on a billiard table
bounce off of each other.• Van der Waals forces in fluid (1/r6).
• Far-field force• fish attract other fish by gravity-like (1/r2 ) force • gravity, electrostatics• forces governed by elliptic PDE.
01/31/2005 CS267 Lecture 4 40
Parallelism in External Forces
• External forces are the simplest to implement.• The force on each particle is independent of other
particles.• Called “embarrassingly parallel”.
• Evenly distribute particles on processors• Any even distribution works.• Locality is not an issue, no communication.
• For each particle on processor, apply the external force.
01/31/2005 CS267 Lecture 4 41
Parallelism in Nearby Forces• Nearby forces require interaction and therefore communication.• Force may depend on other nearby particles:
• Example: collisions.• simplest algorithm is O(n2): look at all pairs to see if they collide.
• Usual parallel model is decomposition* of physical domain:• O(n2/p) particles per processor if evenly distributed.• Often called domain decomposition (which also refers to numerical alg.)
• Challenges:• Dealing with particles near processor boundaries• Dealing with load imbalance from nonuniformly distributed particles
Need to check for collisions between regions
01/31/2005 CS267 Lecture 4 42
Parallelism in Far-Field Forces
• Far-field forces involve all-to-all interaction and therefore communication.
• Force depends on all other particles:• Examples: gravity, protein folding• Simplest algorithm is O(n2)• Just decomposing space does not help since every particle
needs to “visit” every other particle.
• Use more clever algorithms to lower O(n2) to O(n log n)• Several later lectures
Implement by rotating particle sets.
• Keeps processors busy
• All processor eventually see all particles
01/31/2005 CS267 Lecture 4 43
Examine Sharks and Fish code
• Gravitational forces among fish only• Use Euler’s method to move fish numerically
• Sequential and Shared Memory with Pthreads:• www.cs.berkeley.edu/~demmel/cs267_Spr05/SharksAndFish
01/31/2005 CS267 Lecture 4 44
Extra Slides
01/31/2005 CS267 Lecture 4 45
Engineering: Intel Pentium Pro Quad
SMP for the masses:• All coherence and
multiprocessing glue in processor module
• Highly integrated, targeted at high volume
• Low latency and bandwidth
P-Pro bus (64-bit data, 36-bit addr ess, 66 MHz)
CPU
Bus interface
MIU
P-Promodule
P-Promodule
P-Promodule256-KB
L2 $Interruptcontroller
PCIbridge
PCIbridge
Memorycontroller
1-, 2-, or 4-wayinterleaved
DRAM
PC
I bus
PC
I busPCI
I/Ocards
01/31/2005 CS267 Lecture 4 46
Engineering: SUN Enterprise
• Proc + mem card - I/O card• 16 cards of either type• All memory accessed over bus, so symmetric• Higher bandwidth, higher latency bus
Gigaplane bus (256 data, 41 addr ess, 83 MHz)
SB
US
SB
US
SB
US
2 F
iber
Cha
nnel
100b
T, S
CS
I
Bus interface
CPU/memcardsP
$2
$
P
$2
$
Mem ctrl
Bus interface/switch
I/O cards
01/31/2005 CS267 Lecture 4 47
Outline
• Historical perspective• Bus-based machines
• Pentium SMP• IBM SP node
• Directory-based (CC-NUMA) machine• Origin 2000
• Global address space machines• Cray t3d and (sort of) t3e
01/31/2005 CS267 Lecture 4
60s Mainframe Multiprocessors
• Enhance memory capacity or I/O capabilities by adding memory modules or I/O devices
• How do you enhance processing capacity?• Add processors
• Already need an interconnect between slow memory banks and processor + I/O channels
• cross-bar or multistage interconnection network
Proc
I/ODevices
Interconnect
Proc
Mem IOCMem Mem Mem IOC
P IO IOP
M
M
M
M
01/31/2005 CS267 Lecture 4
70s Breakthrough: Caches
• Memory system scaled by adding memory modules• Both bandwidth and capacity
• Memory was still a bottleneck• Enter… Caches!
• Cache does two things:• Reduces average access time (latency)• Reduces bandwidth requirements to memory
P
memory (slow)
interconnect
I/O Deviceor
Processor
A: 17
processor (fast)
01/31/2005 CS267 Lecture 4
Technology Perspective
DRAM
Year Size Cycle Time
1980 64 Kb 250 ns
1983 256 Kb 220 ns
1986 1 Mb 190 ns
1989 4 Mb 165 ns
1992 16 Mb 145 ns
1995 64 Mb 120 ns
1000:1! 2:1!
Capacity Speed
Logic: 2x in 3 years 2x in 3 years
DRAM: 4x in 3 years 1.4x in 10 years
Disk: 2x in 3 years 1.4x in 10 years
0
50
100
150
200
250
300
350
1986 1988 1990 1992 1994 1996
Year
SpecInt
SpecFP
01/31/2005 CS267 Lecture 4 51
Example: Write-thru Invalidate
• Update and write-thru both use more memory bandwidth if there are writes to the same address• Update to the other caches• Write-thru to memory
I/O devices
Memory
P1
$ $ $
P2 P3
u :51
u :5
2
u :5
3
u = 7
5
u = ?
4
u = ?
CS267 Lecture 401/31/2005
Write-Back/Ownership Schemes
• When a single cache has ownership of a block, processor writes do not result in bus writes, thus conserving bandwidth.
• reads by others cause it to return to “shared” state
• Most bus-based multiprocessors today use such schemes.
• Many variants of ownership-based protocols
01/31/2005 CS267 Lecture 4
Directory-Based Cache-Coherence
01/31/2005 CS267 Lecture 4
90 Scalable, Cache Coherent Multiprocessors
P
Cache
P
Cache
Interconnection Netw ork
Memory presence bits
dirty-bit
Directory
memory block
1 n
01/31/2005 CS267 Lecture 4
Cache Coherenceand
Memory Consistency
01/31/2005 CS267 Lecture 4 56
Violations of Sequential Consistency
• Flag/data program is one example that relies on SC• Given coherent memory,all violations of SC based on
reordering on independent operations are figure 8s• See paper by Shasha and Snir for more details
• Operations can be linearized (move forward time) if SC
read y write x
P0 P1 P2write x write y read ytim
e (p
rog
ram o
rde
r)
01/31/2005 CS267 Lecture 4 57
Sufficient Conditions for Sequential Consistency
• Processors issues memory operations in program order• Processor waits for store to complete before issuing any
more memory operations• E.g., wait for write-through and invalidations
• Processor waits for load to complete before issuing any more memory operations
• E.g., data in another cache may have to be marked as shared rather than exclusive
• A load must also wait for the store that produced the value to complete
• E.g., if data is in cache and update event changes value, all other caches much also have processed that update
• There are much more aggressive ways of implementing SC, but most current commercial SMPs give up
Based on slide by Mark Hill et al
01/31/2005 CS267 Lecture 4 58
Classification for Relaxed Models
• Optimizations can generally be categorized by• Program order relaxation:
• Write Read• Write Write • Read Read, Write
• Read others’ write early• Read own write early
• All models provide safety net, e.g., • A write fence instruction waits for writes to complete• A read fence prevents prefetches from moving before this point• Prefetches may be synchronized automatically on use
• All models maintain uniprocessor data and control dependences, write serialization
• Memory models differ on orders to two different locations
Slide source: Sarita Adve et al
01/31/2005 CS267 Lecture 4 59
Some Current System-Centric Models
SYNCPowerPC
various MEMBARsRMO
MB, WMBAlpha
release, acquire, nsync, RMW
RCpc
release, acquire, nsync, RMW
RCsc
synchronizationWO
RMW, STBARPSO
RMWPC
RMWTSO
serialization instructions
IBM 370
Safety NetRead Own Write Early
Read Others’ Write Early
R RW Order
W W Order
W R Order
Relaxation:
Slide source: Sarita Adve et al
01/31/2005 CS267 Lecture 4 60
Data-Race-Free-0: Some Definitions
• (Consider SC executions global total order)• Two conflicting operations race if
– From different processors– Execute one after another (consecutively)
P1 P2 Write, A, 23 Write, B, 37 Read, Flag, 0 Write, Flag, 1
Read, Flag, 1 Read, B, ___
Read, A, ___ • Races usually labeled as synchronization, others data• Can optimize operations that never race
Slide source: Sarita Adve et al
01/31/2005 CS267 Lecture 4
Cache-Coherent Shared Memoryand Performance