predictable programming on a precision timed architecture

Predictable Programming on a Precision Timed Architecture

Ben Lickly - UC Berkeley Isaac Liu - UC Berkeley

Sungjun Kim - Columbia UniversityHiren D. Patel – UC Berkeley

Stephen A. Edwards - Columbia UniversityEdward A. Lee - UC Berkeley

Patel, UC Berkeley, PRET 2

Edwards and Lee - Case for PRET

• 2007 – Edwards and Lee made a case for precision timed computers (PRET machines)– Predictability– Repeatability

S. A. Edwards and E. A. Lee, The case for the precision timed (PRET) machine. In Proceedings of the 44th Annual Conference on Design Automation (San Diego, California, June 04 - 08, 2007). DAC '07. ACM, New York, NY, 264-265.

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Edwards and Lee - Case for PRET

• Unpredictability– Difficulty in determining timing behavior

through analysis

• Non-repeatability– Different executions may yield different

timing behavior

• Brittleness– Small changes have big effects on timing

behavior

3


Brittleness

• Expensive affair

• Tight coupling of software and hardware

• Reliance on testing for validation

• Upgrading difficult

• Solution: stockpile

4

Source: www.skycontrol.net

http://www.skycontrol.net/


But wait …

• Real-time scheduling– Worst-case execution

time• Detailed model of

hardware• Large engineering

effort• Valid for particular

hardware models

– Interrupts, inter-process communication, locks …

• Bench testing

– Brittle

5

Sebastian Altmeyer, Christian Hümbert, Björn Lisper, and Reinhard Wilhelm. Parametric Timing Analysis for Complex Architectures. In Proceedings of the 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'08), pages 367-376, Kaohsiung, Taiwan, August 2008. IEEE Computer Society.


Precise Timing and High Performance

6

Traditional Alternative

Caches Scratchpads

Deep pipelines Thread-interleaved pipelines

Function-only ISAs ISAs with timing instructions

Function-only languages Languages and programming models with timing

Best-effort communication Fixed-latency communication

Time-sharing Multiple independent processors


Outline

• Introduction• Related Work• PRET Machine• Programming Example• Future Work• Conclusion

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Related Work

• Java Optimized Processor– Schoeberl et al. [2003]

• Timing instructions – Ip and Edwards [2006]

• Reactive processors– Von Hanxleden et al. [2005]– Salcic et al. [2005]

• Virtual Simple Architecture– Mueller et al. [2003]

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Semantics of Timing Instructions

• Ip and Edwards [2007]

• Deadline instructions– Denote the required

execution time of a block

• When decoded– Stall instruction until

timer value is 0– Then set timer value

to new value

deadi $t0, 10

…

deadi $t0, 8

…

deadi $t0, 0

…

L0:

deadi $t0, 10

…

b L0

…

Straight Line Block 0

Straight Line Block 1

Loop Block


Tracing A Program Fragment

A: deadi $t0, 6B: sethi %hi(0x3f800000),

%g1C: or %g1, 0x200, %g1 D: st %g1, [ %fp + -12 ]E: deadi $t0, 8F: …

cycle

065432108

$t0


Precision Timed Architecture

Thread-interleaved pipeline

Scratchpad memories

Time-triggered main memoryaccess

Round-robin thread scheduling


Clocks and Memory Hierarchy

• Clocks– Main clock– Derived clocks

• Instruction and data scratchpad memories – 1 cycle access latency

• Main memory – 16MB size– Latency of 50ns– Frequency:250Mhz

• ~13 cycles latency12

CoreCore MainMem.MainMem.

SPMSPMSPMSPMSPMSPMSPMSPMSPMSPMSPMSPM

DMADMA


Thread-interleaved Pipeline

• Thread stalls – Main memory access– Deadline instructions

• Replay mechanism– Execute same PC next

iteration

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FetchFetch

DecodeDecode

Reg. AccessReg. Access

ExecuteExecute

MemoryMemory

WriteBackWriteBack

F/D

D/R

R/E

E/M

M/W

Decrement DeadlineTimers

Stall ifDeadlineInstruction

Increment PC

Check main memory access


Time-Triggered Access through Memory Wheel

• Decouple thread’s access pattern

• Time-triggered access

• Each thread must make and complete access within its window

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thread0 thread1 thread2 thread3 thread4 thread5 thread0

90 cycles until thread0 completes

On time On time On time On time On time


Tool Flow

• GCC 3.4.4, SystemC 2.2, Python 2.4

Boot code Motorola SREC files

C programstiming instructions

GCC to compile boot codeand program code


Simple Mutual Exclusion Example

• Producer followed by Consumer and Observer– Consumer and Observer execute together

• Loop rate of two rotations of memory wheel– 1st for Producer to write– 2nd Consumer and Observer to read

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Write to shared dataRead from shared data

Write to output


Video Game Example

Graphic Thread

Graphic Thread

VGA-Driver Thread

VGA-Driver Thread

Even BufferEven Buffer

Odd BufferOdd

Buffer

Main-Control Thread

Main-Control Thread

Odd Queue

Even Queue

Command

Command

Pixel Data

Pixel Data

Swap (When Sync Requested and When Odd Queue Empty)

Sync (After queue swapped)

Update Screen (Sync request)

Sync (After buffer swapped)

Refresh (Sync request)

Swap (When sync requested and when Vertical blank)


Timing Requirements

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Signal Timing Requirement

Pixel Cycles

V. Sync 64µs 1611

V. Back-porch 1.02ms 25679

Draw 480 lines 15.25ms

V. Front-porch 350µs 8811

H. Sync 3.77µs 96

H. Back-porch 1.89µs 48

Draw 640 pixels 25.42µs

H. Front-porch 0.64µs 16


Timing Implementation

• Pixel-clock using derived clock– 25.175Mhz

• Drawing 16 pixels

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Future Work

• Architecture– DMA– DDR2 main memory model– Thread synchronization primitives– Shared data between threads

• Real-time Benchmarks– With timing requirements

• Programming models– Memory allocation schemes– Synchronizations


Conclusion

• What we want …– Time as a first class citizen of embedded

computing– Predictability– Repeatability

• Where we are at …– PRET cycle-accurate simulator– Release …

• http://chess.eecs.berkeley.edu/pret/

http://chess.eecs.berkeley.edu/pret/


Extras


More on Brittleness

• Small changes may have big effects on timing behavior

Theorem (Richard’s anomalies):If a task set with fixed priorities, execution times, and

precedence constraints is optimally scheduled on a fixed number of processors, then increasing the number of processors, reducing execution times, or weakening precedence constraints can increase the schedule length.

Richard L. Graham, “Bounds on the performance of scheduling algorithms”, in E. G. Coffman, Jr.(ed.), Computer and Job-Shop Scheduling Theory, John Wiley, New York, 1975.


Richard’s Anomalies

1

9

2

5

3

6

4

7

T1/3 T2/2 T3/2 T4/2

T9/9 T5/4 T6/4 T7/4

8

T8/4

0 3 12

• 9 tasks, 3 processors, priority list, precedence order, execution times.


• eTime’ = eTime - 1

Richard’s Anomalies: Reducing Execution Times

1

9

2

5

3

6

4

7

T1/2 T2/1 T3/1 T4/1

T9/8 T5/3 T6/3 T7/3

8

T8/3

0 3 12


Richard’s Anomalies: More Processors

1

9

2

5

3

6

4

7

T1/3 T2/2 T3/2 T4/2

T9/9 T5/4 T6/4 T7/4

8

T8/4

0 3 12

• 4 processors

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Richard’s Anomalies: Changing Priority List

1

7

2

4

6

3

3

8

T1/3 T2/2 T3/2 T4/2

T9/9 T5/4 T6/4 T7/4

9

T8/4

0 3 12

• L = (T1,T2,T4,T5,T6,T3,T9,T7,T8)


Brittleness Again…

• In general, all task scheduling strategies are brittle

predictable programming on a precision timed architecture

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