Post on 17-Sep-2020
High-Performance and Scalable Designs of Programming Models for Exascale Systems
Dhabaleswar K. (DK) Panda
The Ohio State University
E-mail: panda@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~panda
Keynote Talk at HPCAC, Switzerland (April 2017)
by
HPCAC-Switzerland (April ‘17) 2Network Based Computing Laboratory
High-End Computing (HEC): Towards Exascale-Era
Expected to have an ExaFlop system in 2021!
150-300
PFlops in
2017-18?
1 EFlops
in 2021?
HPCAC-Switzerland (April ‘17) 3Network Based Computing Laboratory
• Scientific Computing
– Message Passing Interface (MPI), including MPI + OpenMP, is the Dominant
Programming Model
– Many discussions towards Partitioned Global Address Space (PGAS)
• UPC, OpenSHMEM, CAF, UPC++ etc.
– Hybrid Programming: MPI + PGAS (OpenSHMEM, UPC)
• Deep Learning
– Caffe, CNTK, TensorFlow, and many more
• Big Data/Enterprise/Commercial Computing
– Focuses on large data and data analysis
– Spark and Hadoop (HDFS, HBase, MapReduce)
– Memcached is also used for Web 2.0
Three Major Computing Categories
HPCAC-Switzerland (April ‘17) 4Network Based Computing Laboratory
Parallel Programming Models Overview
P1 P2 P3
Shared Memory
P1 P2 P3
Memory Memory Memory
P1 P2 P3
Memory Memory Memory
Logical shared memory
Shared Memory Model
SHMEM, DSM
Distributed Memory Model
MPI (Message Passing Interface)
Partitioned Global Address Space (PGAS)
Global Arrays, UPC, Chapel, X10, CAF, …
• Programming models provide abstract machine models
• Models can be mapped on different types of systems
– e.g. Distributed Shared Memory (DSM), MPI within a node, etc.
• PGAS models and Hybrid MPI+PGAS models are gradually receiving
importance
HPCAC-Switzerland (April ‘17) 5Network Based Computing Laboratory
Partitioned Global Address Space (PGAS) Models
• Key features
- Simple shared memory abstractions
- Light weight one-sided communication
- Easier to express irregular communication
• Different approaches to PGAS
- Languages
• Unified Parallel C (UPC)
• Co-Array Fortran (CAF)
• X10
• Chapel
- Libraries
• OpenSHMEM
• UPC++
• Global Arrays
HPCAC-Switzerland (April ‘17) 6Network Based Computing Laboratory
Hybrid (MPI+PGAS) Programming
• Application sub-kernels can be re-written in MPI/PGAS based on communication
characteristics
• Benefits:
– Best of Distributed Computing Model
– Best of Shared Memory Computing Model
Kernel 1MPI
Kernel 2MPI
Kernel 3MPI
Kernel NMPI
HPC Application
Kernel 2PGAS
Kernel NPGAS
HPCAC-Switzerland (April ‘17) 7Network Based Computing Laboratory
Designing Communication Middleware for Multi-Petaflop and Exaflop Systems: Challenges
Programming ModelsMPI, PGAS (UPC, Global Arrays, OpenSHMEM), CUDA, OpenMP,
OpenACC, Cilk, Hadoop (MapReduce), Spark (RDD, DAG), etc.
Application Kernels/Applications
Networking Technologies(InfiniBand, 40/100GigE,
Aries, and OmniPath)
Multi/Many-coreArchitectures
Accelerators(NVIDIA and MIC)
Middleware
Co-Design
Opportunities
and
Challenges
across Various
Layers
Performance
Scalability
Fault-
Resilience
Communication Library or Runtime for Programming Models
Point-to-point
Communication
Collective
Communication
Energy-
Awareness
Synchronization
and Locks
I/O and
File Systems
Fault
Tolerance
HPCAC-Switzerland (April ‘17) 8Network Based Computing Laboratory
• Scalability for million to billion processors– Support for highly-efficient inter-node and intra-node communication (both two-sided and one-sided)
– Scalable job start-up
• Scalable Collective communication– Offload
– Non-blocking
– Topology-aware
• Balancing intra-node and inter-node communication for next generation nodes (128-1024 cores)– Multiple end-points per node
• Support for efficient multi-threading
• Integrated Support for GPGPUs and Accelerators
• Fault-tolerance/resiliency
• QoS support for communication and I/O
• Support for Hybrid MPI+PGAS programming (MPI + OpenMP, MPI + UPC, MPI + OpenSHMEM, MPI+UPC++, CAF, …)
• Virtualization
• Energy-Awareness
Broad Challenges in Designing Communication Middleware for (MPI+X) at Exascale
HPCAC-Switzerland (April ‘17) 9Network Based Computing Laboratory
• Extreme Low Memory Footprint– Memory per core continues to decrease
• D-L-A Framework
– Discover
• Overall network topology (fat-tree, 3D, …), Network topology for processes for a given job
• Node architecture, Health of network and node
– Learn
• Impact on performance and scalability
• Potential for failure
– Adapt
• Internal protocols and algorithms
• Process mapping
• Fault-tolerance solutions
– Low overhead techniques while delivering performance, scalability and fault-tolerance
Additional Challenges for Designing Exascale Software Libraries
HPCAC-Switzerland (April ‘17) 10Network Based Computing Laboratory
Overview of the MVAPICH2 Project• High Performance open-source MPI Library for InfiniBand, Omni-Path, Ethernet/iWARP, and RDMA over Converged Ethernet (RoCE)
– MVAPICH (MPI-1), MVAPICH2 (MPI-2.2 and MPI-3.0), Started in 2001, First version available in 2002
– MVAPICH2-X (MPI + PGAS), Available since 2011
– Support for GPGPUs (MVAPICH2-GDR) and MIC (MVAPICH2-MIC), Available since 2014
– Support for Virtualization (MVAPICH2-Virt), Available since 2015
– Support for Energy-Awareness (MVAPICH2-EA), Available since 2015
– Support for InfiniBand Network Analysis and Monitoring (OSU INAM) since 2015
– Used by more than 2,750 organizations in 83 countries
– More than 413,000 (> 0.4 million) downloads from the OSU site directly
– Empowering many TOP500 clusters (Nov ‘16 ranking)
• 1st, 10,649,600-core (Sunway TaihuLight) at National Supercomputing Center in Wuxi, China
• 13th, 241,108-core (Pleiades) at NASA
• 17th, 462,462-core (Stampede) at TACC
• 40th, 74,520-core (Tsubame 2.5) at Tokyo Institute of Technology
– Available with software stacks of many vendors and Linux Distros (RedHat and SuSE)
– http://mvapich.cse.ohio-state.edu
• Empowering Top500 systems for over a decade
– System-X from Virginia Tech (3rd in Nov 2003, 2,200 processors, 12.25 TFlops) ->
– Sunway TaihuLight (1st in Jun’16, 10M cores, 100 PFlops)
HPCAC-Switzerland (April ‘17) 11Network Based Computing Laboratory
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Timeline
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MVAPICH2 Release Timeline and Downloads
HPCAC-Switzerland (April ‘17) 12Network Based Computing Laboratory
Architecture of MVAPICH2 Software Family
High Performance Parallel Programming Models
Message Passing Interface(MPI)
PGAS(UPC, OpenSHMEM, CAF, UPC++)
Hybrid --- MPI + X(MPI + PGAS + OpenMP/Cilk)
High Performance and Scalable Communication RuntimeDiverse APIs and Mechanisms
Point-to-
point
Primitives
Collectives
Algorithms
Energy-
Awareness
Remote
Memory
Access
I/O and
File Systems
Fault
ToleranceVirtualization
Active
MessagesJob Startup
Introspection
& Analysis
Support for Modern Networking Technology(InfiniBand, iWARP, RoCE, Omni-Path)
Support for Modern Multi-/Many-core Architectures(Intel-Xeon, OpenPower, Xeon-Phi (MIC, KNL), NVIDIA GPGPU)
Transport Protocols Modern Features
RC XRC UD DC UMR ODPSR-
IOV
Multi
Rail
Transport Mechanisms
Shared
MemoryCMA IVSHMEM
Modern Features
MCDRAM* NVLink* CAPI*
* Upcoming
HPCAC-Switzerland (April ‘17) 13Network Based Computing Laboratory
MVAPICH2 Software Family High-Performance Parallel Programming Libraries
MVAPICH2 Support for InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE
MVAPICH2-X Advanced MPI features, OSU INAM, PGAS (OpenSHMEM, UPC, UPC++, and CAF), and MPI+PGAS programming models with unified communication runtime
MVAPICH2-GDR Optimized MPI for clusters with NVIDIA GPUs
MVAPICH2-Virt High-performance and scalable MPI for hypervisor and container based HPC cloud
MVAPICH2-EA Energy aware and High-performance MPI
MVAPICH2-MIC Optimized MPI for clusters with Intel KNC
Microbenchmarks
OMB Microbenchmarks suite to evaluate MPI and PGAS (OpenSHMEM, UPC, and UPC++) libraries for CPUs and GPUs
Tools
OSU INAM Network monitoring, profiling, and analysis for clusters with MPI and scheduler integration
OEMT Utility to measure the energy consumption of MPI applications
HPCAC-Switzerland (April ‘17) 14Network Based Computing Laboratory
• Released on 03/29/2017
• Major Features and Enhancements
– Based on and ABI compatible with MPICH 3.2
– Support collective offload using Mellanox's SHArP for Allreduce
• Enhance tuning framework for Allreduce using SHArP
– Introduce capability to run MPI jobs across multiple InfiniBand subnets
– Introduce basic support for executing MPI jobs in Singularity
– Enhance collective tuning for Intel Knight's Landing and Intel Omni-path
– Enhance process mapping support for multi-threaded MPI applications
• Introduce MV2_CPU_BINDING_POLICY=hybrid
• Introduce MV2_THREADS_PER_PROCESS
– On-demand connection management for PSM-CH3 and PSM2-CH3 channels
– Enhance PSM-CH3 and PSM2-CH3 job startup to use non-blocking PMI calls
– Enhance debugging support for PSM-CH3 and PSM2-CH3 channels
– Improve performance of architecture detection
– Introduce run time parameter MV2_SHOW_HCA_BINDING to show process to HCA binding
• Enhance MV2_SHOW_CPU_BINDING to enable display of CPU bindings on all nodes
– Deprecate OFA-IB-Nemesis channel
– Update to hwloc version 1.11.6
MVAPICH2 2.3a
HPCAC-Switzerland (April ‘17) 15Network Based Computing Laboratory
• Scalability for million to billion processors– Support for highly-efficient inter-node and intra-node communication
– Support for advanced IB mechanisms (ODP)
– Extremely minimal memory footprint (DCT)
– Scalable Job Start-up
– Dynamic and Adaptive Tag Matching
– Collective Support with SHArP
• Unified Runtime for Hybrid MPI+PGAS programming (MPI + OpenSHMEM, MPI + UPC, CAF, UPC++, …)
• Integrated Support for GPGPUs
• InfiniBand Network Analysis and Monitoring (INAM)
• Virtualization and HPC Cloud
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
HPCAC-Switzerland (April ‘17) 16Network Based Computing Laboratory
One-way Latency: MPI over IB with MVAPICH2
0
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0.4
0.6
0.8
1
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1.4
1.6
1.8 Small Message Latency
Message Size (bytes)
Late
ncy
(u
s)
1.111.19
1.011.15
1.04
TrueScale-QDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB switchConnectX-3-FDR - 2.8 GHz Deca-core (IvyBridge) Intel PCI Gen3 with IB switch
ConnectIB-Dual FDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB switchConnectX-4-EDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB Switch
Omni-Path - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with Omni-Path switch
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120TrueScale-QDRConnectX-3-FDRConnectIB-DualFDRConnectX-4-EDROmni-Path
Large Message Latency
Message Size (bytes)
Late
ncy
(u
s)
HPCAC-Switzerland (April ‘17) 17Network Based Computing Laboratory
TrueScale-QDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB switchConnectX-3-FDR - 2.8 GHz Deca-core (IvyBridge) Intel PCI Gen3 with IB switch
ConnectIB-Dual FDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB switchConnectX-4-EDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 IB switch
Omni-Path - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with Omni-Path switch
Bandwidth: MPI over IB with MVAPICH2
0
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ConnectX-3-FDR
ConnectIB-DualFDR
ConnectX-4-EDR
Omni-Path
Bidirectional Bandwidth
Ban
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Byt
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sec)
Message Size (bytes)
21,227
12,161
21,983
6,228
24,136
0
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Ban
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Message Size (bytes)
12,590
3,373
6,356
12,083
12,366
HPCAC-Switzerland (April ‘17) 18Network Based Computing Laboratory
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MVAPICH2 Two-Sided Intra-Node Performance(Shared memory and Kernel-based Zero-copy Support (LiMIC and CMA))
Intel Ivy-bridge
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14,250 MB/s
13,749 MB/s
HPCAC-Switzerland (April ‘17) 19Network Based Computing Laboratory
Minimizing Memory Footprint by Direct Connect (DC) Transport
No
de
0 P1
P0
Node 1
P3
P2
Node 3
P7
P6
No
de
2 P5
P4
IBNetwork
• Constant connection cost (One QP for any peer)
• Full Feature Set (RDMA, Atomics etc)
• Separate objects for send (DC Initiator) and receive (DC Target)
– DC Target identified by “DCT Number”
– Messages routed with (DCT Number, LID)
– Requires same “DC Key” to enable communication
• Available since MVAPICH2-X 2.2a
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NAMD - Apoa1: Large data set
RC DC-Pool UD XRC
1022
4797
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1 1
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Co
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B)
Number of Processes
Memory Footprint for Alltoall
RC DC-Pool UD XRC
H. Subramoni, K. Hamidouche, A. Venkatesh, S. Chakraborty and D. K. Panda, Designing MPI Library with Dynamic Connected Transport (DCT)
of InfiniBand : Early Experiences. IEEE International Supercomputing Conference (ISC ’14)
HPCAC-Switzerland (April ‘17) 20Network Based Computing Laboratory
• Near-constant MPI and OpenSHMEM
initialization time at any process count
• 10x and 30x improvement in startup time
of MPI and OpenSHMEM respectively at
16,384 processes
• Memory consumption reduced for
remote endpoint information by
O(processes per node)
• 1GB Memory saved per node with 1M
processes and 16 processes per node
Towards High Performance and Scalable Startup at Exascale
P M
O
Job Startup Performance
Mem
ory
Req
uir
ed t
o S
tore
En
dp
oin
t In
form
atio
n
a b c d
eP
M
PGAS – State of the art
MPI – State of the art
O PGAS/MPI – Optimized
PMIX_Ring
PMIX_Ibarrier
PMIX_Iallgather
Shmem based PMI
b
c
d
e
aOn-demand Connection
On-demand Connection Management for OpenSHMEM and OpenSHMEM+MPI. S. Chakraborty, H. Subramoni, J. Perkins, A. A. Awan, and D K
Panda, 20th International Workshop on High-level Parallel Programming Models and Supportive Environments (HIPS ’15)
PMI Extensions for Scalable MPI Startup. S. Chakraborty, H. Subramoni, A. Moody, J. Perkins, M. Arnold, and D K Panda, Proceedings of the 21st
European MPI Users' Group Meeting (EuroMPI/Asia ’14)
Non-blocking PMI Extensions for Fast MPI Startup. S. Chakraborty, H. Subramoni, A. Moody, A. Venkatesh, J. Perkins, and D K Panda, 15th
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid ’15)
SHMEMPMI – Shared Memory based PMI for Improved Performance and Scalability. S. Chakraborty, H. Subramoni, J. Perkins, and D K Panda, 16th
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid ’16)
a
b
c d
e
HPCAC-Switzerland (April ‘17) 21Network Based Computing Laboratory
Scalable Job Startup with Non-blocking PMI
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16 64 256 1K 4K 16K
Tim
e Ta
ken
fo
r M
PI_
Init
(s
eco
nd
s)
Number of Processes
Hello World - MV2-2.0 +Default SLURMMPI_Init - MV2-2.0 +Default SLURMHello World - MV2-2.1 +Optimized SLURMMPI_Init - MV2-2.1 +Optimized SLURM 0
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Tim
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con
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Hello World
MPI_Init
• Near-constant MPI_Init at any scale• MPI_Init is 59 times faster• Hello World (MPI_Init + MPI_Finalize) takes 5.7 times
less time with 8,192 processes (512 nodes)• 16,384 processes, 1,024 Nodes (Sandy Bridge + FDR)
• Non-blocking PMI implementation in mpirun_rsh • On-demand connection management for PSM
and Omni-path• Efficient intra-node startup for Knights Landing• MPI_Init takes 5.8 seconds• Hello World takes 21 seconds• 65,536 processes, 1,024 Nodes (KNL + Omni-Path)
Co-designing Resource Manager and MPI library Resource Manager Independent Design
New designs available in MVAPICH2-2.3a and as patch for SLURM-15.08.8 and SLURM-16.05.1
HPCAC-Switzerland (April ‘17) 22Network Based Computing Laboratory
• Introduced by Mellanox to avoid pinning the pages of registered memory regions
• ODP-aware runtime could reduce the size of pin-down buffers while maintaining performance
• Available in MVAPICH2-X 2.2
On-Demand Paging (ODP)
M. Li, K. Hamidouche, X. Lu, H. Subramoni, J. Zhang, and D. K. Panda, “Designing MPI Library with On-Demand Paging (ODP) of
InfiniBand: Challenges and Benefits”, SC, 2016
1
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1000
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e(s
)
Applications (64 Processes)Pin-down
ODP
HPCAC-Switzerland (April ‘17) 23Network Based Computing Laboratory
Dynamic and Adaptive Tag Matching
Normalized Total Tag Matching Time at 512 ProcessesNormalized to Default (Lower is Better)
Normalized Memory Overhead per Process at 512 ProcessesCompared to Default (Lower is Better)
Adaptive and Dynamic Design for MPI Tag Matching; M. Bayatpour, H. Subramoni, S. Chakraborty, and D. K. Panda; IEEE Cluster 2016. [Best Paper Nominee]
Ch
alle
nge
Tag matching is a significant overhead for receivers
Existing Solutions are
- Static and do not adapt dynamically to communication pattern
- Do not consider memory overhead
Solu
tio
n A new tag matching design
- Dynamically adapt to communication patterns
- Use different strategies for different ranks
- Decisions are based on the number of request object that must be traversed before hitting on the required one
Res
ult
s Better performance than other state-of-the art tag-matching schemes
Minimum memory consumption
Will be available in future MVAPICH2 releases
HPCAC-Switzerland (April ‘17) 24Network Based Computing Laboratory
0
2
4
6
8
(4,4) (8,4) (16,4)
Late
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(u
s)
(Number of Nodes, PPN)
MV2-128B
MV2-SHArP-128B
MV2-32B
MV2-SHArP-32B 2.3x
0
2
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4 8 16 32 64 128 256
Late
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s)
Message Size (Bytes)
4 PPN*, 16 NodesMVAPICH2
MVAPICH2-SHArP
02468
10121416
4 8 16 32 64 128 256
Late
ncy
(u
s)
Message Size (Bytes)
28 PPN, 16 NodesMVAPICH2
MVAPICH2-SHArP
Advanced Allreduce Collective Designs Using SHArPosu_allreduce (OSU Micro Benchmark) using MVAPICH2 2.3a
2.3x
*PPN: Processes Per Node
1.5x
0
2
4
6
8
10
12
(4,28) (8,28) (16,28)La
ten
cy (
us)
(Number of Nodes, PPN)
MV2-128B
MV2-SHArP-128B
MV2-32B
MV2-SHArP-32B
1.4x
HPCAC-Switzerland (April ‘17) 25Network Based Computing Laboratory
0
0.05
0.1
0.15
0.2
(4,28) (8,28) (16,28)
Late
ncy
(se
con
ds)
(Number of Nodes, PPN)
MVAPICH2
MVAPICH2-SHArP13%
Mesh Refinement Time of MiniAMR
0
0.1
0.2
0.3
0.4
(4,28) (8,28) (16,28)
Late
ncy
(se
con
ds)
(Number of Nodes, PPN)
MVAPICH2
MVAPICH2-SHArP
Advanced Allreduce Collective Designs Using SHArP
12%
Avg DDOT Allreduce time of HPCG
Based on MVAPICH2 2.3a
HPCAC-Switzerland (April ‘17) 26Network Based Computing Laboratory
• Scalability for million to billion processors
• Unified Runtime for Hybrid MPI+PGAS programming (MPI + OpenSHMEM, MPI + UPC, CAF, UPC++, …)
• Integrated Support for GPGPUs
• InfiniBand Network Analysis and Monitoring (INAM)
• Virtualization and HPC Cloud
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
HPCAC-Switzerland (April ‘17) 27Network Based Computing Laboratory
MVAPICH2-X for Hybrid MPI + PGAS Applications
• Current Model – Separate Runtimes for OpenSHMEM/UPC/UPC++/CAF and MPI– Possible deadlock if both runtimes are not progressed
– Consumes more network resource
• Unified communication runtime for MPI, UPC, UPC++, OpenSHMEM, CAF
– Available with since 2012 (starting with MVAPICH2-X 1.9)
– http://mvapich.cse.ohio-state.edu
HPCAC-Switzerland (April ‘17) 28Network Based Computing Laboratory
UPC++ Support in MVAPICH2-X
MPI + {UPC++} Application
GASNet Interfaces
UPC++ Interface
Network
Conduit (MPI)
MVAPICH2-XUnified Communication
Runtime (UCR)
MPI + {UPC++} Application
UPC++ Runtime
MPI Interfaces
• Full and native support for hybrid MPI + UPC++ applications
• Better performance compared to IBV and MPI conduits
• OSU Micro-benchmarks (OMB) support for UPC++
• Available since MVAPICH2-X (2.2rc1)
0
5
10
15
20
25
30
35
40
1K 2K 4K 8K 16K 32K 64K 128K256K512K 1M
Tim
e (m
s)
Message Size (bytes)
GASNet_MPI
GASNET_IBV
MV2-X14x
Inter-node Broadcast (64 nodes 1:ppn)
MPI Runtime
HPCAC-Switzerland (April ‘17) 29Network Based Computing Laboratory
Application Level Performance with Graph500 and SortGraph500 Execution Time
J. Jose, S. Potluri, K. Tomko and D. K. Panda, Designing Scalable Graph500 Benchmark with Hybrid MPI+OpenSHMEM Programming Models,
International Supercomputing Conference (ISC’13), June 2013
J. Jose, K. Kandalla, M. Luo and D. K. Panda, Supporting Hybrid MPI and OpenSHMEM over InfiniBand: Design and Performance Evaluation,
Int'l Conference on Parallel Processing (ICPP '12), September 2012
05
101520253035
4K 8K 16K
Tim
e (
s)
No. of Processes
MPI-Simple
MPI-CSC
MPI-CSR
Hybrid (MPI+OpenSHMEM)13X
7.6X
• Performance of Hybrid (MPI+ OpenSHMEM) Graph500 Design
• 8,192 processes- 2.4X improvement over MPI-CSR
- 7.6X improvement over MPI-Simple
• 16,384 processes- 1.5X improvement over MPI-CSR
- 13X improvement over MPI-Simple
J. Jose, K. Kandalla, S. Potluri, J. Zhang and D. K. Panda, Optimizing Collective Communication in OpenSHMEM, Int'l Conference on Partitioned
Global Address Space Programming Models (PGAS '13), October 2013.
Sort Execution Time
0
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3000
500GB-512 1TB-1K 2TB-2K 4TB-4K
Tim
e (
seco
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s)
Input Data - No. of Processes
MPI Hybrid
51%
• Performance of Hybrid (MPI+OpenSHMEM) Sort Application
• 4,096 processes, 4 TB Input Size- MPI – 2408 sec; 0.16 TB/min
- Hybrid – 1172 sec; 0.36 TB/min
- 51% improvement over MPI-design
HPCAC-Switzerland (April ‘17) 30Network Based Computing Laboratory
• Scalability for million to billion processors
• Unified Runtime for Hybrid MPI+PGAS programming (MPI + OpenSHMEM, MPI + UPC, CAF, UPC++, …)
• Integrated Support for GPGPUs– CUDA-aware MPI
– GPUDirect RDMA (GDR) Support
– Preliminary Evaluation on OpenPower
• InfiniBand Network Analysis and Monitoring (INAM)
• Virtualization and HPC Cloud
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
HPCAC-Switzerland (April ‘17) 31Network Based Computing Laboratory
At Sender:
At Receiver:
MPI_Recv(r_devbuf, size, …);
inside
MVAPICH2
• Standard MPI interfaces used for unified data movement
• Takes advantage of Unified Virtual Addressing (>= CUDA 4.0)
• Overlaps data movement from GPU with RDMA transfers
High Performance and High Productivity
MPI_Send(s_devbuf, size, …);
GPU-Aware (CUDA-Aware) MPI Library: MVAPICH2-GPU
HPCAC-Switzerland (April ‘17) 32Network Based Computing Laboratory
CUDA-Aware MPI: MVAPICH2-GDR 1.8-2.2 Releases• Support for MPI communication from NVIDIA GPU device memory
• High performance RDMA-based inter-node point-to-point communication (GPU-GPU, GPU-Host and Host-GPU)
• High performance intra-node point-to-point communication for multi-GPU adapters/node (GPU-GPU, GPU-Host and Host-GPU)
• Taking advantage of CUDA IPC (available since CUDA 4.1) in intra-node communication for multiple GPU adapters/node
• Optimized and tuned collectives for GPU device buffers
• MPI datatype support for point-to-point and collective communication from GPU device buffers
• Unified memory
HPCAC-Switzerland (April ‘17) 33Network Based Computing Laboratory
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NVIDIA Tesla K40c GPUMellanox Connect-X4 EDR HCA
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HPCAC-Switzerland (April ‘17) 34Network Based Computing Laboratory
• Platform: Wilkes (Intel Ivy Bridge + NVIDIA Tesla K20c + Mellanox Connect-IB)
• HoomdBlue Version 1.0.5
• GDRCOPY enabled: MV2_USE_CUDA=1 MV2_IBA_HCA=mlx5_0 MV2_IBA_EAGER_THRESHOLD=32768
MV2_VBUF_TOTAL_SIZE=32768 MV2_USE_GPUDIRECT_LOOPBACK_LIMIT=32768
MV2_USE_GPUDIRECT_GDRCOPY=1 MV2_USE_GPUDIRECT_GDRCOPY_LIMIT=16384
Application-Level Evaluation (HOOMD-blue)
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HPCAC-Switzerland (April ‘17) 35Network Based Computing Laboratory
Application-Level Evaluation (Cosmo) and Weather Forecasting in Switzerland
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• 2X improvement on 32 GPUs nodes• 30% improvement on 96 GPU nodes (8 GPUs/node)
C. Chu, K. Hamidouche, A. Venkatesh, D. Banerjee , H. Subramoni, and D. K. Panda, Exploiting Maximal Overlap for Non-Contiguous Data
Movement Processing on Modern GPU-enabled Systems, IPDPS’16
On-going collaboration with CSCS and MeteoSwiss (Switzerland) in co-designing MV2-GDR and Cosmo Application
Cosmo model: http://www2.cosmo-model.org/content
/tasks/operational/meteoSwiss/
HPCAC-Switzerland (April ‘17) 36Network Based Computing Laboratory
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Intra-node Numbers (Basic Configuration)
HPCAC-Switzerland (April ‘17) 37Network Based Computing Laboratory
• Scalability for million to billion processors
• Unified Runtime for Hybrid MPI+PGAS programming (MPI + OpenSHMEM, MPI + UPC, CAF, UPC++, …)
• Integrated Support for GPGPUs
• Energy-Awareness
• InfiniBand Network Analysis and Monitoring (INAM)
• Virtualization and HPC Cloud
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
HPCAC-Switzerland (April ‘17) 38Network Based Computing Laboratory
Overview of OSU INAM• A network monitoring and analysis tool that is capable of analyzing traffic on the InfiniBand network with inputs from the
MPI runtime
– http://mvapich.cse.ohio-state.edu/tools/osu-inam/
• Monitors IB clusters in real time by querying various subnet management entities and gathering input from the MPI runtimes
• OSU INAM v0.9.1 released on 05/13/16
• Significant enhancements to user interface to enable scaling to clusters with thousands of nodes
• Improve database insert times by using 'bulk inserts‘
• Capability to look up list of nodes communicating through a network link
• Capability to classify data flowing over a network link at job level and process level granularity in conjunction with
MVAPICH2-X 2.2rc1
• “Best practices “ guidelines for deploying OSU INAM on different clusters
• Capability to analyze and profile node-level, job-level and process-level activities for MPI communication
– Point-to-Point, Collectives and RMA
• Ability to filter data based on type of counters using “drop down” list
• Remotely monitor various metrics of MPI processes at user specified granularity
• "Job Page" to display jobs in ascending/descending order of various performance metrics in conjunction with MVAPICH2-X
• Visualize the data transfer happening in a “live” or “historical” fashion for entire network, job or set of nodes
HPCAC-Switzerland (April ‘17) 39Network Based Computing Laboratory
OSU INAM Features
• Show network topology of large clusters
• Visualize traffic pattern on different links
• Quickly identify congested links/links in error state
• See the history unfold – play back historical state of the network
Comet@SDSC --- Clustered View
(1,879 nodes, 212 switches, 4,377 network links)
Finding Routes Between Nodes
HPCAC-Switzerland (April ‘17) 40Network Based Computing Laboratory
OSU INAM Features (Cont.)
Visualizing a Job (5 Nodes)
• Job level view• Show different network metrics (load, error, etc.) for any live job
• Play back historical data for completed jobs to identify bottlenecks
• Node level view - details per process or per node• CPU utilization for each rank/node
• Bytes sent/received for MPI operations (pt-to-pt, collective, RMA)
• Network metrics (e.g. XmitDiscard, RcvError) per rank/node
Estimated Process Level Link Utilization
• Estimated Link Utilization view• Classify data flowing over a network link at
different granularity in conjunction with
MVAPICH2-X 2.2rc1
• Job level and
• Process level
HPCAC-Switzerland (April ‘17) 41Network Based Computing Laboratory
• Scalability for million to billion processors
• Unified Runtime for Hybrid MPI+PGAS programming (MPI + OpenSHMEM, MPI + UPC, CAF, UPC++, …)
• Integrated Support for GPGPUs
• InfiniBand Network Analysis and Monitoring (INAM)
• Virtualization and HPC Cloud
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
HPCAC-Switzerland (April ‘17) 42Network Based Computing Laboratory
• Virtualization has many benefits– Fault-tolerance
– Job migration
– Compaction
• Have not been very popular in HPC due to overhead associated with Virtualization
• New SR-IOV (Single Root – IO Virtualization) support available with Mellanox InfiniBand adapters changes the field
• Enhanced MVAPICH2 support for SR-IOV
• MVAPICH2-Virt 2.1 (with and without OpenStack) is publicly available
Can HPC and Virtualization be Combined?
J. Zhang, X. Lu, J. Jose, R. Shi and D. K. Panda, Can Inter-VM Shmem Benefit MPI Applications on SR-IOV based
Virtualized InfiniBand Clusters? EuroPar'14
J. Zhang, X. Lu, J. Jose, M. Li, R. Shi and D.K. Panda, High Performance MPI Libray over SR-IOV enabled InfiniBand
Clusters, HiPC’14 J. Zhang, X .Lu, M. Arnold and D. K. Panda, MVAPICH2 Over OpenStack with SR-IOV: an Efficient Approach to build
HPC Clouds, CCGrid’15
HPCAC-Switzerland (April ‘17) 43Network Based Computing Laboratory
• Redesign MVAPICH2 to make it
virtual machine aware
– SR-IOV shows near to native
performance for inter-node point to
point communication
– IVSHMEM offers zero-copy access to
data on shared memory of co-resident
VMs
– Locality Detector: maintains the locality
information of co-resident virtual machines
– Communication Coordinator: selects the
communication channel (SR-IOV, IVSHMEM)
adaptively
Overview of MVAPICH2-Virt with SR-IOV and IVSHMEM
Host Environment
Guest 1
Hypervisor PF Driver
Infiniband Adapter
Physical Function
user space
kernel space
MPI proc
PCI Device
VF Driver
Guest 2
user space
kernel space
MPI proc
PCI Device
VF Driver
Virtual
Function
Virtual
Function
/dev/shm/
IV-SHM
IV-Shmem Channel
SR-IOV Channel
J. Zhang, X. Lu, J. Jose, R. Shi, D. K. Panda. Can Inter-VM
Shmem Benefit MPI Applications on SR-IOV based
Virtualized InfiniBand Clusters? Euro-Par, 2014.
J. Zhang, X. Lu, J. Jose, R. Shi, M. Li, D. K. Panda. High
Performance MPI Library over SR-IOV Enabled InfiniBand
Clusters. HiPC, 2014.
HPCAC-Switzerland (April ‘17) 44Network Based Computing Laboratory
• OpenStack is one of the most popular
open-source solutions to build clouds and
manage virtual machines
• Deployment with OpenStack
– Supporting SR-IOV configuration
– Supporting IVSHMEM configuration
– Virtual Machine aware design of MVAPICH2
with SR-IOV
• An efficient approach to build HPC Clouds
with MVAPICH2-Virt and OpenStack
MVAPICH2-Virt with SR-IOV and IVSHMEM over OpenStack
J. Zhang, X. Lu, M. Arnold, D. K. Panda. MVAPICH2 over OpenStack with SR-IOV: An Efficient Approach to Build
HPC Clouds. CCGrid, 2015.
HPCAC-Switzerland (April ‘17) 45Network Based Computing Laboratory
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• 32 VMs, 6 Core/VM
• Compared to Native, 2-5% overhead for Graph500 with 128 Procs
• Compared to Native, 1-9.5% overhead for SPEC MPI2007 with 128 Procs
Application-Level Performance on Chameleon
SPEC MPI2007Graph500
5%
HPCAC-Switzerland (April ‘17) 46Network Based Computing Laboratory
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• 64 Containers across 16 nodes, pining 4 Cores per Container
• Compared to Container-Def, up to 11% and 16% of execution time reduction for NAS and Graph 500
• Compared to Native, less than 9 % and 4% overhead for NAS and Graph 500
• Optimized Container support is available in the latest MVAPICH2-Virt
Containers Support: Application-Level Performance on Chameleon
Graph 500NAS
11%
16%
HPCAC-Switzerland (April ‘17) 47Network Based Computing Laboratory
MVAPICH2 Point-to-Point and Collectives Performance with Singularity
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HPCAC-Switzerland (April ‘17) 48Network Based Computing Laboratory
High Performance VM Migration Framework for MPI Applications on SR-IOV enabled InfiniBand Clusters
J. Zhang, X. Lu, D. K. Panda. High-Performance Virtual Machine Migration Framework for MPI Applications on SR-IOV
enabled InfiniBand Clusters. IPDPS, 2017 (Support will be available in upcoming MVAPICH2-Virt)
• Migration with SR-IOV device has to handle the
challenges of detachment/re-attachment of
virtualized IB device and IB connection
• Consist of SR-IOV enabled IB Cluster and External
Migration Controller
• Multiple parallel libraries to notify MPI
applications during migration (detach/reattach
SR-IOV/IVShmem, migrate VMs, migration status)
• Handle the IB connection suspending and
reactivating
• Propose Progress engine (PE) and migration
thread based (MT) design to optimize VM
migration and MPI application performance
HPCAC-Switzerland (April ‘17) 49Network Based Computing Laboratory
• Scientific Computing
– Message Passing Interface (MPI), including MPI + OpenMP, is the Dominant
Programming Model
– Many discussions towards Partitioned Global Address Space (PGAS)
• UPC, OpenSHMEM, CAF, UPC++ etc.
– Hybrid Programming: MPI + PGAS (OpenSHMEM, UPC)
• Deep Learning
– Caffe, CNTK, TensorFlow, and many more
Major Computing Categories
HPCAC-Switzerland (April ‘17) 50Network Based Computing Laboratory
• Deep Learning frameworks are a different game
altogether
– Unusually large message sizes (order of
megabytes)
– Most communication based on GPU buffers
• How to address these newer requirements?
– GPU-specific Communication Libraries (NCCL)
• NVidia's NCCL library provides inter-GPU
communication
– CUDA-Aware MPI (MVAPICH2-GDR)
• Provides support for GPU-based communication
• Can we exploit CUDA-Aware MPI and NCCL to
support Deep Learning applications?
Deep Learning: New Challenges for MPI Runtimes
1
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InternodeComm.(Knomial)
1 2
CPU
PLX
3 4
PLX
Intranode Comm.(NCCLRing)
RingDirection
Hierarchical Communication (Knomial + NCCL ring)
HPCAC-Switzerland (April ‘17) 51Network Based Computing Laboratory
• NCCL has some limitations
– Only works for a single node, thus, no scale-out on
multiple nodes
– Degradation across IOH (socket) for scale-up (within a
node)
• We propose optimized MPI_Bcast
– Communication of very large GPU buffers (order of
megabytes)
– Scale-out on large number of dense multi-GPU nodes
• Hierarchical Communication that efficiently exploits:
– CUDA-Aware MPI_Bcast in MV2-GDR
– NCCL Broadcast primitive
Efficient Broadcast: MVAPICH2-GDR and NCCL
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Performance Benefits: Microsoft CNTK DL framework (25% avg. improvement )
Performance Benefits: OSU Micro-benchmarks
Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning, A. Awan , K. Hamidouche , A. Venkatesh , and D. K. Panda, The 23rd European MPI Users' Group Meeting (EuroMPI 16), Sep 2016 [Best Paper Runner-Up]
HPCAC-Switzerland (April ‘17) 52Network Based Computing Laboratory
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Large Message Optimized Collectives for Deep Learning
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• MV2-GDR provides
optimized collectives for
large message sizes
• Optimized Reduce,
Allreduce, and Bcast
• Good scaling with large
number of GPUs
• Available with MVAPICH2-
GDR 2.2GA
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HPCAC-Switzerland (April ‘17) 53Network Based Computing Laboratory
• Caffe : A flexible and layered Deep Learning
framework.
• Benefits and Weaknesses
– Multi-GPU Training within a single node
– Performance degradation for GPUs across different
sockets
– Limited Scale-out
• OSU-Caffe: MPI-based Parallel Training
– Enable Scale-up (within a node) and Scale-out
(across multi-GPU nodes)
– network on ImageNet dataset
OSU-Caffe: Scalable Deep Learning
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GoogLeNet (ImageNet) on 128 GPUs
Caffe OSU-Caffe (1024) OSU-Caffe (2048)
Invalid use caseOSU-Caffe is publicly available from: http://hidl.cse.ohio-state.edu
Awan, K. Hamidouche, J. Hashmi, and D. K. Panda, S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters,PPoPP, Sep 2017
HPCAC-Switzerland (April ‘17) 54Network Based Computing Laboratory
MVAPICH2 – Plans for Exascale
• Performance and Memory scalability toward 1M cores
• Hybrid programming (MPI + OpenSHMEM, MPI + UPC, MPI + CAF …)• MPI + Task*
• Enhanced Optimization for GPU Support and Accelerators
• Enhanced communication schemes for upcoming architectures• Knights Landing with MCDRAM*
• NVLINK*
• CAPI*
• Extended topology-aware collectives
• Extended Energy-aware designs and Virtualization Support
• Extended Support for MPI Tools Interface (as in MPI 3.1)
• Extended Checkpoint-Restart and migration support with SCR
• Support for * features will be available in future MVAPICH2 Releases
HPCAC-Switzerland (April ‘17) 55Network Based Computing Laboratory
Funding Acknowledgments
Funding Support by
Equipment Support by
HPCAC-Switzerland (April ‘17) 56Network Based Computing Laboratory
Personnel AcknowledgmentsCurrent Students
– A. Awan (Ph.D.)
– R. Biswas (M.S.)
– M. Bayatpour (Ph.D.)
– S. Chakraborthy (Ph.D.)
Past Students
– A. Augustine (M.S.)
– P. Balaji (Ph.D.)
– S. Bhagvat (M.S.)
– A. Bhat (M.S.)
– D. Buntinas (Ph.D.)
– L. Chai (Ph.D.)
– B. Chandrasekharan (M.S.)
– N. Dandapanthula (M.S.)
– V. Dhanraj (M.S.)
– T. Gangadharappa (M.S.)
– K. Gopalakrishnan (M.S.)
– R. Rajachandrasekar (Ph.D.)
– G. Santhanaraman (Ph.D.)
– A. Singh (Ph.D.)
– J. Sridhar (M.S.)
– S. Sur (Ph.D.)
– H. Subramoni (Ph.D.)
– K. Vaidyanathan (Ph.D.)
– A. Venkatesh (Ph.D.)
– A. Vishnu (Ph.D.)
– J. Wu (Ph.D.)
– W. Yu (Ph.D.)
Past Research Scientist
– K. Hamidouche
– S. Sur
Past Post-Docs
– D. Banerjee
– X. Besseron
– H.-W. Jin
– W. Huang (Ph.D.)
– N. Islam (Ph.D.)
– W. Jiang (M.S.)
– J. Jose (Ph.D.)
– S. Kini (M.S.)
– M. Koop (Ph.D.)
– K. Kulkarni (M.S.)
– R. Kumar (M.S.)
– S. Krishnamoorthy (M.S.)
– K. Kandalla (Ph.D.)
– P. Lai (M.S.)
– J. Liu (Ph.D.)
– M. Luo (Ph.D.)
– A. Mamidala (Ph.D.)
– G. Marsh (M.S.)
– V. Meshram (M.S.)
– A. Moody (M.S.)
– S. Naravula (Ph.D.)
– R. Noronha (Ph.D.)
– X. Ouyang (Ph.D.)
– S. Pai (M.S.)
– S. Potluri (Ph.D.)
– M.-W. Rahman (Ph.D.)
– C.-H. Chu (Ph.D.)
– S. Guganani (Ph.D.)
– J. Hashmi (Ph.D.)
– H. Javed (Ph.D.)
– J. Lin
– M. Luo
– E. Mancini
Current Research Scientists
– X. Lu
– H. Subramoni
Past Programmers
– D. Bureddy
– M. Arnold
– J. Perkins
Current Research Specialist
– J. Smith– M. Li (Ph.D.)
– D. Shankar (Ph.D.)
– H. Shi (Ph.D.)
– J. Zhang (Ph.D.)
– S. Marcarelli
– J. Vienne
– H. Wang
HPCAC-Switzerland (April ‘17) 57Network Based Computing Laboratory
International Workshop on Communication Architectures at Extreme Scale (ExaComm)
ExaComm series is being held with Int’l Supercomputing Conference
(ISC) since 2015
ExaComm 2017 will be held in conjunction with ISC ’17
http://nowlab.cse.ohio-state.edu/exacomm/
Keynote Talk: Jeffrey S. Vetter, ORNL
Best Paper Award
Technical Paper Submission Deadline: Friday, April 14, 2017
HPCAC-Switzerland (April ‘17) 58Network Based Computing Laboratory
panda@cse.ohio-state.edu
Thank You!
The High-Performance Deep Learning Projecthttp://hidl.cse.ohio-state.edu/
Network-Based Computing Laboratoryhttp://nowlab.cse.ohio-state.edu/
The MVAPICH2 Projecthttp://mvapich.cse.ohio-state.edu/