MPI+X – The Right Programming Paradigm for Exascale?
Dhabaleswar K. (DK) Panda
The Ohio State University
E-mail: [email protected]
http://www.cse.ohio-state.edu/~panda
Talk at HP-CAST 27
by
HP-CAST 27 2Network Based Computing Laboratory
High-End Computing (HEC): ExaFlop & ExaByte
100-200
PFlops in
2016-2018
1 EFlops in
2023-2024?
10K-20K
EBytes in
2016-2018
40K EBytes
in 2020 ?
ExaFlop & HPC•
ExaByte & BigData•
HP-CAST 27 3Network Based Computing Laboratory
Trends for Commodity Computing Clusters in the Top 500 List (http://www.top500.org)
0102030405060708090100
050
100150200250300350400450500
Per
cen
tage
of
Clu
ste
rs
Nu
mb
er o
f C
lust
ers
Timeline
Percentage of Clusters
Number of Clusters
85%
HP-CAST 27 4Network Based Computing Laboratory
Drivers of Modern HPC Cluster Architectures
Tianhe – 2 Titan Stampede Tianhe – 1A
• Multi-core/many-core technologies
• Remote Direct Memory Access (RDMA)-enabled networking (InfiniBand and RoCE)
• Solid State Drives (SSDs), Non-Volatile Random-Access Memory (NVRAM), NVMe-SSD
• Accelerators (NVIDIA GPGPUs and Intel Xeon Phi)
Accelerators / Coprocessors high compute density, high
performance/watt>1 TFlop DP on a chip
High Performance Interconnects -InfiniBand
<1usec latency, 100Gbps Bandwidth>Multi-core Processors SSD, NVMe-SSD, NVRAM
HP-CAST 27 5Network Based Computing Laboratory
Designing Communication Libraries 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 ModelsPoint-to-point
Communicatio
n
Collective
Communicatio
n
Energy-
Awareness
Synchronizatio
n and Locks
I/O and
File Systems
Fault
Tolerance
HP-CAST 27 6Network Based Computing Laboratory
Exascale Programming models
Next-Generation Programming models
MPI+X
X= ?
OpenMP, OpenACC, CUDA, PGAS, Tasks….
Highly-Threaded
Systems (KNL)
Irregular
Communications
Heterogeneous
Computing (GPU)
• The community believes
exascale programming model
will be MPI+X
• But what is X?
– Can it be just OpenMP?
• Many different environments
and systems are emerging
– Different `X’ will satisfy the
respective needs
HP-CAST 27 7Network 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 (OpenMP)
• Integrated Support for GPGPUs and Accelerators (CUDA)
• Fault-tolerance/resiliency
• QoS support for communication and I/O
• Support for Hybrid MPI+PGAS programming (MPI + OpenMP, MPI + UPC, MPI + OpenSHMEM, CAF, …)
• Virtualization
• Energy-Awareness
MPI+X Programming model: Broad Challenges at Exascale
HP-CAST 27 8Network 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
HP-CAST 27 9Network 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 2,690 organizations in 83 countries
– More than 402,000 (> 0.4 million) downloads from the OSU site directly
– Empowering many TOP500 clusters (Nov ‘16 ranking)
• 1st ranked 10,649,640-core cluster (Sunway TaihuLight) at NSC, Wuxi, China
• 13th ranked 241,108-core cluster (Pleiades) at NASA
• 17th ranked 519,640-core cluster (Stampede) at TACC
• 40th ranked 76,032-core cluster (Tsubame 2.5) at Tokyo Institute of Technology and many others
– 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 at NSC, Wuxi, China (1st in Nov’16, 10,649,640 cores, 93 PFlops)
HP-CAST 27 10Network Based Computing Laboratory
• Hybrid MPI+OpenMP Models for Highly-threaded Systems
• Hybrid MPI+PGAS Models for Irregular Applications
• Hybrid MPI+GPGPUs and OpenSHMEM for Heterogeneous Computing
Outline
HP-CAST 27 11Network Based Computing Laboratory
• Systems like KNL
• MPI+OpenMP is seen as the best fit
– 1 MPI process per socket for Multi-core
– 4-8 MPI processes per KNL
– Each MPI process will launch OpenMP threads
• However, current MPI runtimes are not “efficiently” handling the hybrid
– Most of the application use Funneled mode: Only the MPI processes perform
communication
– Communication phases are the bottleneck
• Multi-endpoint based designs
– Transparently use threads inside MPI runtime
– Increase the concurrency
Highly Threaded Systems
HP-CAST 27 12Network Based Computing Laboratory
• MPI-4 will enhance the thread support
– Endpoint proposal in the Forum
– Application threads will be able to efficiently perform communication
– Endpoint is the communication entity that maps to a thread
• Idea is to have multiple addressable communication entities within a single process
• No context switch between application and runtime => better performance
• OpenMP 4.5 is more powerful than just traditional data parallelism
– Supports task parallelism since OpenMP 3.0
– Supports heterogeneous computing with accelerator targets since OpenMP 4.0
– Supports explicit SIMD and threads affinity pragmas since OpenMP 4.0
MPI and OpenMP
HP-CAST 27 13Network Based Computing Laboratory
• Lock-free Communication
– Threads have their own resources
• Dynamically adapt the number of threads
– Avoid resource contention
– Depends on application pattern and system
performance
• Both intra- and inter-nodes communication
– Threads boost both channels
• New MEP-Aware collectives
• Applicable to the endpoint proposal in MPI-4
MEP-based design: MVAPICH2 Approach
Multi-endpoint Runtime
RequestHandling
ProgressEngine
Comm.Resources
Management
Endpoint Controller
Collective
Optimized AlgorithmPoint-to-Point
MPI/OpenMP Program
MPI Collective
(MPI_Alltoallv,
MPI_Allgatherv...)
*Transparent support
MPI Point-to-Point
(MPI_Isend, MPI_Irecv,
MPI_Waitall...)
*OpenMP Pragma needed
Lock-free Communication Components
M. Luo, X. Lu, K. Hamidouche, K. Kandalla and D. K. Panda, Initial Study of Multi-Endpoint Runtime for MPI+OpenMP Hybrid
Applications on Multi-Core Systems. International Symposium on Principles and Practice of Parallel Programming (PPoPP '14).
HP-CAST 27 14Network Based Computing Laboratory
Performance Benefits: OSU Micro-Benchmarks (OMB) level
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16 64 256 1K 4K 16K 64K 256K
La
ten
cy (
us)
Message size
Orig Multi-threaded RuntimeProposed Multi-Endpoint Runtime
Process based Runtime
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origmep
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origmep
Multi-pairs Bcast Alltoallv
• Reduces the latency from 40us to 1.85 us (21X)
• Achieves the same as Processes
• 40% improvement on latency for Bcast on 4,096 cores
• 30% improvement on latency for Alltoall on 4,096 cores
HP-CAST 27 15Network Based Computing Laboratory
Performance Benefits: Application Kernel level
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CG LU MG
Tim
e (
s)
NAS Benchmark with 256 nodes (4,096 cores) CLASS E
Origmep
0
5
10
15
2K 4K
Exec
uti
on
Tim
e
# of cores and input size
P3DFFT
Orig MEP
• 6.3% improvement for MG, 11.7% improvement for CG, and 12.6% improvement for LU on 4,096
cores.
• With P3DFFT, we are able to observe a 30% improvement in communication time and 13.5%
improvement in the total execution time.
HP-CAST 27 16Network Based Computing Laboratory
• On-load approach
– Takes advantage of the idle cores
– Dynamically configurable
– Takes advantage of highly multithreaded cores
– Takes advantage of MCDRAM of KNL processors
• Applicable to other programming models such as PGAS, Task-based, etc.
• Provides portability, performance, and applicability to runtime as well as
applications in a transparent manner
Enhanced Designs for KNL: MVAPICH2 Approach
HP-CAST 27 17Network Based Computing Laboratory
Performance Benefits of the Enhanced Designs
• New designs to exploit high concurrency and MCDRAM of KNL
• Significant improvements for large message sizes
• Benefits seen in varying message size as well as varying MPI processes
Very Large Message Bi-directional Bandwidth16-process Intra-node All-to-AllIntra-node Broadcast with 64MB Message
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Ban
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MVAPICH2 MVAPICH2-Optimized
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No. of processes
MVAPICH2 MVAPICH2-Optimized
27%
0
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1M 2M 4M 8M 16M 32M
Late
ncy
(u
s)
Message size
MVAPICH2 MVAPICH2-Optimized17.2%
52%
HP-CAST 27 18Network Based Computing Laboratory
Performance Benefits of the Enhanced Designs
0
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50000
60000
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Ban
dw
idth
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B/s
)
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MV2_Opt_DRAM MV2_Opt_MCDRAM
MV2_Def_DRAM MV2_Def_MCDRAM 30%
0
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300
4:268 4:204 4:64
Tim
e (s
)
MPI Processes : OMP Threads
MV2_Def_DRAM MV2_Opt_DRAM
15%
Multi-Bandwidth using 32 MPI processesCNTK: MLP Training Time using MNIST (BS:64)
• Benefits observed on training time of Multi-level Perceptron (MLP) model on MNIST dataset
using CNTK Deep Learning Framework
Enhanced Designs will be available in upcoming MVAPICH2 releases
HP-CAST 27 19Network Based Computing Laboratory
• Hybrid MPI+OpenMP Models for Highly-threaded Systems
• Hybrid MPI+PGAS Models for Irregular Applications
• Hybrid MPI+GPGPUs and OpenSHMEM for Heterogeneous Computing
Outline
HP-CAST 27 20Network Based Computing Laboratory
Maturity of Runtimes and Application Requirements
• MPI has been the most popular model for a long time
- Available on every major machine
- Portability, performance and scaling
- Most parallel HPC code is designed using MPI
- Simplicity - structured and iterative communication patterns
• PGAS Models
- Increasing interest in community
- Simple shared memory abstractions and one-sided communication
- Easier to express irregular communication
• Need for hybrid MPI + PGAS
- Application can have kernels with different communication characteristics
- Porting only part of the applications to reduce programming effort
HP-CAST 27 21Network 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
HP-CAST 27 22Network Based Computing Laboratory
MVAPICH2-X for Hybrid MPI + PGAS Applications
MPI, OpenSHMEM, UPC, CAF, UPC++ or Hybrid (MPI +
PGAS) Applications
Unified MVAPICH2-X Runtime
InfiniBand, RoCE, iWARP
OpenSHMEM Calls MPI CallsUPC Calls
• Unified communication runtime for MPI, UPC, OpenSHMEM, CAF, UPC++ available with MVAPICH2-
X 1.9 onwards! (since 2012)
– http://mvapich.cse.ohio-state.edu
• Feature Highlights
– Supports MPI(+OpenMP), OpenSHMEM, UPC, CAF, UPC++, MPI(+OpenMP) + OpenSHMEM, MPI(+OpenMP)
+ UPC
– MPI-3 compliant, OpenSHMEM v1.0 standard compliant, UPC v1.2 standard compliant (with initial support
for UPC 1.3), CAF 2008 standard (OpenUH), UPC++
– Scalable Inter-node and intra-node communication – point-to-point and collectives
CAF Calls UPC++ Calls
HP-CAST 27 23Network Based Computing Laboratory
OpenSHMEM Atomic Operations: Performance
0
5
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15
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25
30
fadd finc add inc cswap swap
Tim
e (u
s)UH-SHMEM MV2X-SHMEM Scalable-SHMEM OMPI-SHMEM
• OSU OpenSHMEM micro-benchmarks (OMB v4.1)
• MV2-X SHMEM performs up to 40% better compared to UH-SHMEM
HP-CAST 27 24Network Based Computing Laboratory
0
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128
256
512
1K 2K 4K 8K
16K
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128K
Tim
e (
ms)
Message Size
UPC-GASNetUPC-OSU
050
100150200250
64
128
256
512
1K 2K 4K 8K
16K
32K
64K
12
8K
25
6K
Tim
e (
ms)
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UPC-GASNet
UPC-OSU 2X
UPC Collectives PerformanceBroadcast (2,048 processes) Scatter (2,048 processes)
Gather (2,048 processes) Exchange (2,048 processes)
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UPC-GASNetUPC-OSU
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16K
32K
64K
128K
256K
Tim
e (
ms)
Message Size
UPC-GASNet
UPC-OSU
25X
2X
35%
J. Jose, K. Hamidouche, J. Zhang, A. Venkatesh, and D. K. Panda, Optimizing Collective Communication in UPC (HiPS’14, in association with
IPDPS’14)
HP-CAST 27 25Network Based Computing Laboratory
Performance Evaluations for CAF model
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6000
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dw
idth
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B/s
)
Message Size (byte)
GASNet-IBV
GASNet-MPI
MV2X
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dw
idth
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B/s
)
Message Size (byte)
GASNet-IBV
GASNet-MPI
MV2X
02468
10
GASNet-IBV GASNet-MPI MV2X
La
ten
cy
(ms)
02468
10
GASNet-IBV GASNet-MPI MV2XLa
ten
cy (
ms
)
0 100 200 300
bt.D.256
cg.D.256
ep.D.256
ft.D.256
mg.D.256
sp.D.256
GASNet-IBV GASNet-MPI MV2X
Time (sec)
Get NAS-CAFPut
• Micro-benchmark improvement (MV2X vs. GASNet-IBV, UH CAF test-suite)
– Put bandwidth: 3.5X improvement on 4KB; Put latency: reduce 29% on 4B
• Application performance improvement (NAS-CAF one-sided implementation)
– Reduce the execution time by 12% (SP.D.256), 18% (BT.D.256)
3.5X
29%
12%
18%
J. Lin, K. Hamidouche, X. Lu, M. Li and D. K. Panda, High-performance Co-array Fortran support with MVAPICH2-X: Initial
experience and evaluation, HIPS’15
HP-CAST 27 26Network Based Computing Laboratory
UPC++ Collectives Performance
MPI + {UPC++}
application
GASNet Interfaces
UPC++
Runtime
Network
Conduit (MPI)
MVAPICH2-X
Unified
communication
Runtime (UCR)
MPI + {UPC++}
application
UPC++ RuntimeMPI
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
5000
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30000
35000
40000
Tim
e (u
s)
Message Size (bytes)
GASNet_MPI
GASNET_IBV
MV2-X
14x
Inter-node Broadcast (64 nodes 1:ppn)
J. M. Hashmi, K. Hamidouche, and D. K. Panda, Enabling
Performance Efficient Runtime Support for hybrid
MPI+UPC++ Programming Models, IEEE International
Conference on High Performance Computing and
Communications (HPCC 2016)
HP-CAST 27 27Network 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
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
Sort Execution Time
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Tim
e (
seco
nd
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
J. Jose, S. Potluri, H. Subramoni, X. Lu, K. Hamidouche, K. Schulz, H. Sundar and D. Panda Designing Scalable Out-of-core Sorting with Hybrid MPI+PGAS Programming Models, PGAS’14
HP-CAST 27 28Network Based Computing Laboratory
Accelerating MaTEx k-NN with Hybrid MPI and OpenSHMEM
KDD (2.5GB) on 512 cores
9.0%
KDD-tranc (30MB) on 256 cores
27.6%
• Benchmark: KDD Cup 2010 (8,407,752 records, 2 classes, k=5)• For truncated KDD workload on 256 cores, reduce 27.6% execution time• For full KDD workload on 512 cores, reduce 9.0% execution time
J. Lin, K. Hamidouche, J. Zhang, X. Lu, A. Vishnu, D. Panda. Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM,
OpenSHMEM 2015
• MaTEx: MPI-based Machine learning algorithm library• k-NN: a popular supervised algorithm for classification• Hybrid designs:
– Overlapped Data Flow; One-sided Data Transfer; Circular-buffer Structure
HP-CAST 27 29Network Based Computing Laboratory
• Hybrid MPI+OpenMP Models for Highly-threaded Systems
• Hybrid MPI+PGAS Models for Irregular Applications
• Hybrid MPI+GPGPUs and OpenSHMEM for Heterogeneous Computing
Outline
HP-CAST 27 30Network 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
HP-CAST 27 31Network 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
HP-CAST 27 32Network Based Computing Laboratory
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MV2-GDR2.2MV2-GDR2.0bMV2 w/o GDR
GPU-GPU Internode Bi-Bandwidth
Message Size (bytes)
Bi-
Ban
dw
idth
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B/s
)
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MV2-GDR2.2 MV2-GDR2.0bMV2 w/o GDR
GPU-GPU internode latency
Message Size (bytes)
Late
ncy
(u
s)
MVAPICH2-GDR-2.2Intel Ivy Bridge (E5-2680 v2) node - 20 cores
NVIDIA Tesla K40c GPUMellanox Connect-X4 EDR HCA
CUDA 8.0Mellanox OFED 3.0 with GPU-Direct-RDMA
10x2X
11x
Performance of MVAPICH2-GPU with GPU-Direct RDMA (GDR)
2.18us
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MV2-GDR2.2
MV2-GDR2.0b
MV2 w/o GDR
GPU-GPU Internode Bandwidth
Message Size (bytes)
Ba
nd
wid
th
(MB
/s) 11X
2X
3X
HP-CAST 27 33Network 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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e St
eps
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d (
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MV2 MV2+GDR
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64K Particles 256K Particles
2X2X
HP-CAST 27 34Network Based Computing Laboratory
Application-Level Evaluation (Cosmo) and Weather Forecasting in Switzerland
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CSCS GPU cluster
Default Callback-based Event-based
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e
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Wilkes GPU Cluster
Default Callback-based Event-based
• 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/
HP-CAST 27 35Network Based Computing Laboratory
• GDR for small/medium message sizes
• Host-staging for large message to avoid PCIe
bottlenecks
• Hybrid design brings best of both
• 3.13 us Put latency for 4B (7X improvement ) and 4.7
us latency for 4KB
• 9X improvement for Get latency of 4B
Exploiting GDR: OpenSHMEM: Inter-node Evaluation
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GDR
Small Message shmem_put D-D
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Late
ncy
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s)
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GDR
Large Message shmem_put D-D
Message Size (bytes)
Late
ncy
(u
s)
05
101520253035
1 4 16 64 256 1K 4K
Host-Pipeline
GDR
Small Message shmem_get D-D
Message Size (bytes)
Late
ncy
(u
s)
7X
9X
HP-CAST 27 36Network Based Computing Laboratory
0
2
4
6
8
10
1 4 16 64 256 1K 4K
IPC GDR
Small Message shmem_put D-H
Message Size (bytes)
Late
ncy
(u
s)
• GDR for small and medium message sizes
• IPC for large message to avoid PCIe bottlenecks
• Hybrid design brings best of both
• 2.42 us Put D-H latency for 4 Bytes (2.6X improvement) and 3.92 us latency for 4 KBytes
• 3.6X improvement for Get operation
• Similar results with other configurations (D-D, H-D and D-H)
OpenSHMEM: Intra-node Evaluation
0
2
4
6
8
10
12
14
1 4 16 64 256 1K 4K
IPC GDR
Small Message shmem_get D-H
Message Size (bytes)
Late
ncy
(u
s)
2.6X 3.6X
HP-CAST 27 37Network Based Computing Laboratory
0
0.05
0.1
8 16 32 64
Exec
uti
on
tim
e
(sec
)
Number of GPU Nodes
Host-Pipeline Enhanced-GDR
- Platform: Wilkes (Intel Ivy Bridge + NVIDIA Tesla K20c +
Mellanox Connect-IB)
- New designs achieve 20% and 19% improvements on 32
and 64 GPU nodes
Application Evaluation: GPULBM and 2DStencil
19%
0
100
200
8 16 32 64
Evo
luti
on
Tim
e
(mse
c)
Number of GPU Nodes
Weak Scaling Host-Pipeline Enhanced-GDR
GPULBM: 64x64x64 2DStencil 2Kx2K• Redesign the application
• CUDA-Aware MPI : Send/Recv=> hybrid CUDA-Aware MPI+OpenSHMEM• cudaMalloc =>shmalloc(size,1); • MPI_Send/recv => shmem_put + fence• 53% and 45% • Degradation is due to smallInput size • Will be available in future MVAPICH2-GDR
45 %
1. K. Hamidouche, A. Venkatesh, A. Awan, H. Subramoni, C. Ching and D. K. Panda, Exploiting
GPUDirect RDMA in Designing High Performance OpenSHMEM for GPU Clusters. IEEE Cluster 2015.
2. K. Hamidouche, A. Venkatesh, A. Awan, H. Subramoni, C. Ching and D. K. Panda, CUDA-Aware
OpenSHMEM: Extensions and Designs for High Performance OpenSHMEM on GPU Clusters.
To appear in PARCO.
HP-CAST 27 38Network Based Computing Laboratory
OpenACC-Aware MVAPICH2
• acc_malloc to allocate device memory
– No changes to MPI calls
– MVAPICH2 detects the device pointer and optimizes data movement
• acc_deviceptr to get device pointer (in OpenACC 2.0)
– Enables MPI communication from memory allocated by compiler when it is available in OpenACC 2.0 implementations
– MVAPICH2 will detect the device pointer and optimize communication
• Delivers the same performance as with CUDA
A = acc_malloc(sizeof(int) * N);
……
#pragma acc parallel loop deviceptr(A) . . .
//compute for loop
MPI_Send (A, N, MPI_INT, 0, 1, MPI_COMM_WORLD);
……
acc_free(A);
A = malloc(sizeof(int) * N);
……
#pragma acc data copyin(A) . . .
{
#pragma acc parallel loop . . .
//compute for loop
MPI_Send(acc_deviceptr(A), N, MPI_INT, 0, 1, MPI_COMM_WORLD);
}
……
free(A);
HP-CAST 27 39Network Based Computing Laboratory
• Architectures for Exascale systems are evolving
• Exascale systems will be constrained by– Power
– Memory per core
– Data movement cost
– Faults
• Programming Models, Runtimes and Middleware need to be designed for– Scalability
– Performance
– Fault-resilience
– Energy-awareness
– Programmability
– Productivity
• MPI+OpenMP may not be `the only’ paradigm
• MPI+X (with different options for X) need to be explored
• Highlighted some of these options, associated issues, and challenges
• Need continuous innovation on all these fronts to have the right paradigm
Looking into the Future ….
HP-CAST 27 40Network Based Computing Laboratory
Funding Acknowledgments
Funding Support by
Equipment Support by
HP-CAST 27 41Network Based Computing Laboratory
Personnel AcknowledgmentsCurrent Students
– A. Awan (Ph.D.)
– M. Bayatpour (Ph.D.)
– S. Chakraborthy (Ph.D.)
– C.-H. Chu (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. Vishnu (Ph.D.)
– J. Wu (Ph.D.)
– W. Yu (Ph.D.)
Past Research Scientist
– S. Sur
Past Post-Docs
– D. Banerjee
– X. Besseron
– H.-W. Jin
– W. Huang (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.)
– S. Guganani (Ph.D.)
– J. Hashmi (Ph.D.)
– N. Islam (Ph.D.)
– M. Li (Ph.D.)
– J. Lin
– M. Luo
– E. Mancini
Current Research Scientists
– K. Hamidouche
– X. Lu
– H. Subramoni
Past Programmers
– D. Bureddy
– M. Arnold
– J. Perkins
Current Research Specialist
– J. Smith– M. Rahman (Ph.D.)
– D. Shankar (Ph.D.)
– A. Venkatesh (Ph.D.)
– J. Zhang (Ph.D.)
– S. Marcarelli
– J. Vienne
– H. Wang
HP-CAST 27 42Network Based Computing Laboratory
• Three Conference Tutorials (IB+HSE, IB+HSE Advanced, Big Data)
• Intel HPC Developers Conference
• Technical Papers (SC main conference; Doctoral Showcase; Poster; PDSW-DISC, PAW, COMHPC, and ESPM2 Workshops)
• Booth Presentations (Mellanox, NVIDIA, NRL, PGAS)
• HPC Connection Workshop
• Will be stationed at Ohio Supercomputer Center/OH-TECH Booth (#1107)– Multiple presentations and demos
• More Details from http://mvapich.cse.ohio-state.edu/talks/
OSU Team Will be Participating in Multiple Events at SC ‘16
HP-CAST 27 43Network Based Computing Laboratory
Thank You!
Network-Based Computing Laboratoryhttp://nowlab.cse.ohio-state.edu/
The MVAPICH Projecthttp://mvapich.cse.ohio-state.edu/