Data-Centric Execution of Speculative Parallel Programs MA MARK - - PowerPoint PPT Presentation

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Data-Centric Execution of Speculative Parallel Programs

MA MARK JEFFREY, SUVINAY SUBRAMANIAN, MALEEN ABEYDEERA, JOEL EMER, DANIEL SANCHEZ MI MICRO 2016

SLIDE 2

Executive summary

Many-cores must exploit cache locality to scale Current speculative systems, e.g. TLS or TM, do not exploit locality Spatial Hints: run tasks likely to access the same data in the same place

• A software-given hint denotes the data a new task is likely to access
• Hardware maps tasks with the same hint to the same place
• Hardware uses hints to perform locality-aware load balancing

Our techniques make speculative parallelism practical at large scale

• It is easy to modify programs to convey locality through hints
• Performance improves by 3.3x at 256 cores
• We reduce network traffic by 6.4x and wasted work by 3.5x

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 3

Prior speculative systems scale poorly

TRANSACTIONAL MEMORY (TM) SCHEDULERS

Reduce wasted work of coarse-grain txns Limit concurrency: When to run a task?

SPATIAL HINTS

Make accesses local for fine-grain tasks Less data movement: Where to run a task?

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 4

Prior speculative systems scale poorly

TRANSACTIONAL MEMORY (TM) SCHEDULERS

Reduce wasted work of coarse-grain txns Limit concurrency: When to run a task?

SPATIAL HINTS

Make accesses local for fine-grain tasks Less data movement: Where to run a task?

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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Spatially map tasks for improved locality and less waste

SLIDE 5

Prior non-speculative locality techniques do not work for speculation

STATIC TASK MAPPING

Data dependences known a priori

• Linear algebra, Anton 2 [ASPLOS ‘13]

Graph partitioning

• Localizes communication and scheduling
• Slow preprocessing step
• Cannot adapt to imbalance

DYNAMIC TASK MAPPING

Work stealing

• Cheap, local enqueues
• Steals to adapt to imbalance
• Limited application types
• Stealing interfereswith speculation

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 6

Baseline Architecture: Swarm [MICRO ‘15]

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 7

General execution model supports

• rdered and unordered parallelism

Baseline Swarm execution model

Programs consist of timestamped tasks

• Tasks can create children tasks with >= timestamp
• Tasks appear to execute in timestamp order

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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swarm::enqueue(function_pointer, timestamp, arguments...);

SLIDE 8

Baseline Swarm architecture

Speculatively executes tasks out of order Large hardware task queues Scalable ordered speculation Scalable ordered commits

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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64-tile, 256-core chip Tile organization

Core Core Core Core L1I/D L1I/D L1I/D L1I/D

L2 L3 slice

Router

Task unit

Mem / IO Mem / IO Mem / IO Mem / IO

Tile

Efficiently supports tiny speculative tasks

SLIDE 9

Spatial Hints in Action

COMBINING SPECULATION AND LOCALITY

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 10

Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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E A B r s t D C

r s t = r XOR s

1 1 1 1

SLIDE 11

Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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E A B r s t 1 1 D C

r s t = r XOR s

1 1 1 1

SLIDE 12

Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

E A B r s t 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

E A B r s t 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

D0=1

E A B r s t 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

D0=1

E A B r s t 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1

E A B r s t 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1

E A B r s t 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1

E A B r s t 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1

E A B r s t 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1

E A B r s t 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1

E A B r s t 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1

E A B r s t 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

SLIDE 23

Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1

E A B r s t 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1

E A B r s t 1 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1

E A B r s t 1 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1

E A B r s t 1 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

9

r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1 D1=0

E A B r s t 1 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

9

r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1 D1=0

E A B r s t 1 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

9

r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1 D1=0 E1=0

E A B r s t 1 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1 D1=0 E1=0 t=0

E A B r s t 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Example: Discrete event simulation (DES)

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r=1 A=1

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C0=0 D0=1 E1=1 t=1 s=1 C1=1 B=1 D1=0 E1=0 t=0

E A B r s t 1 1 1 1 D C

r s t = r XOR s

1 1 1 1

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Extracting parallelism in DES

Execute independent tasks out of order

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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r A

Order = Simulated time (ns)

1 2 3 4 5 6

Tasks

C D E t s C B D E t

Data dependences

r A C D E t s C B D E t

Valid Schedule

2.4x parallelism (more in larger circuits)

Parallelism is plentiful despite data dependences

SLIDE 33

1 128 256 Speedup 1c 128c 256c 1 128 256 Speedup 1c 128c 256c

Speculation scales poorly without locality

Swarm sends new tasks to random tiles

• Good for load balance
• Poor locality hurts scalability beyond 100 cores

Work stealing: a non-speculative scheduler

• Enqueuenew tasks locally
• Steal from the most-loaded tile
• Not a good strategy for DES

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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des

Random Stealing

SLIDE 34

Where is the locality?

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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Each task operates on a single gate The gate is known when the task is created With fine-grain tasks, most data accessed is known at creation time

r A C D E t s C B D E t

DES Schedule

SLIDE 35

1 128 256 Speedup 1c 128c 256c 1 128 256 Speedup 1c 128c 256c

Data-centric speculation scales well

Hints: map each gate to a statically-chosen tile

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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des

Stealing Random Hints

But we can do better!

A B D C

• 1. Less data movement
• 2. Conflicts are local, cheap, and less frequent

Send new tasks for a gate to its corresponding tile

D

186x

E

E

SLIDE 36

1 128 256 Speedup 1c 128c 256c

Load-balanced speculation scales best

Static gate-to-tile mapping may cause hotspots

• E.g. some gates toggle more frequently

Dynamically remap gates (Hints) across tiles

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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1 128 256 Speedup 1c 128c 256c

des

Stealing Random Hints Load-Balanced Hints

Programmer knows most of the data accessed Spatial Hints convey program-level knowledge to exploit locality

236x

SLIDE 37

Spatial Hints Implementation

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 38

Hint mechanisms are straightforward

SOFTWARE

A Spatial Hint is an integer value

• Given at task creation time
• Denotes data likely to be accessed by the task
• E.g. the gate ID in DES

HARDWARE

Hashes each new task’s Hint to a tile ID Serializes same-Hint tasks

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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7 4 1 1

Localize most data accesses within a tile Serialize tasks likely to conflict

SLIDE 39

Load balance with a level of indirection

Static hint-to-tile mapping may cause imbalance

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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Tile ID 2 H Hint 0xF00 2 Bucket H Reconfigurable Tile Map Tile ID Hint 0xF00

1 7 1 … 61 63 40

Instead, periodically remap hints across tiles to equalize load

SLIDE 40

“Load” is different for speculation

Non-speculative systems use # queued tasks as a proxy for load When imbalanced, speculative systems often

• Don’t run out of work
• Abort more work or strain speculation resources

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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Remap hints to tiles to balance # of committed cycles per tile

2 Bucket H Reconfigurable Tile Map Tile ID Hint 0xF00

1 7 1 … 61 63 40

SLIDE 41

Adding hints to applications is easy

void desTask(Timestamp ts, GateInput* input) { Gate* g = input->gate(); bool toggledOutput = g.simulateToggle(input); if (toggledOutput) { // Toggle all inputs connected to this gate for (GateInput* i : g->connectedInputs()) swarm::enqueue(desTask,

/*Timestamp*/ ts + delay(g, i),

/*Hint*/ i->gate()->id, i); } }

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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One line of code to express the Gate ID as a Hint

SLIDE 42

Benchmark Hint Why? des Gate ID Map tasks for same gate to same tile nocsim Router ID Frequent intra-router communication bfs, sssp, astar, color Cache-line address Several vertices reside on the same line silo (Table ID, primary key) Each task accesses one database tuple genome, kmeans Multiple

Adding hints to applications is easy

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 43

See the paper for more details!

Load balance reconfiguration algorithm Choice of application hints Relationship between task size and hint effectiveness

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 44

Evaluation

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 45

Methodology

Event-driven, Pin-based simulator Target system: 256-core, 64-tile chip Scalability experiments from 1–256 cores

• Scaled-down systems have fewer tiles

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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Core Core Core Core L1I/D L1I/D L1I/D L1I/D

L2 L3 slice

Router

Task unit

Mem / IO Mem / IO Mem / IO Mem / IO

Tile

64 MB shared L3 (1MB/tile) 256 KB per-tile L2s 16 KB per-core L1s 16K task queue entries (64/core) 4K commit queue entries (16/core) In-order, single-issue, scoreboarded

SLIDE 46

Load-Balanced Hints 3.3x faster than Random (193x gmean vs 58x)

1 256 512 Speedup

bfs

1 256 512

sssp

1 128 256

astar

1 64 128 Speedup

color

1 128 256

des

1 256 512

nocsim

1 128 256 Speedup 1c 128c 256c

silo

1 64 128 1c 128c 256c

genome

1 128 256 1c 128c 256c

kmeans Random

1 256 512 Speedup

bfs

1 256 512

sssp

1 128 256

astar

1 64 128 Speedup

color

1 128 256

des

1 256 512

nocsim

1 128 256 Speedup 1c 128c 256c

silo

1 64 128 1c 128c 256c

genome

1 128 256 1c 128c 256c

kmeans Hints Random

1 256 512 Speedup

bfs

1 256 512

sssp

1 128 256

astar

1 64 128 Speedup

color

1 128 256

des

1 256 512

nocsim

1 128 256 Speedup 1c 128c 256c

silo

1 64 128 1c 128c 256c

genome

1 128 256 1c 128c 256c

kmeans LBHints Hints Random

1 256 512 Speedup

bfs

1 256 512

sssp

1 128 256

astar

1 64 128 Speedup

color

1 128 256

des

1 256 512

nocsim

1 128 256 Speedup 1c 128c 256c

silo

1 64 128 1c 128c 256c

genome

1 128 256 1c 128c 256c

kmeans LBHints Hints Random Stealing

Hints make speculation practical

• n large-scale systems

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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Stealing is inconsistent across benchmarks Load-Balanced Hints 17% – 27% faster than Hints

SLIDE 47

Hints make speculation more efficient

0.0 0.2 0.4 0.6 0.8 1.0

Aborted Cycles

R L R L R L R L R L R L R L R L R L

bfs sssp astar color des nocsim silo genome kmeans DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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0.0 0.2 0.4 0.6 0.8 1.0

NoC data transferred

R L R L R L R L R L R L R L R L R L

bfs sssp astar color des nocsim silo genome kmeans

Reduce wasted work by 6.4x Reduce network traffic by 3.5x

SLIDE 48

Conclusion

Speculative architectures must exploit locality to scale to 100s of cores

• Important to simplify parallel programming

Spatial Hints convey app-level knowledge to exploit cache locality Hardware leverages hints by:

• Sending tasks likely to access the same data to the same tile
• Serializing tasks likely to conflict
• Balancing work in a locality-aware and speculation-friendly way

Our techniques make speculation practical on large-scale systems

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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SLIDE 49

Thank you! Questions?

Speculative architectures must exploit locality to scale to 100s of cores

• Important to simplify parallel programming

Spatial Hints convey app-level knowledge to exploit cache locality Hardware leverages hints by:

• Sending tasks likely to access the same data to the same tile
• Serializing tasks likely to conflict
• Balancing work in a locality-aware and speculation-friendly way

Our techniques make speculation practical on large-scale systems

DATA-CENTRIC EXECUTION OF SPECULATIVE PARALLEL PROGRAMS

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