Consolidation of Real-Time Systems into a Multi-Core...
Transcript of Consolidation of Real-Time Systems into a Multi-Core...
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CMAS CPSWeek’15
Consolidation of Real-Time Systems
into a Multi-Core Platform
[email protected] [email protected]
Raj Rajkumar Hyoseung Kim
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CMAS CPSWeek’15
Consolidation onto Multicores
• Benefits
– Reduces the number of CPUs and wiring harness among them
– Leads to a significant reduction in cost and space requirements
Vendor #1 Vendor #2
Vendor #3 Vendor #4
Real-time System
Consolidation
Multi-core SBC
Single-core SBCs
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CMAS CPSWeek’15
Key Challenges
Timing predictability without losing efficiency
Temporal isolation among consolidated workloads
(Quantification & minimization of temporal interference)
Use of standardized COTS multi-core platforms
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CMAS CPSWeek’15
Multi-Core Memory Hierarchy
Core 1 Core 2 Core 3 Core P
L1/L2
Cache
L1/L2
Cache
L1/L2
Cache
L1/L2
Cache
…
Last-level Shared Cache
Memory Controller
DRAM
Bank 1
DRAM
Bank 2
DRAM
Bank 3
DRAM
Bank B
…
Cache
interference
Memory bus
interference
DRAM bank
interference
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CMAS CPSWeek’15
Last-Level Shared Cache
• A large cache shared among processing cores
– Reduces memory access time (7 – 10x faster than DRAM access)
– Mitigates DRAM bandwidth consumption
– Allows high cache utilization
Intel Core i7
8-15 MB L3 Cache
Freescale P4080
2MB L3 Cache
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CMAS CPSWeek’15
Uncontrolled Use of Shared Cache
1. Inter-core Interference
P1 P2 P3
L1
L2
L1
L2
L1
L2
L3
P4
L1
L2
Core
Private
Cache
Shared
Cache
2. Intra-core Interference
P1 P2 P3
L1
L2
L1
L2
L1
L2
L3
P4
L1
L2
Core
Private
Cache
Shared
Cache
43% Slowdown 27% Slowdown (C:2.5ms, T:10ms)
* PARSEC Benchmark on Intel i7 6
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CMAS CPSWeek’15
Coordinated Cache Management
• Challenges
– Uncontrolled shared cache: Cache interference penalties
– Cache partitioning:
• Limited number of cache partitions
• Cache under-utilization
• Key idea: Controlled sharing of partitioned caches
while ensuring timing predictability
1. Provides predictability on multi-core real-time systems
2. Addresses the limitations of cache partitioning
[ECRTS14] Hyoseung Kim, Arvind Kandhalu, and Raj Rajkumar. A Coordinated Approach for Practical OS-Level Cache Management in Multi-Core Real-Time Systems. In ECRTS, 2013.
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CMAS CPSWeek’15
Coordinated Cache Management
Tasks
+
+
+
Bounded Penalties
Memory partitions
Cache partitions
1
2
3
NP -1
NP
1. Per-core Cache
Reservation
2. Reserved Cache Sharing
3. Cache-Aware
Task Allocation
Task Parameters
Coordinated Cache
Management
τi :(Ci
p, Ti, Di, Mi )
Core
1
Core
2
Core
NC
…
Pa
rtit
ion
ed
Fix
ed
-pri
ori
ty S
ch
ed
uli
ng
…
…
Pa
ge
Co
lori
ng
(C
ac
he
Pa
rtit
ion
ing
)
Per-core cache reservation
Prevent Inter-core cache interference Reserved cache sharing: Mitigate the problems with page coloring
Considerations 1. Analyzing intra-core interference
2. Guaranteeing memory requirements
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CMAS CPSWeek’15
Coordinated Cache Management
Tasks
+
+
+
Bounded Penalties
Memory partitions
Cache partitions
1
2
3
NP -1
NP
1. Per-core Cache
Reservation
2. Reserved Cache Sharing
3. Cache-Aware
Task Allocation
Coordinated Cache
Management Core
1
Core
2
Core
NC
…
Pa
rtit
ion
ed
Fix
ed
-pri
ori
ty S
ch
ed
uli
ng
…
…
Pa
ge
Co
lori
ng
(C
ac
he
Pa
rtit
ion
ing
)
Task Parameters
τi :(Ci
p, Ti, Di, Mi )
Cache-Aware Task Allocation
• Algorithm to allocate tasks and cache partitions to cores
• Exploits the benefits of cache sharing by load concentration
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CMAS CPSWeek’15
Multi-Core Memory Hierarchy
Core 1 Core 2 Core 3 Core P
L1/L2
Cache
L1/L2
Cache
L1/L2
Cache
L1/L2
Cache
…
Last-level Shared Cache
Memory Controller
DRAM
Bank 1
DRAM
Bank 2
DRAM
Bank 3
DRAM
Bank B
…
Cache
interference
Memory bus
interference
DRAM bank
interference
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CMAS CPSWeek’15
DRAM Organization
DRAM Rank CHIP
1
CHIP
2
CHIP
3
CHIP
4
CHIP
5
CHIP
6
CHIP
7
CHIP
8
Data bus 8-bit
Address bus
Command bus
64-bit
DRAM Chip
Bank 1
Com
ma
nd d
eco
de
r
Data bus
Address bus
Command bus
8-bit
Bank ... Bank 8
Columns
Row
s
Row buffer
Row
de
co
der
Column decoder
Ro
w a
dd
ress
Column address
DRAM access latency depends on which row is currently in the row buffer
Row hit
Row conflict
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CMAS CPSWeek’15
Memory Controller
Request buffer Bank 1 Priority Queue
Bank 2 Priority Queue
Bank B Priority Queue
...
Bank 1 Scheduler
Bank 2 Scheduler
Bank B Scheduler
...
Channel Scheduler
Memory scheduler
Read/
Write
Buffers
DRAM address/command buses
Processor data bus
DRAM data bus
Memory requests from CPU cores
Two-level hierarchical
scheduling structure
(FR-FCFS)
Inter-bank interference at channel scheduler
Intra-bank interference at bank scheduler Memory interference
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CMAS CPSWeek’15
DRAM Bank Partitioning & Sharing
• DRAM bank partitioning: software method to assign dedicated DRAM
banks to each core (or task)
• Problems
– Limited number of DRAM bank partitions
– Reduced usable memory size
Bank 1 Scheduler
Bank 2 Scheduler
Channel Scheduler
Core 1 Core 2
(1) w/o bank partitioning
Bank 1 Scheduler
Bank 2 Scheduler
Channel Scheduler
Core 1 Core 2
(2) w/ bank partitioning
Intra-bank and inter-bank
interference Inter-bank interference only
DRAM bank sharing
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CMAS CPSWeek’15
Bounding Memory Interference Delay
• Provides an upper bound on memory interference delay
– Intra-bank and inter-bank interference delays
– Private and shared DRAM banks
Response-Time Based
Schedulability Analysis
[RTAS14] Hyoseung Kim et al. Bounding Memory Interference Delay in COTS-based Multi-Core Systems. In IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2014. Best Paper Award
Request-driven bounding
Task’s own memory requests
Job-driven bounding
Interfering memory requests
during the task execution
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CMAS CPSWeek’15
Multi-Core Memory Hierarchy
Core 1 Core 2 Core 3 Core P
L1/L2
Cache
L1/L2
Cache
L1/L2
Cache
L1/L2
Cache
…
Last-level Shared Cache
Memory Controller
DRAM
Bank 1
DRAM
Bank 2
DRAM
Bank 3
DRAM
Bank B …
Cache
interference
Memory bus
interference
DRAM bank
interference
Operating System
Task τ1
Task τ2
Task τ3
Task τ4
Task τ5
Task τ6
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CMAS CPSWeek’15
Linux/RK
• CMU’s open source real-time extensions to the Linux kernel
• Resource kernel (RK) approach
• Resource reservation: tasks can reserve a portion of system resources
– Ex) CPU reserve: (budget, replenishment period, CPU affinity, policy)
• Guarantees resource allocations at admission time
• Enforces resource usage
Resource
Set
CPU
Reserve Task
1
Network
Reserve
Memory
Reserve
Task
2
Task
3
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CMAS CPSWeek’15
Multi-Core Memory Management
• Linux/RK memory manager
– Supports software cache and DRAM partitioning
– Allows tasks to specify their memory size, cache, DRAM bank demands
Task i: memory reservation - Memory size = m pages - Cache partitions = h - DRAM bank partitions = b
Real-time task
c Task i: allocated pages
Linux/RK Memory Manager
Cache 1 Cache 2 Cache 3 Cache H ...
Bank 1
Bank 2
Bank B
...
...
...
Bank 1
Bank 2
Bank B
...
...
...
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Unallocated physical pages
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CMAS CPSWeek’15
Cache Interference
• Task execution time and L3 misses
– Intel i7 2600 3.4GHz quad-core, 8MB L3 cache, DDR3-1333
Task Instance
10
11
12
13
14
0 10 20 30 40 50 60 70 80 90 100
Exe
cuti
on
Tim
e
(mse
c)
w/o Cache Reservation
w/ Cache Reservation (p=12)
0
3
6
9
12
0 10 20 30 40 50 60 70 80 90 100
L3 m
isse
s p
er
mse
c (x
10
00
)
w/o Cache Reservation
w/ Cache Reservation (p=12)
Solo WCET
Solo WC L3 misses
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CMAS CPSWeek’15
Memory Interference
• Shared DRAM Bank
0
200
400
600
800
1000
1200
1400
No
rm. R
esp
on
se T
ime
(%
)
black- scholes
body- track
canneal ferret fluid- animate
freq- mine
ray- trace
stream- cluster
swap- tions
vips x264
12x increase due to
memory interference
Observed
Predicted by analysis
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CMAS CPSWeek’15
Memory Interference
• Private DRAM Bank
black- scholes
body- track
canneal ferret fluid- animate
freq- mine
ray- trace
stream- cluster
swap- tions
vips x264 0
100
200
300
400
500
No
rm. R
esp
on
se T
ime
(%
)
Observed
Predicted by analysis
4.1x increase DRAM bank partitioning
helps reducing the memory interference
Our analysis enables the quantification of the benefit of DRAM bank partitioning
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CMAS CPSWeek’15
Memory Interference
• Private DRAM Bank under moderate memory contention
0
20
40
60
80
100
120
140
160
180
No
rm. R
esp
on
se T
ime
(%
) Average over-estimates are 8%
(13% for a shared bank) Observed
Predicted by analysis
black- scholes
body- track
canneal ferret fluid- animate
freq- mine
ray- trace
stream- cluster
swap- tions
vips x264
Our analysis bounds memory interference delay with low pessimism
under both severe and moderate memory contentions
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CMAS CPSWeek’15
Multi-Core Virtualization
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CMAS CPSWeek’15
Real-Time System Virtualization
• Barrier to consolidation
– Each app. could have been developed
independently by different vendors
• Bare-metal / Proprietary OS
• Linux
– Legacy software
– Different license issues
• Consolidation via virtualization
– Each application can maintain
its own implementation
– Minimizes re-certification process
– IP protection, license segregation
– Fault isolation
Virtualization
Multi-core CPU
Real-Time Hypervisor
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CMAS CPSWeek’15
VM Scheduling Structure
• Two-level hierarchical scheduling structure
– Task scheduling and VCPU scheduling
VM1
VCPU1
Task τ1
Task Scheduler
Task τ2
VCPU2
Task τ3
Task Scheduler
Task τ4
Hypervisor
Physical Core 1 (PCPU1)
VCPU Scheduler
VM2
VCPU3
Task τ5
Task Scheduler
Task τ6
VCPU4
Task τ7
Task Scheduler
Task τ8
Physical Core 2 (PCPU2)
VCPU Scheduler
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CMAS CPSWeek’15
Virt/RK
• Real-time virtualization with resource kernel approach
– CPU reservation for VCPUs
– Memory reservation for VMs
– Current implementation: Virt/RK::KVM-x86
– Under development: Virt/RK::KVM-ARM, Virt/RK::L4
VM1
VCPU1
Task τ1
Task Scheduler
Task τ2
VCPU2
Task τ3
Task Scheduler
Task τ4
VM Resource Reservation
• VCPU1: 30% of physical CPU
• VCPU2: 30% of physical CPU
• VM1: 20% of host memory w/
cache & DRAM bank
partitioning
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CMAS CPSWeek’15
Resource Sharing in Virtualization
• Consolidation inevitably causes the sharing of resources
– Sensors
– Network interfaces
– I/O devices
– Shared data
• Long blocking time in a virtualized environment
– VCPU level preemptions
– VCPU budget depletion
Requires mutually-exclusive locks
to avoid race conditions
Need a synchronization mechanism with bounded blocking times
for multi-core real-time virtualization
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CMAS CPSWeek’15
vMPCP
• Virtualization-aware Multiprocessor Priority Ceiling Protocol
– Provides bounded blocking time on accessing shared resources
in multi-core virtualization
• Hierarchical priority ceilings
• Two-level priority queue for a mutex waiting list
– Optional VCPU budget overrun to reduce blocking times
– VCPU budget replenishment policies
• Periodic server
• Deferrable server
– Implemented on Virt/RK::KVM-x86
[RTSS14] Hyoseung Kim, Shige Wang, and Raj Rajkumar. vMPCP: A Synchronization Framework for Multi-Core Virtual Machines. In IEEE Real-Time Systems Symposium (RTSS), 2014.
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CMAS CPSWeek’15
Case Study: Effect of vMPCP
Baseline
Virtualization-unaware
synchronization protocol
(MPCP)
vMPCP
Virtualization-aware
synchronization protocol
τ1
τ2
τ3
τ4
τ5
τ6
τ7
τ8 (μsec)
τ1
τ2
τ3
τ4
τ5
τ6
τ7
τ8 (μsec)
vMPCP yields 29% shorter task response time with shared resources
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CMAS CPSWeek’15
Summary
• Workload Consolidation into a Multi-core Platform
– Predictability without losing efficiency
– Quantification and minimization of temporal interference
• Multi-core memory hierarchy
– Coordinated cache management
• Cache partitioning, reserve, and sharing
– Bounding memory interference
• DRAM partitioning and sharing Quantifies the benefit of partitioning
• Memory-bus and DRAM bank interference analysis
• Real-time virtualization
– Virt/RK: resource reservation approach
– vMPCP: resource sharing for real-time virtualization
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CMAS CPSWeek’15
What next?
• Modeling state-of-the-art computer architecture techniques
– Hardware prefetchers
– Future memory controllers
– Future DRAM designs
• Extending existing real-time systems work to virtualization
– Diverse locking protocols (e.g., RW-lock)
– Fault tolerance mechanisms
– Hardware accelerations (e.g., GPGPU)
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