Executing Multiple Thread on Modern Processor

Executing Multiple Threads
ECE/CS 752 Fall 2017
Prof. Mikko H. Lipasti
University of Wisconsin-Madison

Readings
• Read on your own:
– Shen & Lipasti Chapter 11
– G. S. Sohi, S. E. Breach and T.N. Vijaykumar. Multiscalar Processors,
Proc. 22nd Annual International Symposium on Computer
Architecture, June 1995.
– Dean M. Tullsen, Susan J. Eggers, Joel S. Emer, Henry M. Levy, Jack L.
Lo, and Rebecca L. Stamm. Exploiting Choice: Instruction Fetch and
Issue on an Implementable Simultaneous Multithreading Processor,
Proc. 23rd Annual International Symposium on Computer
Architecture, May 1996 (B5)
• To be discussed in class:
– Review #6 due 11/17/2017: Y.-H. Chen, J. Emer, V. Sze, "Eyeriss: A
Spatial Architecture for Energy-Efficient Dataflow for Convolutional
Neural Networks," International Symposium on Computer
Architecture (ISCA), pp. 367-379, June 2016. Online PDF

Executing Multiple Threads
• Thread-level parallelism
• Synchronization
• Multiprocessors
• Explicit multithreading
• Data parallel architectures
• Multicore interconnects
• Implicit multithreading: Multiscalar
• Niagara case study

Thread-level Parallelism
• Instruction-level parallelism
– Reaps performance by finding independent work in a single
thread
– Reaps performance by finding independent work across multiple
threads
• Historically, requires explicitly parallel workloads
– Originate from mainframe time-sharing workloads
– Even then, CPU speed >> I/O speed
– Had to overlap I/O latency with “something else” for the CPU to
do
– Hence, operating system would schedule other
tasks/processes/threads that were “time-sharing” the CPU

• Reduces effectiveness of temporal and spatial locality

• Initially motivated by time-sharing of single CPU
– OS, applications written to be multithreaded
• Quickly led to adoption of multiple CPUs in a single system
– Enabled scalable product line from entry-level single-CPU systems
to high-end multiple-CPU systems
– Same applications, OS, run seamlessly
– Adding CPUs increases throughput (performance)
• More recently:
– Multiple threads per processor core
• Coarse-grained multithreading (aka “switch-on-event”)
• Fine-grained multithreading
• Simultaneous multithreading
– Multiple processor cores per die
• Chip multiprocessors (CMP)
• Chip multithreading (CMT)

• Parallelism limited by sharing
– Amdahl’s law:
• Access to shared state must be serialized
• Serial portion limits parallel speedup
– Many important applications share (lots of) state
• Relational databases (transaction processing): GBs of shared state
– Even completely independent processes “share” virtualized
hardware through O/S, hence must synchronize access
• Access to shared state/shared variables
– Must occur in a predictable, repeatable manner
– Otherwise, chaos results
• Architecture must provide primitives for serializing access
to shared state

Some Synchronization Primitives
• Only one is necessary
– Others can be synthesized
Primitive Semantic Comments
Fetch-and-add Atomic load/add/store
operation
Permits atomic increment, can be
used to synthesize locks for
mutual exclusion
Compare-and-swap Atomic
load/compare/conditional
store
Stores only if load returns an
expected value
Load-linked/store-
conditional
Atomic load/conditional
store
Stores only if load/store pair is
atomic; that is, there is no
intervening store

Synchronization Examples
• All three guarantee same semantic:
– Initial value of A: 0
– Final value of A: 4
• b uses additional lock variable AL to protect critical section with a spin
lock
– This is the most common synchronization method in modern
multithreaded applications

Multiprocessor Systems
• Focus on shared-memory symmetric multiprocessors
– Many other types of parallel processor systems have been
proposed and built
– Key attributes are:
• Shared memory: all physical memory is accessible to all CPUs
• Symmetric processors: all CPUs are alike
– Other parallel processors may:
• Share some memory, share disks, share nothing
• Have asymmetric processing units
• Shared memory idealisms
– Fully shared memory: usually nonuniform latency
– Unit latency: approximate with caches
– Lack of contention: approximate with caches
– Instantaneous propagation of writes: coherence required

Cache Coherence Problem
P0 P1
Load A
A 0
Load A
A 0
Store A<= 1
1
Load A
Memory

Cache Coherence Problem
P0 P1
Load A
A 0
Load A
A 0
Store A<= 1
Memory
1
Load A
A 1

Invalidate Protocol
• Basic idea: maintain single writer property
– Only one processor has write permission at any point in time
• Write handling
– On write, invalidate all other copies of data
– Make data private to the writer
– Allow writes to occur until data is requested
– Supply modified data to requestor directly or through memory
• Minimal set of states per cache line:
– Invalid (not present)
– Modified (private to this cache)
• State transitions:
– Local read or write: I->M, fetch modified
– Remote read or write: M->I, transmit data (directly or through memory)
– Writeback: M->I, write data to memory

Invalidate Protocol
Optimizations
• Observation: data can be read-shared
– Add S (shared) state to protocol: MSI
– Local read: I->S, fetch shared
– Local write: I->M, fetch modified; S->M, invalidate other copies
– Remote read: M->S, supply data
– Remote write: M->I, supply data; S->I, invalidate local copy
• Observation: data can be write-private (e.g. stack frame)
– Avoid invalidate messages in that case
– Add E (exclusive) state to protocol: MESI
– Local read: I->E if only copy, I->S if other copies exist
– Local write: E->M silently, S->M, invalidate other copies

Sample Invalidate Protocol (MESI)
BR

Sample Invalidate Protocol (MESI)
Current
State s
Event and Local Coherence Controller Responses and Actions (s' refers to next state)
Local Read (LR) Local Write
(LW)
Local
Eviction (EV)
Bus Read
(BR)
Bus Write
(BW)
Bus Upgrade
(BU)
Invalid (I) Issue bus read
if no sharers
then s' = E
else s' = S
Issue bus
write
s' = M
s' = I Do nothing Do nothing Do nothing
Shared (S) Do nothing Issue bus
upgrade
s' = M
s' = I Respond
shared
s' = I s' = I
Exclusive
(E)
Do nothing s' = M s' = I Respond
shared
s' = S
s' = I Error
Modified
(M)
Do nothing Do nothing Write data
back;
s' = I
Respond
dirty;
Write data
back;
s' = S
Respond
dirty;
Write data
back;
s' = I
Error

Implementing Cache Coherence
• Snooping implementation
– Origins in shared-memory-bus systems
– All CPUs could observe all other CPUs requests on the bus;
hence “snooping”
• Bus Read, Bus Write, Bus Upgrade
– React appropriately to snooped commands
• Invalidate shared copies
• Provide up-to-date copies of dirty lines
– Flush (writeback) to memory, or
– Direct intervention (modified intervention or dirty miss)
• Snooping suffers from:
– Scalability: shared busses not practical
– Ordering of requests without a shared bus
– Lots of prior work on scaling snoop-based systems

Alternative to Snooping
• Directory implementation
– Extra bits stored in memory (directory) record MSI state of line
– Memory controller maintains coherence based on the current state
– Other CPUs’ commands are not snooped, instead:
• Directory forwards relevant commands
– Ideal filtering: only observe commands that you need to observe
– Meanwhile, bandwidth at directory scales by adding memory
controllers as you increase size of the system
• Leads to very scalable designs (100s to 1000s of CPUs)
• Directory shortcomings
– Indirection through directory has latency penalty
– Directory overhead for all memory, not just what is cached
– If shared line is dirty in other CPU’s cache, directory must forward
request, adding latency
– This can severely impact performance of applications with heavy
sharing (e.g. relational databases)

Memory Consistency
• How are memory references from different processors interleaved?
• If this is not well-specified, synchronization becomes difficult or even
impossible
– ISA must specify consistency model
• Common example using Dekker’s algorithm for synchronization
– If load reordered ahead of store (as we assume for a baseline OOO CPU)
– Both Proc0 and Proc1 enter critical section, since both observe that other’s
lock variable (A/B) is not set
• If consistency model allows loads to execute ahead of stores, Dekker’s
algorithm no longer works
– Common ISAs allow this: IA-32, PowerPC, SPARC, Alpha

Sequential Consistency [Lamport 1979]
• Processors treated as if they are interleaved processes on a single
time-shared CPU
• All references must fit into a total global order or interleaving that
does not violate any CPUs program order
– Otherwise sequential consistency not maintained
• Now Dekker’s algorithm will work
• Appears to preclude any OOO memory references
– Hence precludes any real benefit from OOO CPUs

High-Performance Sequential Consistency
• Coherent caches isolate CPUs if no sharing is
occurring
– Absence of coherence activity means CPU is free to
reorder references
• Still have to order references with respect to
misses and other coherence activity (snoops)
• Key: use speculation
– Reorder references speculatively
– Track which addresses were touched speculatively
– Force replay (in order execution) of such references
that collide with coherence activity (snoops)

Constraint graph example - SC
Proc 1
ST A
Proc 2
LD A
ST B
LD B
Program
order
Program
order
WAR
RAW
Cycle indicates that execution is
incorrect
1.
2.
3.
4.

Anatomy of a cycle
Proc 1
ST A
Proc 2
LD A
ST B
LD B
Program
order
Program
order
WAR
RAW
Incoming invalidate
Cache miss

High-Performance Sequential Consistency
• Load queue records all speculative loads
• Bus writes/upgrades are checked against LQ
• Any matching load gets marked for replay
• At commit, loads are checked and replayed if necessary
– Results in machine flush, since load-dependent ops must also replay
• Practically, conflicts are rare, so expensive flush is OK

Relaxed Consistency Models
• Key insight: only synchronizing references need ordering
• Hence, relax memory for all other references
– Enable high-performance OOO implementation
• Require programmer to label synchronization references
– Hardware must carefully order these labeled references
– All other references can be performed out of order
• Labeling schemes:
– Explicit synchronization ops (acquire/release)
– Memory fence or memory barrier ops:
• All preceding ops must finish before following ones begin
• Often: fence ops cause pipeline drain in modern OOO
machine
• More: ECE/CS 757

Split Transaction Bus
• “Packet switched” vs. “circuit switched”
• Release bus after request issued
• Allow multiple concurrent requests to overlap memory latency
• Complicates control, arbitration, and coherence protocol
– Transient states for pending blocks (e.g. “req. issued but not completed”)

Example: MSI (SGI-Origin-like, directory, invalidate)
High Level

High Level
Busy States

High Level
Busy States
Races

Multithreaded Cores
• 1990’s: Memory wall and multithreading
– Processor-DRAM speed mismatch:
• nanosecond to fractions of a microsecond (1:500)
– H/W task switch used to bring in other useful
work while waiting for cache miss
– Cost of context switch must be much less than
cache miss latency
• Very attractive for applications with
abundant thread-level parallelism
– Commercial multi-user workloads
33

Approaches to Multithreading
• Fine-grain multithreading
– Switch contexts at fixed fine-grain interval (e.g. every
cycle)
– Need enough thread contexts to cover stalls
– Example: Tera MTA, 128 contexts, no data caches
• Benefits:
– Conceptually simple, high throughput, deterministic
behavior
• Drawback:
– Very poor single-thread performance
34

• Coarse-grain multithreading
– Switch contexts on long-latency events (e.g. cache
misses)
– Need a handful of contexts (2-4) for most benefit
• Example: IBM RS64-IV (Northstar), 2 contexts
• Benefits:
– Simple, improved throughput (~30%), low cost
– Thread priorities mostly avoid single-thread
slowdown
• Drawback:
– Nondeterministic, conflicts in shared caches
35

• Simultaneous multithreading
– Multiple concurrent active threads (no notion of thread
switching)
– Need a handful of contexts for most benefit (2-8)
• Example: Intel Pentium 4/Nehalem/Sandybridge, IBM
Power 5/6/7, Alpha EV8/21464
• Benefits:
– Natural fit for OOO superscalar
– Improved throughput
– Low incremental cost
• Drawbacks:
– Additional complexity over OOO superscalar
– Cache conflicts
36

• Chip Multiprocessors (CMP)
Processor Cores/
chip
Multi-
threaded
?
Resources shared
IBM Power 4 2 No L2/L3, system interface
IBM Power 7 8 Yes (4T) Core, L2/L3, DRAM, system
interface
Sun Ultrasparc 2 No System interface
Sun Niagara 8 Yes (4T) Everything
Intel Pentium D 2 Yes (2T) Core, nothing else
Intel Core i7 4 Yes (2T) L3, DRAM, system interface
AMD Opteron 2, 4, 6,
12
No System interface (socket), L3

• Chip Multithreading (CMT)
– Similar to CMP
• Share something in the core:
– Expensive resource, e.g. floating-point unit (FPU)
– Also share L2, system interconnect (memory and I/O bus)
• Examples:
– Sun Niagara, 8 cores per die, one FPU
– AMD Bulldozer: one FP cluster for every two INT clusters
• Benefits:
– Same as CMP
– Further: amortize cost of expensive resource over multiple cores
• Drawbacks:
– Shared resource may become bottleneck
– 2nd
generation (Niagara 2) does not share FPU
38

Multithreaded/Multicore Processors
• Many approaches for executing multiple threads on a
single die
– Mix-and-match: IBM Power7 CMP+SMT
39
MT Approach Resources shared between threads Context Switch Mechanism
None Everything Explicit operating system context
switch
Fine-grained Everything but register file and control
logic/state
Switch every cycle
Coarse-grained Everything but I-fetch buffers, register file and
con trol logic/state
Switch on pipeline stall
SMT Everything but instruction fetch buffers, return
address stack, architected register file, control
logic/state, reorder buffer, store queue, etc.
All contexts concurrently active; no
switching
CMT Various core components (e.g. FPU), secondary
cache, system interconnect
All contexts concurrently active; no
switching
CMP Secondary cache, system interconnect All contexts concurrently active; no
switching

SMT Microarchitecture (from Emer, PACT ‘01)

SMT Performance (from Emer, PACT ‘01)

SMT Summary
• Goal: increase throughput
– Not latency
• Utilize execution resources by sharing among
multiple threads
• Usually some hybrid of fine-grained and SMT
– Front-end is FG, core is SMT, back-end is FG
• Resource sharing
– I$, D$, ALU, decode, rename, commit – shared
– IQ, ROB, LQ, SQ – partitioned vs. shared

Data Parallel Architectures
From [Lee et al., ISCA ‘11]
•Regular vs. Irregular data access, control flow

Data Parallel Execution
•MIMD vs. SIMD

Data Parallel Execution
•SIMT [Nvidia GPUs]
– Large number of threads, MIMD programmer view
– Threads ganged into warps, executed in SIMD fashion for
efficiency
– Control/data divergence causes inefficiency
– Programmer optimization required (ECE 759)

Multicore Interconnects
• Bus/crossbar - dismiss as short-term solutions?
• Point-to-point links, many possible topographies
– 2D (suitable for planar realization)
• Ring
• Mesh
• 2D torus
– 3D - may become more interesting with 3D packaging (chip
stacks)
• Hypercube
• 3D Mesh
• 3D torus
47

On-Chip Bus/Crossbar
• Used widely (Power4/5/6,/7 Piranha, Niagara, etc.)
– Assumed not scalable
– Is this really true, given on-chip characteristics?
– May scale "far enough" : watch out for arguments at the
limit
• Simple, straightforward, nice ordering properties
– Wiring is a nightmare (for crossbar)
– Bus bandwidth is weak (even multiple busses)
– Workload:
• “Commercial” applications usually latency-limited
• “Scientific” applications usually bandwidth-limited
48

On-Chip Ring
• Point-to-point ring interconnect
– Simple, easy
– Nice ordering properties (unidirectional)
– Every request a broadcast (all nodes can snoop)
– Scales poorly: O(n) latency, fixed bandwidth
49

On-Chip Mesh
• Widely assumed in academic literature
• Tilera (MIT startup), Intel 80-core prototype
• Not symmetric, so have to watch out for load
imbalance on inner nodes/links
– 2D torus: wraparound links to create symmetry
• Not obviously planar
• Can be laid out in 2D but longer wires, more intersecting
links
• Latency, bandwidth scale well
• Lots of existing literature
50

On-Chip Interconnects
• More coverage in ECE/CS 757 (usually)
• Synthesis lecture:
– Natalie Enright Jerger & Li-Shiuan Peh, “On-Chip
Networks”, Synthesis Lectures on Computer
Architecture
– http://www.morganclaypool.com/doi/abs/10.220
0/S00209ED1V01Y200907CAC008
51

Implicitly Multithreaded Processors
• Goal: speed up execution of a single thread
(latency)
• Implicitly break program up into multiple smaller
threads, execute them in parallel, e.g.:
– Parallelize loop iterations across multiple processing
units
– Usually, exploit control independence in some fashion
– Not parallelism of order 100x, more like 3-5x
• Typically, one of two goals:
– Expose more ILP for a single window, or
– Build a more scalable, partitioned execution window
• Or, try to achieve both

Implicitly Multithreaded Processors
• Many challenges:
– Find additional ILP, past hard-to-predict branches
• Control independence
– Maintain data dependences (RAW, WAR, WAW) for
registers
– Maintain precise state for exception handling
– Maintain memory dependences (RAW/WAR/WAW)
– Maintain memory consistency model
• Still a research topic
– Multiscalar reading provides historical context
– Lots of related work in TLS (thread-level speculation)

Multiscalar
• Seminal work on implicit multithreading
– Started in mid 80’s under Guri Sohi @ Wisconsin
• Solved many of the “hard” problems
• Threads or tasks identified by compiler
– Tasks look like mini-programs, can contain loops, branches
• Hardware consists of a ring of processing nodes
– Head processor executes most speculative task
– Tail processor commits and resolves
– Miss-speculation causes task and all newer tasks to get flushed
• Nodes connected to:
– Sequencing unit that dispatches tasks to each one
– Shared register file that resolves RAW/WAR/WAW
– Address Resolution Buffer: resolves memory dependences
• http://www.cs.wisc.edu/mscalar
– Publications, theses, tools, contact information

Niagara Case Study
• Targeted application: web servers
– Memory intensive (many cache misses)
– ILP limited by memory behavior
– TLP: Lots of available threads (one per client)
• Design goal: maximize throughput (/watt)
• Results:
– Pack many cores on die (8)
– Keep cores simple to fit 8 on a die, share FPU
– Use multithreading to cover pipeline stalls
– Modest frequency target (1.2 GHz)

Niagara Block Diagram [Source: J. Laudon]
• 8 in-order cores, 4 threads each
• 4 L2 banks, 4 DDR2 memory controllers

Ultrasparc T1 Die Photo [Source: J. Laudon]

Niagara Pipeline [Source: J. Laudon]
• Shallow 6-stage pipeline
• Fine-grained multithreading

Power Consumption[Source: J. Laudon]

Thermal Profile
• Low operating temp
• No hot spots
• Improved reliability
• No need for exotic
cooling

T2000 System Power
• 271W running SpecJBB2000
• Processor is only 25% of total
• DRAM & I/O next, then conversion losses

Niagara Summary
• Example of application-specific system
optimization
– Exploit application behavior (e.g. TLP, cache misses,
low ILP)
– Build very efficient solution
• Downsides
– Loss of general-purpose suitability
– E.g. poorly suited for software development
(parallel make, gcc)
– Very poor FP performance (fixed in Niagara 2)

Lecture Summary
• Synchronization
• Multiprocessors
• Explicit multithreading
• Data parallel architectures
• Multicore interconnects
• Implicit multithreading: Multiscalar
• Niagara case study

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