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Transactional Memory Coherence and Consistency. Lance Hammond, Vicky Wong, Mike Chen, Brian D. Carlstrom, John D. Davis, Ben Hertzberg, Manohar K. Prabhu, Honggo Wijaya, Christos Kozyrakis, and Kunle Oluktun. Presented by Peter Gilbert ECE259 Spring 2008. Motivation. - PowerPoint PPT Presentation
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Transactional Memory Coherence and
ConsistencyLance Hammond, Vicky Wong, Mike Chen, Brian
D. Carlstrom, John D. Davis, Ben Hertzberg, Manohar K. Prabhu, Honggo Wijaya, Christos
Kozyrakis, and Kunle Oluktun
Presented by Peter GilbertECE259 Spring 2008
Motivation• Shared memory can support simple programming
models• However, shared memory means complex consistency
and cache coherence protocols• Consistency - must provide rules for ordering
loads/stores• Tradeoff between ease of use and performance (e.g.
SC vs. RC)• Cache coherence - must track ownership of cache lines
• Requires low latency for many small coherence messages
Motivation• Latency of small coherence messages unlikely to scale
well• Interprocessor bandwidth likely to scale better
• Message passing models can take advantage, but hard to program
• Can we design a shared memory model with:• Simple programming model• Simple hardware • Good performance• ...and take advantage of available bandwidth?
TCC• Transaction - sequence of instructions that executes speculatively
on a processor and completes only as an atomic unit• All writes are buffered locally and only committed to shared
memory when the transaction completes• Commit is atomic: entire effect of transaction committed to
shared memory state at once• Transaction’s effects not visible to other processors until
commit is performed• Processor broadcasts entire write buffer in one large commit
packet• High broadcast bandwidth needed, latency not as important• Interconnect need not provide ordering
• System-wide view: transactions appear to execute in commit order
Dependence violations
• Each processor must snoop on commit packets to detect violations
• Transaction detecting violation must rollback and restart• Register checkpointing mechanism needed
Consistency• Consistency is simplified:
• Other consistency models: ordering rules between individual memory references
• TCC: sequential ordering only between transaction commits• All references from an earlier commit appear to occur
“before” all references from later commit• Interleaving between memory accesses by different
processors only allowed at transaction boundaries• Can provide illusion of uniprocessor execution by
imposing original program’s transaction order
Coherence• Coherence is simplified:
• No ownership of cache lines• Writes are buffered
• Invalidation or update only occurs by snooping commit packets
• Don’t rely on many latency-sensitive coherence messages
Antidependencies
• TCC automatically handles WAW and WAR
WAW WAR
Programming model• Programmer inserts transaction boundaries
• Similar to threading, but no locks… less errors• One hard rule: transaction breaks cannot be placed
between a load and a subsequent store of a shared value• Steps for parallelizing code with TCC:
1. Divide into potentially parallel transactions• Examples: loop iterations, after function calls• Transactions need not be independent
• Dependence violations caught at runtime• Specify order• Tune performance
Transaction Ordering• Most programs require ordering between certain
transactions• Solution: assign phase number to each transaction
• Only transactions from the “oldest” phase can commit• Can implement barriers or full ordering
Performance tuning• How to choose transactions:
• Large transactions amortize startup and commit overhead
• Smaller transactions should be used when violations are frequent• Minimize amount of lost work
• TCC system provides feedback about violations to facilitate tuning
Hardware requirements
• Write buffer• Read bit(s), modified bit, optional renamed bits for L1 cache lines• System wide commit arbitration
Extensions• Double buffering
• Allow a processor to work on next transaction while previous transaction waits to commit
• Use additional write buffers and sets of read and modified bits
Without double buffering Extra write buffer Extra write buffer and read bits
Extensions• Hardware-controlled transactions
• Automatically divide program into transactions as buffers overflow• Take full advantage of available buffer space• Programmer must still mark critical regions where
transaction boundaries cannot occur• Automatically merge small transactions into larger ones
• I/O• Must guarantee no rollback after input is read
• Obtain commit permission before reading input• If ordering of outputs is important
• Same idea: request commit permission immediately
Evaluation• Can TCC extract parallelism for shared memory
benchmarks?• How large are the read and write states which must be
buffered?• What is the broadcast bandwidth requirement?
Simulation results• Optimal TCC model (infinite bus bandwidth, no memory
delays) extracted parallelism well for many benchmarksSpeedups for automatically and manually parallelized benchmarks with optimal TCC
model
Read and write state• Most benchmarks needed 6-12 KB of read state and 4-8 KB
of write state• Reasonable for current caches and on-chip write buffers
• Most of the benchmarks requiring large read and write state can probably be divided into smaller transactions (e.g. radix_l vs. radix_s)
Write state for smallest 10%, 50%, and 90% of iterations
Broadcast bandwidth• If invalidate protocol is used with 32-bit addresses,
average of 0.5 bytes/cycle for 32 processors• For update protocol, up to 16 bytes/cycle for 32
processors• If only dirty data is sent, only 8 bytes/cycle
Average bytes/cycle broadcast by 1 IPC system with an update protocol
Other parameters• Snooping requirement: significantly less than 1
address/cycle• Single snoop port per processor sufficient for up to 32
processors• Commit arbitration overhead: compiler-parallelized apps
were insensitive, while performance suffered for apps with smaller transaction sizes
• Extensions did not yield benefits in most cases• Extra read state bits (per-word rather than per-line)
only mattered for a few applications• Double-buffering did not help
• Will be useful when bandwidth is limited
Conclusions• TCC simplifies consistency and coherence
• No need for rules for ordering individual memory references• No need for latency-sensitive coherence messages
• TCC provides a simple and flexible programming model• Correctness is guaranteed: no error-prone locks• Tuning performance based on observed violations is
straightforward• Uniprocessor ordering can be achieved by ordering all transactions
• An optimal TCC implementation extracts parallelism well for a wide range of benchmarks
• Performance?• Will be limited by broadcast bandwidth and commit arbitration
overhead• Evaluation on a realistic hardware model necessary