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Correct and efficient implementations of synchronous models on asynchronous execution platforms

Stavros TripakisUC Berkeley and Verimag

EC^2 Workshop, Grenoble, June 2009

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Some observations

• Concurrency => interleaving– C.f., synchronous systems (e.g., circuits)

• Concurrency => non-determinism– synchronous circuits are deterministic

• Concurrency => shared memory– C.f., data flow models

• Asynchronous concurrency (interleaving) => non-determinism – C.f., Kahn Process Networks

Threads have conquered the world, but …

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What are the problems we (as a community) are trying to solve?

• Cope with concurrency… but what does it mean?• What are the right execution platforms?– Which multicore architecture, memory model, …

• What are the right programming models?• For which types of applications?• How to map the latter to the former?– Correctly and efficiently!

• How to verify stuff?

± given,synchronous

given,asynchronous

focus

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Synchronous vs. asynchronous concurrency

• Synchronous concurrency– Execution platforms: synchronous hardware– Programming models: Simulink, SCADE, synchronous

languages (Esterel, Lustre, …), …• Asynchronous concurrency– Execution platforms: many, including distributed

platforms– Programming models: thread-based (often

communicating by shared-memory)

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Concurrency => non-determinism

• Most synchronous models are deterministic: synchronous hardware, Simulink, SCADE, most synchronous languages, …

Copyright The Mathworks

Engine control model in Simulink

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Concurrency => non-determinism

• Some asynchronous models are also deterministic, e.g.:– Kahn Process Networks: the sequence of values

(stream) produced at each FIFO is the same independent of process interleaving

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Our choice of programming model: synchronous

• Set of parallel processes, notion of global synchronous cycle– Simulink, SCADE, VHDL, Verilog, Lustre, Esterel, …

• Main advantages:– Determinism, no process interleaving:

• Easier to understand, easier to verify (less state explosion)

• Main objections:– “Synchrony is impossible/hard/too expensive to implement”– “This is especially true for distributed systems”

• “You need clock synchronization”– Practice seems to agree with this…

• Most available implementations of synchronous systems are either synchronous hardware, or centralized “read; compute; write;” control loops.

– …but it is not quite true.

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Semantics-preserving implementation of synchronous models

Simulink

single-processorsingle-task

single-processormulti-task

distributed,synchronous(TTA) …

…

distributed,asynchronous(KPN, LTTA, ...)

application

executionplatform

design

implementation

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From synchronous models to asynchronous distributed implementations

Joint work with Claudio Pinello, Cadence

Alberto Sangiovanni-Vincentelli, UC BerkeleyAlbert Benveniste, IRISA (France)

Paul Caspi, VERIMAG (France)Marco di Natale, SSSA (Italy)

[IEEE Trans. Computers, Oct’08]

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Implementation on asynchronous distributed platforms

• Asynchronous distributed platforms:– Many computers, each with a

local clock• No clock synchronization

– Computers communicate using some network/protocol• Don’t care which network, as

long as finite FIFO queues (TCP) can be implemented on top

synchronous model

asynchronous platformwith some communication network

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Implementation on asynchronous distributed platforms

synchronous model

asynchronous platformwith some communication network

Intermediate layer:asynchronous processes

communicating with finite FIFO queues

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Implementation on asynchronous distributed platforms

synchronous model

Intermediate layer:asynchronous processes

communicating with finite FIFO queues

This is like Kahn Process Networks with blocking write()

when FIFO is full.

FIFOs must be largeenough to avoid

deadlocks.

=> semantical (stream) preservation

Semantical preservation: proof

• Use old theories [1970s]:

• Marked graphs– Subclass of Petri Nets– Used to show FFP liveness (no

deadlock)

• Kahn Process Networks– Used Kahn’s fundamental result:

determinism– Streams do not depend on

process interleaving13

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Performance analysis: worst-case logical-time throughput and latency

Computing worst-case logical-time throughput

1Reachability lasso of marked graph

LT thput = 3/4

deterministic firing policy

Relating real-time and logical-time throughput

P1 P2

P1 P2

WCLTT = 1/2

WCLTT = 1

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From synchronous models toasynchronous multitask implementations

Joint work with Paul Caspi,

Norman Scaife, Christos Sofronis,

VERIMAG

[ACM Trans. Embed. Comp. Sys., Feb’08]

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Implementation on centralized, multitasking platforms

Sync

Single-processorPriority scheduling(fixed priority or EDF)

scheduler

T1 T2 T3

tasks

• Why multitasking and not single “real-compute-write” loop?

• For multi-rate models:– Multitask implementation

schedulable, but single-task not schedulable

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Implementation on centralized, multitasking platforms

Sync

Single-processorPriority scheduling(fixed priority or EDF)

scheduler

T1 T2 T3

tasks

Goal:semantical preservation

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Implementation on centralized, multitasking platforms

Sync

Single-processorPriority scheduling(fixed priority or EDF)

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“Naïve” implementations don’t work

Q PrioQ > PrioA > PrioB

A

Q

BA

AQ

A B

A B

ERROR

The Dynamic Buffering Protocol

scheduler

T1 T2 T3

tasks

- non-blocking (wait-free)- memory-optimal- semantics-preserving

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Conclusions

• Concurrency => non-determinism• Synchronous models are deterministic– easier to understand and verify

• Synchronous models can be implemented on a variety of asynchronous execution platforms, using non-trivial techniques:– Implementations are correct-by-construction– They are memory-optimal– Performance (throughput, latency, …) can be analyzed

and optimized

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Open questions• For which applications is the synchronous

programming model suitable?– Traditionally for control: avionics, automotive, …– Some recent works trying to apply it to multimedia/signal

processing

• To what extent these methods apply to multicores?

• Are dataflow computers going to come back?