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A New Scalable and Cost-Effective Congestion Management Strategy for Lossless Multistage
Interconnection Networks
J. Duato1, I. Johnson2, J. Flich1, F. Naven2, P.J. García3, T. Nachiondo1
1Technical University of Valencia
Valencia, Spain
2Xyratex
Havant, UK
3University of Castilla-La Mancha
Albacete, Spain
The Eleventh International Symposium on High-Performance Computer Architecture, San Francisco, 2005
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Outline
• Introduction
• Congestion and HOL blocking
• Why now?
• Why previous proposals are inadequate
• Proposal: RECN
• Performance evaluation
• Conclusions
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Interconnection Networks
MPPs• Earth Simulator (640 vectorial CPUs)• ASCI Q (12,288 EV68 CPUs, Quadrics network)• BlueGene/L (65.535 nodes, each one 2 processors, 360 TFlops)
PC Clusters• Storage Area Network (SANs)
– Google (6.000 CPUs and 12.000 disks)
• Thunder (1.024 nodes each one 4 Itaniums/8GB)• Many data centers all around the world
ASCI QEarth Simulator Thunder
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Network Throughput beyond Saturation
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Congestion and HOL Blocking
Networkcontention
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Congestion and HOL Blocking
Persistentnetworkcontention
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Congestion and HOL Blocking
Persistentnetworkcontention
Flow control
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Congestion and HOL Blocking
Persistentnetworkcontention
Congestionpropagates
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Congestion and HOL Blocking
• Congestion introduces HOL blocking, and this may degrade network performance dramatically
33%
33%
HOL 33%
33%100%
33%
33%
33%
100%
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Traditional Solution
Overdimensioning the network
Late
ncy
Injected traffic
CongestionzoneWorking
zone
Network bandwidth is much higher than the bandwidth requested by end nodes
Low link utilization
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Why Congestion Management Now?
New problems arising:
System cost: Recent interconnects (Myrinet, InfiniBand, ASI) are
expensive compared to processors Power consumption: As network size increases, higher power
consumption, higher heat dissipation
Frequency/voltage scaling techniques: Not very efficient, and do not solve the system cost problem
Possible Solutions:
Reducing the number of network components: Possible by using a suitable topology, but link utilization increases
Systems will work closer to network saturation zone, thus,
a congestion management technique will be mandatory
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Why Current Techniques Are not Suitable?
Proactive Congestion Management (congestion prevention)• Path setup before data transmission• Used in ATM, computer networks (QoS)• High overhead, high latencies (not suitable for HPC)
The real problem is not the congestion, but its negative effects (HOL blocking)
Reactive Congestion Management (congestion recovery)• Injection limitation techniques using closed-loop feedback• Do not scale with network size and link bandwidth
– Notification delay (proportional to distance)
– Link capacity (proportional to clock frequency)
– May produce network instabilities
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Why Current Techniques Are not Suitable?
HOL blocking elimination/reduction• DAMQs and Virtual Channels
– not efficient for multihop networks
• VOQ (Virtual Output Queueing)– VOQ at switch level scales but does not eliminate HOL– VOQ at network level: A separate queue at every input port for every destination– Number of required resources scales at least quadratically with network
size !!!
• Credit Flow Controlled ATM– References congestion to network output only
– Consumes large number of buffers: A separate queue at every output port for every destination
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Proposal
Initial idea:• Exploit spatial and temporal locality in packet destinations• Manage the set of queues as a cache
– No equivalent to main memory!!! (where to replace?)– Not enough locality!!! (reduction in queue silicon area by a factor of 4)
Observation:• Non-congested flows do not introduce significant HOL blocking
RECN: Regional Explicit Congestion Notification• Non-congested flows are mapped to the same queue• Effective reduction in number of queues and no replacement needed• Congested flows are detected and mapped to set aside queues (SAQs)
RECN is a scalable congestion management technique because:• It reacts locally (and thus, it is not affected by propagation delays)• A very small number of queues (SAQs) for a wide range of network sizes
RECN enables:• Effective reduction of network cost by working closer to the saturation point• More efficient use of voltage/frequency scaling techniques
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RECN
Based on the PCI Express Advanced Switching Interconnect (ASI) specification
• Routing (turnpools)
Relevant switch architectural features• Congestion detection• Congestion notification and queue allocation• Queue deallocation• Packet processing• Flow control
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Turnpools
0
1
2
3
4
5
6
7
3
7
turn poolt. pointerD
Direction bit
Turn example
31 bits = 231 destinations
AS packet header
2 1
122 11
3
AA
33 22B
B
2
Allows to know if a packetwill pass through a givenport in the network
Mask bits required
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Switch Model
RAMin
XBARS=1.5
RAMin
RAMin
. . . . . .
RAMout
RAMout
RAMout
Arbiter
. . .
. . . . . .
Dynamic queuemanagement (VCs)
Dynamic queuemanagement (VCs)
LC
LC
LC
LC
LC
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RAM in and RAM out
RAMSAQ 0SAQ 1
SAQ 2
SAQ 3
Cold Queue
SAQ 0SAQ 1
SAQ 2
SAQ 3
Tokens (one per each input port)
Root
Only at egress:Avoids successive
internal notifications
vv
vv
vv
vv
turn poolturn pool
turn poolturn pool
turn poolturn pool
turn poolturn pool
mask bitsmask bits
mask bitsmask bits
mask bitsmask bits
mask bitsmask bits
CAM
SAQ 0SAQ 1
SAQ 2
SAQ 3bb
bb
bb
bb
Valid bit Congested pointblocked
nextSAQnextSAQ
nextSAQnextSAQ
nextSAQnextSAQ
nextSAQnextSAQ
Xon/XoffFlow control
XoffXoff
XoffXoff
XoffXoff
XoffXoff
lvlv
lvlv
lvlv
lvlv
leave bit (only at ingress)
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A congestion point forms
How it Works
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How it Works
Cold queue fills over a threshold
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How it Works
Internal notification to each input port
sending packets to the output port
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How it Works
Input ports allocate a new SAQ for
packets addressed tothe congested output port
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How it Works
Notification sent whenthe SAQ fills
over a threshold
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How it Works
A new SAQ allocatedfor the congested port
at each output port
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How it Works
Internal notification when the SAQ fills over
A threshold
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How it Works
The input port allocatesA new SAQ
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How it Works
At the end, the congestion tree builds and is mapped
entirely onto SAQs
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Performance Evaluation
•Evaluation based on simulation results
•Two evaluation studies:• Network performance when using:
– RECN– VOQ at network level (VOQnet)– VOQ at switch level (VOQsw)– 4 queues at ingress and egress ports (4Q)– 1 queue at ingress and egress ports (1Q)
• RECN scalability
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Simulation Model
• Network configurations evaluated:• 64 hosts connected by a 64x64 BMIN• 256 hosts connected by a 256x256 BMIN• 512 hosts connected by a 512x512 BMIN
• Simulation assumptions:• BMINs based on shuffle-exchange connection scheme• Deterministic routing• 128 KB memories at ingress/egress ports• Multiplexed crossbar (BW=12 Gbps)• Serial full-duplex pipelined links (BW=8 Gbps)• 64 and 512-byte packets• Credit-based and Xon-Xoff (for SAQs) flow control• Maximum of 8 SAQs at ingress/egress ports (RECN)
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Traffic Load
•Synthetic Traffic:
•Traces:• From I/O activity at cello system disk interface• Different compression factors applied
# Srcs Dst.Injection Rate (%)
Traffic Start Time
Traffic End Time
Corner Case 1
75% Random 50% 0 Sim. End
25% Hot-Spot 100% 800 μs 970 μs
Corner Case 2
75% Random 100% 0 Sim. End
25% Hot-Spot 100% 800 μs 970 μs
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Performance Comparison
•Network throughput - Corner case 2, 64x64 BMIN
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Performance Comparison
• Network throughput – Traces, 64x64 BMIN
Compression Factor set to 20 Compression Factor set to 40
Tit
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Man
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Scalability Analysis
• SAQ utilization – Corner Case 1, 64x64 BMINMaximum # SAQs used (ingress) Maximum # SAQs used (egress)
Total # of active SAQS
Tit
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Man
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Scalability Analysis
• SAQ utilization – Corner Case 2, 64x64 BMINMaximum # SAQs used (ingress) Maximum # SAQs used (egress)
Total # of active SAQS
Tit
le:A
New
Sca
lab
le a
nd
Cost
-Eff
ect
ive C
on
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Man
ag
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Loss
less
Mu
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In
t.
Netw
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In
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ati
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Hog
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47
Scalability Analysis
• SAQ utilization – Traces, Comp. Factor 20, 64x64 BMINMaximum # SAQs used (ingress) Maximum # SAQs used (egress)
Total # of active SAQS
Tit
le:A
New
Sca
lab
le a
nd
Cost
-Eff
ect
ive C
on
gest
ion
Man
ag
em
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t S
trate
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Loss
less
Mu
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In
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Netw
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sC
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: Th
e 1
1th
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Hog
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48
Scalability Analysis
• SAQ utilization – Traces, Comp. Factor 40, 64x64 BMINMaximum # SAQs used (ingress) Maximum # SAQs used (egress)
Total # of active SAQS
Tit
le:A
New
Sca
lab
le a
nd
Cost
-Eff
ect
ive C
on
gest
ion
Man
ag
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Loss
less
Mu
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In
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Netw
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sC
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Hog
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49
Scalability Analysis
• Network throughput – Corner Case 2, 256x256 BMIN
Maximum # SAQs used (egress)Maximum # SAQs used (ingress)
Tit
le:A
New
Sca
lab
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Cost
-Eff
ect
ive C
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Man
ag
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Loss
less
Mu
ltis
tate
In
t.
Netw
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sC
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: Th
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Hog
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Scalability Analysis
• Network throughput – Corner Case 2, 512x512 BMIN
Maximum # SAQs used (ingress) Maximum # SAQs used (egress)
Tit
le:A
New
Sca
lab
le a
nd
Cost
-Eff
ect
ive C
on
gest
ion
Man
ag
em
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t S
trate
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or
Loss
less
Mu
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tate
In
t.
Netw
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sC
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fere
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: Th
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Final Remarks
• We also designed a protocol to deallocate SAQs when they are no longer needed
• Many optimizations– CAM IDs to reduce control message size– CAM search done in parallel with packet reception– Merging of congestion trees
• Silicon area reduced with respect to switch-level VOQs
Tit
le:A
New
Sca
lab
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nd
Cost
-Eff
ect
ive C
on
gest
ion
Man
ag
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t S
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Loss
less
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In
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Netw
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Conclusions
• We have proposed a scalable congestion management strategy for lossless networks
• We have shown that it only requires a small number of buffers for a wide range of network sizes
• We have modeled an existing ASI switch design, verifying:–Maintains network performance close to
ideal (but non-scalable) solution–Silicon area requirements are now smaller
than for the original design
