sacr: scheduling-aware cache reconfiguration for real-time embedded systems
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SACR: Scheduling-Aware Cache Reconfiguration for Real-Time Embedded Systems. Weixun Wang and Prabhat Mishra Embedded Systems Lab Computer and Information Science and Engineering University of Florida Ann Gordon-Ross Electrical and Computer Engineering University of Florida. Outline. - PowerPoint PPT PresentationTRANSCRIPT
SACR: Scheduling-Aware Cache Reconfiguration for Real-Time
Embedded Systems
Weixun Wang and Prabhat MishraEmbedded Systems Lab
Computer and Information Science and Engineering
University of Florida
Ann Gordon-RossElectrical and Computer Engineering
University of Florida
Outline
Introduction
Related Work
Scheduling-Aware Cache Reconfigurations
Phase-based Optimal Cache Selection
Scheduling-Aware Dynamic Reconfigurations
Experiments
Conclusion
IntroductionReal-time embedded systems
Energy constraints (battery operated) Time constrained
Hard task deadlines– Safety-critical systems
Soft task deadlines– Gaming, multimedia, housekeeping devices have soft deadlines
– Deadline miss results in temporary service/quality degradation
Dynamic cache reconfigurations Promising for improving energy and performance Not applicable in real-time systems
Dynamic computation is expensive Dynamic reconfiguration leads to timing uncertainty 3
Propose a scheduling-aware dynamic cache reconfiguration technique to generate significant energy savings in soft real-time systems by exploiting static analysis during runtime.
Outline
Introduction
Related Work
Scheduling-Aware Cache Reconfigurations
Phase-based Optimal Cache Selection
Scheduling-Aware Dynamic Reconfigurations
Experiments
Conclusion
Related Work Energy-aware task scheduling techniques
Early Deadline First (EDF) and Rate Monotonic (RM)Dynamic Voltage Scaling (DVS)
Jejurikar et al. [IEEE TCAD 06’], Quan et al. [ACM TECS 07’]
Caches in Real-Time SystemsCache locking and Cache Partitioning
Puant [RTSS 02’] and Wolfe [IWRCS 1993]Cache-related preemption delay analysis
Tan et al. [ACM TECS 2007]
Reconfigurable Cache Architectures Reconfigurable cache architecture
Zhang et al. [ACM TECS 05’] Application-based vs. Phase-based tuning
Gordon-Ross et al. [ISLPED 05] vs. Sherwood et al. [Micro 03] 5
Outline
Introduction
Related Work
Scheduling-Aware Cache Reconfigurations
Phase-based Optimal Cache Selection
Scheduling-Aware Dynamic Reconfigurations
Experiments
Conclusion
Overview
7
Task 1 Task 2
In traditional real-time systems
In our approach
Phase-based Optimal Cache Selection
8
Task Execution Time
C1
C2
C3
Cn
phase 1 (0/n)
phase 2 (1/n)
phase 3 (2/n)
phase n (n-1/n)
P1 P20 Pn-1
A task is divided by n potential preemption points
A phase is the period of time between a predefined potential preemption point and task completion
Each phase has its optimal cache configuration Performance-optimal and energy-optimal
A static profile table is generated for each task
Phase-based Optimal Cache Selection
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Potential preemption points may not be the same as actual preemption points. They are used for cache configuration selection.
Partition factor determines the potential preemption points and resulting phases Large partition factor leads to large look-up table
Not feasible due to area constraints
Large partition factor may not save more energy
Partition factor around 4 to 7 is profitable
Outline Introduction
Related Work
Scheduling-Aware Cache Reconfigurations Phase-based Optimal Cache Selection Scheduling-Aware Dynamic Reconfigurations
Statically Scheduled Systems Dynamically Scheduled Systems
Experiments
Conclusion
Scheduling-Aware Cache Reconfiguration
Statically scheduled systemsArrival times, execution times, and deadlines are
known a priori for each task
Statically profile energy-optimal configurations for every execution period of each task without violating any task deadlines
Dynamically scheduled systemsTask preemption points are unknown
New tasks can enter the system at any time Conservative approach Aggressive approach
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Conservative Approach
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Energy-optimal cache configuration with equal or higher performance than base cache
Nearest-neighborUse the nearest partition point to decide which cache
configuration to tune to
Static Profile tableDeadline-aware energy-
optimal configurations
Task list entryRuntime information
Aggressive Approach
13
Static Profile tableEnergy-opt configurationPerf.-optimal configuration
Task list entryRuntime information
Energy-optimal cache configuration & Performance-optimal cache configuration Includes their execution time as well.
Ready task list (RTL) Contains all the tasks currently in the system
Outline
Introduction
Related Work
Scheduling-Aware Cache Reconfigurations
Phase-based Optimal Cache Selection
Scheduling-Aware Dynamic Reconfigurations
Experiments
Conclusion
Experimental Setup
15
SimpleScalar to obtain simulation statisticsUsed external I/O (eio) trace file, checkpointing
and fastforwarding to generate static profile tableEnergy model
Zhang et al. and CACTI 4.2 Benchmarks
EEMBC MediaBench
Energy Savings (Instruction Cache)
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51% average energy saving using aggressive approach
28% average energy savings using conservative approach
Energy Savings (Data Cache)
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22% average energy saving using aggressive approach
17% average energy savings using conservative approach
Hardware Overhead
18
Profile Table stores 18 cache configurations Synthesized using Synopsys Design Compiler Assumed lookup frequency of one million nanoseconds
Table lookup every 500K cycles using 500 MHz CPU
Average energy penalty is 450 nJ Less than 0.02% of overall savings (2825563 nJ)
ConclusionDynamic cache reconfiguration is a promising
approach to improve both energy consumption and overall performance.
Developed a scheduling aware dynamic cache reconfiguration technique On average 50% reduction in overall cache
energy consumption in soft real-time systems
Future workHard real-time systemsMulti-core and multi-processor systems
19
Thank you !
Aggressive Approach
21
When task T is the only task in the system Always tune to energy-optimal cache if possible
When task T preempts another task Run schedulability check
Discard the lowest priority task if absolutely necessary
Tune to energy-optimal cache
if all other tasks in RTL can meet their deadlines using their performance-optimal caches
When task T is preempted by another task Calculate and store runtime information (RIN, CP)