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Deploying Accelerators At
Datacenter Scale Using Spark
Di Wu and Muhuan Huang
University of California, Los Angeles,
Falcon Computing Solutions, Inc.
UCLA Collaborators: Cody Hao Yu, Zhenman Fang,
Tyson Condie and Jason Cong
3x speedup in 3 hours
Accelerators in Datacenter
• CPU core scaling coming to an end
– Datacenters demand new technology to sustain scaling
• GPU is popular, FPGA is gaining popularity
– Intel prediction: 30% datacenter nodes with FPGA by 2020
About us
• UCLA Center for Domain-Specific Computing
– Expeditions in Computing program from NSF in 2009
– Public-private partnership between NSF and Intel in 2014
– http://cdsc.ucla.edu
• Falcon Computing Solutions, Inc.
– Founded in 2014
– Enable customized computing for big data applications
– http://www.falcon-computing.com
What is FPGA?
• Field Programmable Gate Array
(FPGA)
– Reconfigurable hardware
– Can be used to accelerate specific
computations
• FPGA benefits
– Low-power, energy efficient
– Customized high performance
PCI-E FPGA
- IBM CAPI
FPGA in CPU
socket
- Intel HARP
Problems of deploying accelerators
in datacenters efficiently …
1. Complicated Programming Model
• Too much hardware specific knowledge
• Lack of platform-portability
2. JVM-to-ACC data transfer overheads
• Data serialization/deserialization
• Additional data transfer
3. Accelerator management is non-trivial
Accelerator designer
System administrator
Big-data application developer
Does my
accelerator work in
your cluster …?
Which cluster
node has the
accelerator …?
How can I use your
accelerator …?
More Challenges for FPGAs
4. Reconfiguration time is long
• Takes about 0.5 - 2 seconds
– Transfer FPGA Binary
– Reset the bits
– …
• Naïve runtime FPGA sharing may slow down the
performance by 4x
What we did
• Provide a better programming model:
– APIs for accelerator developers
• Easier to integrate into big-data workload, e.g. Spark and Hadoop
– APIs for big-data application developers
• Requires no knowledge about accelerators
• Provide an accelerator management runtime
– Currently supports FPGAs and GPUs
è Blaze: a system providing Accelerator-as-a-Service
YARN Today
Client RM
AM
NM
NM
Container
Container
Node	status
Job	submission
Resource request
Application status
Blaze: Accelerator Runtime System
Client RM
AM
NM
NM
Container
Container
Accelerator	status
GAM
NAM
NAM
FPGA
GPU
GlobalAccelerator Manager
accelerator-centric scheduling
Node Accelerator Manager
Local accelerator service management, JVM-to-ACC communication optimization
GAM
NAM
Details on
Programming Interfaces and
Runtime Implementation
Interface for Spark
val points = sc.textfile().cache
for (i <- 1 to ITERATIONS) {
val gradient = points.map(p =>
(1 / (1 + exp(-p.y*(w dot p.x)))
- 1) * p.y * p.x
).reduce(_ + _)
w -= gradient
}
val points = blaze.wrap(sc.textfile().cache)
for (i <- 1 to ITERATIONS) {
val gradient = points.map(
new LogisticGrad(w)
).reduce(_ + _)
w -= gradient
}
class LogisticGrad(..)
extends Accelerator[T, U] {
val id: String = “Logistic”
}
RDD AccRDD
def compute():
serialize data
communicate with NAM
deserialize results
blaze.wrap()
Interface for Accelerators
class LogisticACC : public Task {
// extend the basic Task interface
LogisticACC(): Task() {;}
// overwrite the compute function
virtual void compute() {
// get input/output using provided APIs
// perform computation
}
};
Interface for Deployment
• Managing accelerator services: through labels
• [YARN-796] allow for labels on nodes and resource-
requests
Putting it All Together
• Register
– Interface to add
accelerator service to
corresponding nodes
• Request
– Use acc_id as label
– GAM allocates
corresponding nodes
User Application
Global ACC Manager
Node ACC
Manager
FPGA
GPU
ACC
ACC Labels Containers
ContainerInfo ACC Info
ACC Invoke
Input data
Output data
Accelerator-as-a-Service
• Logical Accelerators
– Accelerator function
– Services for applications
• Physical Accelerators
– Implementation on a specific
device (FPGA/GPU)
NAM
Logical
Queue
Logical
Queue
Logical
Queue
Physical
Queue
Physical Queue
FPGA GPU
Task Scheduler
Application Scheduler
Task Task
Task Task
Task
JVM-to-ACC Transfer Optimization
• Double-buffering / Accelerator Sharing
• Data caching
– On GPU/FPGA device memory
• Broadcast
Global FPGA Allocation Optimization
• Avoid reprogramming
• GAM policy
– Group the containers that
need the same
accelerator to the same
set of nodes Node
ACC1 ACC2
App1
container
App3
container
Node
ACC1 ACC2
App2
container
App4
container
Better container allocation
Node
ACC1 ACC 2
App1
container
App2
container
Node
ACC1 ACC2
App3
container
App4
container
Bad container allocation
Programming Efforts Reduction
Lines of Code Accelerator Management
Logistic Regression (LR) 325 à 0
Kmeans (KM) 364 à 0
Compression (ZIP) 360 à 0
Genome Sequency Alignment (GSA) 896 à 0
Heterogeneous Clusters
• CDSC and Falcon Clusters
– Low-power GPUs
– PCI-E FPGAs
• Workloads
– Genome sequencing
– Machine learning
3.0
2.0
1.7
3.2
2.6
1.8
1.5
2.8
0.0
1.0
2.0
3.0
4.0
LR KM GSA ZIP
ImprovementOver
CPUBaseline(x)
Job latency Energy
1.7x ~ 3.2x
Speedup
System Performance and Energy Efficiency
1.5x ~ 2.8x
Energy reduction
DEMO
Take Away
• Accelerator deployment can be made easy
• FPGA requires special considerations
• Key to efficiency is JVM-to-ACC overheads
– Looking for new ideas
• Blaze is an open-source project
– Looking for collaboration
About Falcon Computing
Runtime VirtualizationOffline customization
Accelerated Applications
(Spark/MapReduce/C/Java)
Falcon
Accelerator Libraries
Falcon
Merlin Compiler
Falcon
Kestrel Runtime
Blaze
Customized Platform Support
Managementtools
Accelerators
We thank our sponsors:
• NSF/Intel Innovation Transition Grant awarded to the Center for
Domain-Specific Computing
• Intel for funding and machine donations
• Xilinx for FPGA board donations
THANK YOU.
Di Wu, allwu@cs.ucla.edu
Muhuan Huang, mhhuang@cs.ucla.edu
Blaze: http://www.github.com/UCLA-VAST/blaze
Center for Domain-Specific Computing: http://cdsc.ucla.edu
Falcon Computing Solutions, Inc.: http://www.falcon-computing.com

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Deploying Accelerators At Datacenter Scale Using Spark

  • 1. Deploying Accelerators At Datacenter Scale Using Spark Di Wu and Muhuan Huang University of California, Los Angeles, Falcon Computing Solutions, Inc. UCLA Collaborators: Cody Hao Yu, Zhenman Fang, Tyson Condie and Jason Cong
  • 2. 3x speedup in 3 hours
  • 3. Accelerators in Datacenter • CPU core scaling coming to an end – Datacenters demand new technology to sustain scaling • GPU is popular, FPGA is gaining popularity – Intel prediction: 30% datacenter nodes with FPGA by 2020
  • 4. About us • UCLA Center for Domain-Specific Computing – Expeditions in Computing program from NSF in 2009 – Public-private partnership between NSF and Intel in 2014 – http://cdsc.ucla.edu • Falcon Computing Solutions, Inc. – Founded in 2014 – Enable customized computing for big data applications – http://www.falcon-computing.com
  • 5. What is FPGA? • Field Programmable Gate Array (FPGA) – Reconfigurable hardware – Can be used to accelerate specific computations • FPGA benefits – Low-power, energy efficient – Customized high performance PCI-E FPGA - IBM CAPI FPGA in CPU socket - Intel HARP
  • 6. Problems of deploying accelerators in datacenters efficiently …
  • 7. 1. Complicated Programming Model • Too much hardware specific knowledge • Lack of platform-portability
  • 8. 2. JVM-to-ACC data transfer overheads • Data serialization/deserialization • Additional data transfer
  • 9. 3. Accelerator management is non-trivial Accelerator designer System administrator Big-data application developer Does my accelerator work in your cluster …? Which cluster node has the accelerator …? How can I use your accelerator …?
  • 10. More Challenges for FPGAs 4. Reconfiguration time is long • Takes about 0.5 - 2 seconds – Transfer FPGA Binary – Reset the bits – … • Naïve runtime FPGA sharing may slow down the performance by 4x
  • 11. What we did • Provide a better programming model: – APIs for accelerator developers • Easier to integrate into big-data workload, e.g. Spark and Hadoop – APIs for big-data application developers • Requires no knowledge about accelerators • Provide an accelerator management runtime – Currently supports FPGAs and GPUs è Blaze: a system providing Accelerator-as-a-Service
  • 13. Blaze: Accelerator Runtime System Client RM AM NM NM Container Container Accelerator status GAM NAM NAM FPGA GPU GlobalAccelerator Manager accelerator-centric scheduling Node Accelerator Manager Local accelerator service management, JVM-to-ACC communication optimization GAM NAM
  • 14. Details on Programming Interfaces and Runtime Implementation
  • 15. Interface for Spark val points = sc.textfile().cache for (i <- 1 to ITERATIONS) { val gradient = points.map(p => (1 / (1 + exp(-p.y*(w dot p.x))) - 1) * p.y * p.x ).reduce(_ + _) w -= gradient } val points = blaze.wrap(sc.textfile().cache) for (i <- 1 to ITERATIONS) { val gradient = points.map( new LogisticGrad(w) ).reduce(_ + _) w -= gradient } class LogisticGrad(..) extends Accelerator[T, U] { val id: String = “Logistic” } RDD AccRDD def compute(): serialize data communicate with NAM deserialize results blaze.wrap()
  • 16. Interface for Accelerators class LogisticACC : public Task { // extend the basic Task interface LogisticACC(): Task() {;} // overwrite the compute function virtual void compute() { // get input/output using provided APIs // perform computation } };
  • 17. Interface for Deployment • Managing accelerator services: through labels • [YARN-796] allow for labels on nodes and resource- requests
  • 18. Putting it All Together • Register – Interface to add accelerator service to corresponding nodes • Request – Use acc_id as label – GAM allocates corresponding nodes User Application Global ACC Manager Node ACC Manager FPGA GPU ACC ACC Labels Containers ContainerInfo ACC Info ACC Invoke Input data Output data
  • 19. Accelerator-as-a-Service • Logical Accelerators – Accelerator function – Services for applications • Physical Accelerators – Implementation on a specific device (FPGA/GPU) NAM Logical Queue Logical Queue Logical Queue Physical Queue Physical Queue FPGA GPU Task Scheduler Application Scheduler Task Task Task Task Task
  • 20. JVM-to-ACC Transfer Optimization • Double-buffering / Accelerator Sharing • Data caching – On GPU/FPGA device memory • Broadcast
  • 21. Global FPGA Allocation Optimization • Avoid reprogramming • GAM policy – Group the containers that need the same accelerator to the same set of nodes Node ACC1 ACC2 App1 container App3 container Node ACC1 ACC2 App2 container App4 container Better container allocation Node ACC1 ACC 2 App1 container App2 container Node ACC1 ACC2 App3 container App4 container Bad container allocation
  • 22. Programming Efforts Reduction Lines of Code Accelerator Management Logistic Regression (LR) 325 à 0 Kmeans (KM) 364 à 0 Compression (ZIP) 360 à 0 Genome Sequency Alignment (GSA) 896 à 0
  • 23. Heterogeneous Clusters • CDSC and Falcon Clusters – Low-power GPUs – PCI-E FPGAs • Workloads – Genome sequencing – Machine learning
  • 24. 3.0 2.0 1.7 3.2 2.6 1.8 1.5 2.8 0.0 1.0 2.0 3.0 4.0 LR KM GSA ZIP ImprovementOver CPUBaseline(x) Job latency Energy 1.7x ~ 3.2x Speedup System Performance and Energy Efficiency 1.5x ~ 2.8x Energy reduction
  • 25. DEMO
  • 26. Take Away • Accelerator deployment can be made easy • FPGA requires special considerations • Key to efficiency is JVM-to-ACC overheads – Looking for new ideas • Blaze is an open-source project – Looking for collaboration
  • 27. About Falcon Computing Runtime VirtualizationOffline customization Accelerated Applications (Spark/MapReduce/C/Java) Falcon Accelerator Libraries Falcon Merlin Compiler Falcon Kestrel Runtime Blaze Customized Platform Support Managementtools Accelerators
  • 28. We thank our sponsors: • NSF/Intel Innovation Transition Grant awarded to the Center for Domain-Specific Computing • Intel for funding and machine donations • Xilinx for FPGA board donations
  • 29. THANK YOU. Di Wu, allwu@cs.ucla.edu Muhuan Huang, mhhuang@cs.ucla.edu Blaze: http://www.github.com/UCLA-VAST/blaze Center for Domain-Specific Computing: http://cdsc.ucla.edu Falcon Computing Solutions, Inc.: http://www.falcon-computing.com