Designing Scalable HPC, Deep Learning, Big Data, and Cloud Middleware for Exascale Systems
Dhabaleswar K. (DK) Panda
The Ohio State University
E-mail: [email protected]
http://www.cse.ohio-state.edu/~panda
Talk at SCEC ’18 Workshop
by
SCEC (Dec ‘18) 2Network Based Computing Laboratory
Big Data (Hadoop, Spark,
HBase, Memcached,
etc.)
Deep Learning(Caffe, TensorFlow, BigDL,
etc.)
HPC (MPI, RDMA, Lustre, etc.)
Increasing Usage of HPC, Big Data and Deep Learning
Convergence of HPC, Big Data, and Deep Learning!
Increasing Need to Run these applications on the Cloud!!
SCEC (Dec ‘18) 3Network Based Computing Laboratory
Can We Run Big Data and Deep Learning Jobs on Existing HPC Infrastructure?
Physical Compute
SCEC (Dec ‘18) 4Network Based Computing Laboratory
Can We Run Big Data and Deep Learning Jobs on Existing HPC Infrastructure?
SCEC (Dec ‘18) 5Network Based Computing Laboratory
Can We Run Big Data and Deep Learning Jobs on Existing HPC Infrastructure?
SCEC (Dec ‘18) 6Network Based Computing Laboratory
Can We Run Big Data and Deep Learning Jobs on Existing HPC Infrastructure?
Spark Job
Hadoop Job Deep LearningJob
SCEC (Dec ‘18) 7Network Based Computing Laboratory
• Traditional HPC
– Message Passing Interface (MPI), including MPI + OpenMP
– Exploiting Accelerators
• Deep Learning
– Caffe, CNTK, TensorFlow, and many more
• Big Data/Enterprise/Commercial Computing
– Spark and Hadoop (HDFS, HBase, MapReduce)
– Deep Learning over Big Data (DLoBD)
• Cloud for HPC and BigData
– Virtualization with SR-IOV and Containers
HPC, Big Data, Deep Learning, and Cloud
SCEC (Dec ‘18) 8Network 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
SCEC (Dec ‘18) 9Network Based Computing Laboratory
Supporting Programming Models 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 Omni-Path)
Multi-/Many-coreArchitectures
Accelerators(GPU and FPGA)
MiddlewareCo-Design
Opportunities
and
Challenges
across Various
Layers
Performance
Scalability
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
SCEC (Dec ‘18) 10Network 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
– Low memory footprint
• 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 Accelerators (GPGPUs and FPGAs)
• 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 Runtimes for (MPI+X) at Exascale
SCEC (Dec ‘18) 11Network 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-3.1), 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 2,950 organizations in 86 countries
– More than 511,000 (> 0.5 million) downloads from the OSU site directly
– Empowering many TOP500 clusters (Nov ‘18 ranking)
• 3rd ranked 10,649,640-core cluster (Sunway TaihuLight) at NSC, Wuxi, China
• 14th, 556,104 cores (Oakforest-PACS) in Japan
• 17th, 367,024 cores (Stampede2) at TACC
• 27th, 241,108-core (Pleiades) at NASA and many others
– Available with software stacks of many vendors and Linux Distros (RedHat, SuSE, and OpenHPC)
– http://mvapich.cse.ohio-state.edu
• Empowering Top500 systems for over a decade
Partner in the upcoming TACC Frontera System
SCEC (Dec ‘18) 12Network Based Computing Laboratory
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Timeline
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SCEC (Dec ‘18) 13Network 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, ARM, 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
XPMEM
SCEC (Dec ‘18) 14Network Based Computing Laboratory
MVAPICH2 Software Family
Requirements Library
MPI with IB, iWARP, Omni-Path, and RoCE MVAPICH2
Advanced MPI Features/Support, OSU INAM, PGAS and MPI+PGAS with IB, Omni-Path, and RoCE
MVAPICH2-X
MPI with IB, RoCE & GPU and Support for Deep Learning MVAPICH2-GDR
HPC Cloud with MPI & IB MVAPICH2-Virt
Energy-aware MPI with IB, iWARP and RoCE MVAPICH2-EA
MPI Energy Monitoring Tool OEMT
InfiniBand Network Analysis and Monitoring OSU INAM
Microbenchmarks for Measuring MPI and PGAS Performance OMB
SCEC (Dec ‘18) 15Network Based Computing Laboratory
• Scalability for million to billion processors– Support for highly-efficient inter-node and intra-node communication
– Scalable Start-up
– Optimized Collectives using SHArP and Multi-Leaders
– Optimized CMA-based and XPMEM-based Collectives
– Asynchronous Progress
• Exploiting Accelerators (NVIDIA GPGPUs)
• Optimized MVAPICH2 for OpenPower (with/ NVLink) and ARM
• Application Scalability and Best Practices
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
SCEC (Dec ‘18) 16Network Based Computing Laboratory
One-way Latency: MPI over IB with MVAPICH2
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ConnectIB-Dual FDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB switchConnectX-5-EDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with IB Switch
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SCEC (Dec ‘18) 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-5-EDR - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 IB switch
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SCEC (Dec ‘18) 18Network Based Computing Laboratory
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MPI_Init Hello World
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• MPI_Init takes 22 seconds on 231,936 processes on 3,624 KNL nodes (Stampede2 – Full scale)• At 64K processes, MPI_Init and Hello World takes 5.8s and 21s respectively (Oakforest-PACS)• All numbers reported with 64 processes per node, MVAPICH2-2.3a• Designs integrated with mpirun_rsh, available for srun (SLURM launcher) as well
SCEC (Dec ‘18) 19Network Based Computing Laboratory
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Benefits of SHARP Allreduce at Application Level
12%
Avg DDOT Allreduce time of HPCG
SHARP support available since MVAPICH2 2.3a
Parameter Description Default
MV2_ENABLE_SHARP=1 Enables SHARP-based collectives Disabled
--enable-sharp Configure flag to enable SHARP Disabled
• Refer to Running Collectives with Hardware based SHARP support section of MVAPICH2 user guide for more information
• http://mvapich.cse.ohio-state.edu/static/media/mvapich/mvapich2-2.3-userguide.html#x1-990006.26
SCEC (Dec ‘18) 20Network Based Computing Laboratory
MPI_Allreduce on KNL + Omni-Path (10,240 Processes)
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• For MPI_Allreduce latency with 32K bytes, MVAPICH2-OPT can reduce the latency by 2.4X
M. Bayatpour, S. Chakraborty, H. Subramoni, X. Lu, and D. K. Panda, Scalable Reduction Collectives with Data Partitioning-based
Multi-Leader Design, SuperComputing '17. Available since MVAPICH2-X 2.3b
SCEC (Dec ‘18) 21Network Based Computing Laboratory
Optimized CMA-based Collectives for Large Messages
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Intel MPI 2017
OpenMPI 2.1.0
Tuned CMA
• Significant improvement over existing implementation for Scatter/Gather with 1MB messages (up to 4x on KNL, 2x on Broadwell, 14x on OpenPower)
• New two-level algorithms for better scalability• Improved performance for other collectives (Bcast, Allgather, and Alltoall)
~ 2.5xBetter
~ 3.2xBetter
~ 4xBetter
~ 17xBetter
S. Chakraborty, H. Subramoni, and D. K. Panda, Contention Aware Kernel-Assisted MPI
Collectives for Multi/Many-core Systems, IEEE Cluster ’17, BEST Paper Finalist
Performance of MPI_Gather on KNL nodes (64PPN)
Available since MVAPICH2-X 2.3b
SCEC (Dec ‘18) 22Network Based Computing Laboratory
Shared Address Space (XPMEM)-based Collectives Design
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OSU_Allreduce (Broadwell 256 procs)
• “Shared Address Space”-based true zero-copy Reduction collective designs in MVAPICH2
• Offloaded computation/communication to peers ranks in reduction collective operation
• Up to 4X improvement for 4MB Reduce and up to 1.8X improvement for 4M AllReduce
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OSU_Reduce (Broadwell 256 procs)
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J. Hashmi, S. Chakraborty, M. Bayatpour, H. Subramoni, and D. Panda, Designing Efficient Shared Address Space Reduction
Collectives for Multi-/Many-cores, International Parallel & Distributed Processing Symposium (IPDPS '18), May 2018.Available in MVAPICH2-X 2.3rc1
SCEC (Dec ‘18) 23Network Based Computing Laboratory
Application-Level Benefits of XPMEM-Based Collectives
MiniAMR (Broadwell, ppn=16)
• Up to 20% benefits over IMPI for CNTK DNN training using AllReduce
• Up to 27% benefits over IMPI and up to 15% improvement over MVAPICH2 for MiniAMR application kernel
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CNTK AlexNet Training (Broadwell, B.S=default, iteration=50, ppn=28)
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SCEC (Dec ‘18) 24Network Based Computing Laboratory
Efficient Zero-copy MPI Datatypes for Emerging Architectures
• New designs for efficient zero-copy based MPI derived datatype processing
• Efficient schemes mitigate datatype translation, packing, and exchange overheads
• Demonstrated benefits over prevalent MPI libraries for various application kernels
• To be available in the upcoming MVAPICH2-X release!
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3D-Stencil Datatype Kernel on
Broadwell (2x14 core)
MILC Datatype Kernel on KNL 7250 in Flat-Quadrant Mode (64-core)
NAS-MG Datatype Kernel on OpenPOWER (20-core)
FALCON: Efficient Designs for Zero-copy MPI Datatype Processing on Emerging Architectures, J. Hashmi, S. Chakraborty, M. Bayatpour, H. Subramoni, D. K. (DK)
Panda, 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS ’19), May 2019.
SCEC (Dec ‘18) 25Network Based Computing Laboratory
Benefits of the New Asynchronous Progress Design: Broadwell + InfiniBand
Up to 44% performance improvement in P3DFFT application with 448 processesUp to 19% and 9% performance improvement in HPL application with 448 and 896 processes
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Lower is better Higher is better
A. Ruhela, H. Subramoni, S. Chakraborty, M. Bayatpour, P. Kousha, and D.K. Panda, Efficient Asynchronous Communication Progress for MPI without Dedicated Resources, EuroMPI 2018 Available in MVAPICH2-X 2.3rc1
SCEC (Dec ‘18) 26Network Based Computing Laboratory
• Scalability for million to billion processors
• Exploiting Accelerators (NVIDIA GPGPUs)
• Optimized MVAPICH2 for OpenPower (with/ NVLink) and ARM
• Application Scalability and Best Practices
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
SCEC (Dec ‘18) 27Network 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
SCEC (Dec ‘18) 28Network Based Computing Laboratory
CUDA-Aware MPI: MVAPICH2-GDR 1.8-2.3 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
SCEC (Dec ‘18) 29Network Based Computing Laboratory
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NVIDIA Volta V100 GPUMellanox Connect-X4 EDR HCA
CUDA 9.0Mellanox OFED 4.0 with GPU-Direct-RDMA
10x
9x
Optimized MVAPICH2-GDR Design
1.85us11X
SCEC (Dec ‘18) 30Network 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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SCEC (Dec ‘18) 31Network 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/
SCEC (Dec ‘18) 32Network Based Computing Laboratory
• Scalability for million to billion processors
• Exploiting Accelerators (NVIDIA GPGPUs)
• Optimized MVAPICH2 for OpenPower (with/ NVLink) and ARM
• Application Scalability and Best Practices
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
SCEC (Dec ‘18) 33Network Based Computing Laboratory
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SpectrumMPI-10.1.0.2
OpenMPI-3.0.0
Intra-node Point-to-Point Performance on OpenPower
Platform: Two nodes of OpenPOWER (Power8-ppc64le) CPU using Mellanox EDR (MT4115) HCA
Intra-Socket Small Message Latency Intra-Socket Large Message Latency
Intra-Socket Bi-directional BandwidthIntra-Socket Bandwidth
0.30us0
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80
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SpectrumMPI-10.1.0.2
OpenMPI-3.0.0
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MVAPICH2-2.3
SpectrumMPI-10.1.0.2
OpenMPI-3.0.0
SCEC (Dec ‘18) 34Network Based Computing Laboratory
0
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MVAPICH2-GDR: Performance on OpenPOWER (NVLink + Pascal)
010203040
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Platform: OpenPOWER (ppc64le) nodes equipped with a dual-socket CPU, 4 Pascal P100-SXM GPUs, and 4X-FDR InfiniBand Inter-connect
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Message Size (Bytes)
INTRA-NODE LATENCY (LARGE)
INTRA-SOCKET(NVLINK) INTER-SOCKET
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Intra-node Bandwidth: 33.2 GB/sec (NVLINK)Intra-node Latency: 13.8 us (without GPUDirectRDMA)
Inter-node Latency: 23 us (without GPUDirectRDMA) Inter-node Bandwidth: 6 GB/sec (FDR)Available since MVAPICH2-GDR 2.3a
SCEC (Dec ‘18) 35Network Based Computing Laboratory
0
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SpectrumMPI-10.1.0
OpenMPI-3.0.0
3X0
2000
4000
16K 32K 64K 128K 256K 512K 1M 2M
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Message Size
MVAPICH2-GDR-Next
SpectrumMPI-10.1.0
OpenMPI-3.0.0
34%
0
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2000
3000
4000
16K 32K 64K 128K 256K 512K 1M 2M
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MVAPICH2-GDR-Next
SpectrumMPI-10.1.0
OpenMPI-3.0.0
0
1000
2000
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(u
s)MVAPICH2-GDR-Next
SpectrumMPI-10.1.0
OpenMPI-3.0.0
Optimized All-Reduce with XPMEM on OpenPOWER(N
od
es=1
, PP
N=2
0)
Optimized Runtime Parameters: MV2_CPU_BINDING_POLICY=hybrid MV2_HYBRID_BINDING_POLICY=bunch
• Optimized MPI All-Reduce Design in MVAPICH2– Up to 2X performance improvement over Spectrum MPI and 4X over OpenMPI for intra-node
2X
(No
des
=2, P
PN
=20
)
4X48%
3.3X
2X
2X
SCEC (Dec ‘18) 36Network Based Computing Laboratory
0
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Intra-node Point-to-point Performance on ARM Cortex-A72
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Platform: ARM Cortex A72 (aarch64) processor with 64 cores dual-socket CPU. Each socket contains 32 cores.
Small Message Latency Large Message Latency
Bi-directional BandwidthBandwidth
0.27 micro-second
(1 bytes)
0
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800
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MVAPICH2-2.3
SCEC (Dec ‘18) 37Network Based Computing Laboratory
• Scalability for million to billion processors
• Exploiting Accelerators (NVIDIA GPGPUs)
• Optimized MVAPICH2 for OpenPower (with/ NVLink) and ARM
• Application Scalability and Best Practices
Overview of A Few Challenges being Addressed by the MVAPICH2 Project for Exascale
SCEC (Dec ‘18) 38Network Based Computing Laboratory
0
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160
MILC Leslie3D POP2 LAMMPS WRF2 LU
Exe
cuti
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Tim
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(s)
Intel MPI 18.1.163
MVAPICH2-X-2.3rc1
31%
SPEC MPI 2007 Benchmarks: Broadwell + InfiniBand
MVAPICH2-X outperforms Intel MPI by up to 31%
Configuration: 448 processes on 16 Intel E5-2680v4 (Broadwell) nodes having 28 PPN and interconnected
with 100Gbps Mellanox MT4115 EDR ConnectX-4 HCA
29% 5%
-12%1%
11%
SCEC (Dec ‘18) 39Network Based Computing Laboratory
Application Scalability on Skylake and KNL (Stamepede2)
MiniFE (1300x1300x1300 ~ 910 GB)
Runtime parameters: MV2_SMPI_LENGTH_QUEUE=524288 PSM2_MQ_RNDV_SHM_THRESH=128K PSM2_MQ_RNDV_HFI_THRESH=128K
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Exec
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)
No. of Processes (Skylake: 48ppn)
MVAPICH2
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48 96 192 384 768No. of Processes (Skylake: 48ppn)
MVAPICH2
NEURON (YuEtAl2012)
Courtesy: Mahidhar Tatineni @SDSC, Dong Ju (DJ) Choi@SDSC, and Samuel Khuvis@OSC ---- Testbed: TACC Stampede2 using MVAPICH2-2.3b
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No. of Processes (KNL: 64ppn)
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68 136 272 544 1088 2176 4352
No. of Processes (KNL: 68ppn)
MVAPICH2
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48 96 192 384 768 1536 3072No. of Processes (Skylake: 48ppn)
MVAPICH2
Cloverleaf (bm64) MPI+OpenMP, NUM_OMP_THREADS = 2
SCEC (Dec ‘18) 40Network Based Computing Laboratory
• MPI runtime has many parameters
• Tuning a set of parameters can help you to extract higher performance
• Compiled a list of such contributions through the MVAPICH Website– http://mvapich.cse.ohio-state.edu/best_practices/
• Initial list of applications– Amber
– HoomDBlue
– HPCG
– Lulesh
– MILC
– Neuron
– SMG2000
– Cloverleaf
– SPEC (LAMMPS, POP2, TERA_TF, WRF2)
• Soliciting additional contributions, send your results to mvapich-help at cse.ohio-state.edu.
• We will link these results with credits to you.
Applications-Level Tuning: Compilation of Best Practices
SCEC (Dec ‘18) 41Network Based Computing Laboratory
• Traditional HPC
– Message Passing Interface (MPI), including MPI + OpenMP
– Exploiting Accelerators
• Deep Learning
– Caffe, CNTK, TensorFlow, and many more
• Big Data/Enterprise/Commercial Computing
– Spark and Hadoop (HDFS, HBase, MapReduce)
– Deep Learning over Big Data (DLoBD)
• Cloud for HPC and BigData
– Virtualization with SR-IOV and Containers
HPC, Big Data, Deep Learning, and Cloud
SCEC (Dec ‘18) 42Network Based Computing Laboratory
• Deep Learning frameworks are a different game
altogether
– Unusually large message sizes (order of megabytes)
– Most communication based on GPU buffers
• Existing State-of-the-art
– cuDNN, cuBLAS, NCCL --> scale-up performance
– NCCL2, CUDA-Aware MPI --> scale-out performance
• For small and medium message sizes only!
• Proposed: Can we co-design the MPI runtime (MVAPICH2-
GDR) and the DL framework (Caffe) to achieve both?
– Efficient Overlap of Computation and Communication
– Efficient Large-Message Communication (Reductions)
– What application co-designs are needed to exploit
communication-runtime co-designs?
Deep Learning: New Challenges for MPI Runtimes
Scal
e-u
p P
erf
orm
ance
Scale-out PerformanceA. A. Awan, K. Hamidouche, J. M. Hashmi, and D. K. Panda, S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters. In Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '17)
cuDNN
gRPC
Hadoop
MPIMKL-DNN
DesiredNCCL1NCCL2
SCEC (Dec ‘18) 43Network Based Computing Laboratory
• MVAPICH2-GDR offers
excellent performance via
advanced designs for
MPI_Allreduce.
• Up to 11% better
performance on the RI2
cluster (16 GPUs)
• Near-ideal – 98% scaling
efficiency
Exploiting CUDA-Aware MPI for TensorFlow (Horovod)
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No. of GPUs
Horovod-MPI Horovod-NCCL2 Horovod-MPI-Opt (Proposed) Ideal
MVAPICH2-GDR 2.3 (MPI-Opt) is up to 11% faster than MVAPICH2
2.3 (Basic CUDA support)
A. A. Awan et al., “Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation”, Under Review, https://arxiv.org/abs/1810.11112
SCEC (Dec ‘18) 44Network Based Computing Laboratory
0
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6
33
55
443
2
67
10
886
4
13
42
177
28
26
84
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56
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MVAPICH2 BAIDU OPENMPI
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16
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53
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44
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MVAPICH2 BAIDU OPENMPI
• 16 GPUs (4 nodes) MVAPICH2-GDR vs. Baidu-Allreduce and OpenMPI 3.0
MVAPICH2-GDR: Allreduce Comparison with Baidu and OpenMPI
*Available since MVAPICH2-GDR 2.3a
~30X betterMV2 is ~2X better
than Baidu
~10X better OpenMPI is ~5X slower
than Baidu
~4X better
SCEC (Dec ‘18) 45Network Based Computing Laboratory
MVAPICH2-GDR vs. NCCL2 – Allreduce Operation
• Optimized designs in MVAPICH2-GDR 2.3 offer better/comparable performance for most cases
• MPI_Allreduce (MVAPICH2-GDR) vs. ncclAllreduce (NCCL2) on 16 GPUs
1
10
100
1000
10000
100000
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(u
s)
Message Size (Bytes)
MVAPICH2-GDR NCCL2
~1.2X better
Platform: Intel Xeon (Broadwell) nodes equipped with a dual-socket CPU, 1 K-80 GPUs, and EDR InfiniBand Inter-connect
1
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MVAPICH2-GDR NCCL2
~3X better
SCEC (Dec ‘18) 46Network Based Computing Laboratory
MVAPICH2-GDR vs. NCCL2 – Allreduce on DGX-2 (Preliminary Results)
• Optimized designs in upcoming MVAPICH2-GDR offer better/comparable performance for most cases
• MPI_Allreduce (MVAPICH2-GDR) vs. ncclAllreduce (NCCL2) on 1 DGX-2 node (16 Volta GPUs)
1
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100
1000
10000
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s)
Message Size (Bytes)
MVAPICH2-GDR-NEW NCCL-2.3
~1.7X better
Platform: Nvidia DGX-2 system (16 Nvidia Volta GPUs connected with NVSwitch), CUDA 9.2
0
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MVAPICH2-GDR-NEW NCCL-2.3
~2.5X better
SCEC (Dec ‘18) 47Network 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)
– Scale-out on 64 GPUs for training CIFAR-10 network on
CIFAR-10 dataset
– Scale-out on 128 GPUs for training GoogLeNet 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 publicly available from
http://hidl.cse.ohio-state.edu/
SCEC (Dec ‘18) 48Network Based Computing Laboratory
• High-Performance Design of TensorFlow over RDMA-enabled Interconnects
– High performance RDMA-enhanced design with native InfiniBand support at the verbs-level for gRPC and TensorFlow
– RDMA-based data communication
– Adaptive communication protocols
– Dynamic message chunking and accumulation
– Support for RDMA device selection
– Easily configurable for different protocols (native InfiniBand and IPoIB)
• Current release: 0.9.1
– Based on Google TensorFlow 1.3.0
– Tested with
• Mellanox InfiniBand adapters (e.g., EDR)
• NVIDIA GPGPU K80
• Tested with CUDA 8.0 and CUDNN 5.0
– http://hidl.cse.ohio-state.edu
RDMA-TensorFlow Distribution
SCEC (Dec ‘18) 49Network Based Computing Laboratory
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gRPPC (IPoIB-100Gbps)
Verbs (RDMA-100Gbps)
MPI (RDMA-100Gbps)
AR-gRPC (RDMA-100Gbps)
Performance Benefit for RDMA-TensorFlow (Inception3)
• TensorFlow Inception3 performance evaluation on an IB EDR cluster
– Up to 20% performance speedup over Default gRPC (IPoIB) for 8 GPUs
– Up to 34% performance speedup over Default gRPC (IPoIB) for 16 GPUs
– Up to 37% performance speedup over Default gRPC (IPoIB) for 24 GPUs
4 Nodes (8 GPUS) 8 Nodes (16 GPUS) 12 Nodes (24 GPUS)
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gRPPC (IPoIB-100Gbps)Verbs (RDMA-100Gbps)MPI (RDMA-100Gbps)AR-gRPC (RDMA-100Gbps)
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ages
/ S
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nd
Batch Size
gRPPC (IPoIB-100Gbps)
Verbs (RDMA-100Gbps)
MPI (RDMA-100Gbps)
AR-gRPC (RDMA-100Gbps)
SCEC (Dec ‘18) 50Network Based Computing Laboratory
• Traditional HPC
– Message Passing Interface (MPI), including MPI + OpenMP
– Exploiting Accelerators
• Deep Learning
– Caffe, CNTK, TensorFlow, and many more
• Big Data/Enterprise/Commercial Computing
– Spark and Hadoop (HDFS, HBase, MapReduce)
– Deep Learning over Big Data (DLoBD)
• Cloud for HPC and BigData
– Virtualization with SR-IOV and Containers
HPC, Big Data, Deep Learning, and Cloud
SCEC (Dec ‘18) 51Network Based Computing Laboratory
Designing Communication and I/O Libraries for Big Data Systems: Challenges
Big Data Middleware(HDFS, MapReduce, HBase, Spark and Memcached)
Networking Technologies
(InfiniBand, 1/10/40/100 GigE and Intelligent NICs)
Storage Technologies(HDD, SSD, NVM, and NVMe-
SSD)
Programming Models(Sockets)
Applications
Commodity Computing System Architectures
(Multi- and Many-core architectures and accelerators)
RDMA?
Communication and I/O Library
Point-to-PointCommunication
QoS & Fault Tolerance
Threaded Modelsand Synchronization
Performance TuningI/O and File Systems
Virtualization (SR-IOV)
Benchmarks
Upper level
Changes?
SCEC (Dec ‘18) 52Network Based Computing Laboratory
• RDMA for Apache Spark
• RDMA for Apache Hadoop 2.x (RDMA-Hadoop-2.x)
– Plugins for Apache, Hortonworks (HDP) and Cloudera (CDH) Hadoop distributions
• RDMA for Apache HBase
• RDMA for Memcached (RDMA-Memcached)
• RDMA for Apache Hadoop 1.x (RDMA-Hadoop)
• OSU HiBD-Benchmarks (OHB)
– HDFS, Memcached, HBase, and Spark Micro-benchmarks
• http://hibd.cse.ohio-state.edu
• Users Base: 290 organizations from 34 countries
• More than 28,500 downloads from the project site
The High-Performance Big Data (HiBD) Project
Available for InfiniBand and RoCE
Also run on Ethernet
Available for x86 and OpenPOWER
Support for Singularity and Docker
SCEC (Dec ‘18) 53Network Based Computing Laboratory
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IPoIB (EDR)OSU-IB (EDR)
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Performance Numbers of RDMA for Apache Hadoop 2.x –RandomWriter & TeraGen in OSU-RI2 (EDR)
Cluster with 8 Nodes with a total of 64 maps
• RandomWriter
– 3x improvement over IPoIB
for 80-160 GB file size
• TeraGen
– 4x improvement over IPoIB for
80-240 GB file size
RandomWriter TeraGen
Reduced by 3x Reduced by 4x
SCEC (Dec ‘18) 54Network Based Computing Laboratory
• InfiniBand FDR, SSD, 32/64 Worker Nodes, 768/1536 Cores, (768/1536M 768/1536R)
• RDMA vs. IPoIB with 768/1536 concurrent tasks, single SSD per node.
– 32 nodes/768 cores: Total time reduced by 37% over IPoIB (56Gbps)
– 64 nodes/1536 cores: Total time reduced by 43% over IPoIB (56Gbps)
Performance Evaluation of RDMA-Spark on SDSC Comet – HiBench PageRank
32 Worker Nodes, 768 cores, PageRank Total Time 64 Worker Nodes, 1536 cores, PageRank Total Time
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Tim
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IPoIB
RDMA
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Huge BigData Gigantic
Tim
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ec)
Data Size (GB)
IPoIB
RDMA
43%37%
SCEC (Dec ‘18) 55Network Based Computing Laboratory
Using HiBD Packages on Existing HPC Infrastructure
SCEC (Dec ‘18) 56Network Based Computing Laboratory
Using HiBD Packages on Existing HPC Infrastructure
SCEC (Dec ‘18) 57Network Based Computing Laboratory
(1) Prepare Datasets @Scale
(2) Deep Learning @Scale
(3) Non-deep learning
analytics @Scale
(4) Apply ML model @Scale
• Deep Learning over Big Data (DLoBD) is one of the most efficient analyzing paradigms
• More and more deep learning tools or libraries (e.g., Caffe, TensorFlow) start running over big
data stacks, such as Apache Hadoop and Spark
• Benefits of the DLoBD approach
– Easily build a powerful data analytics pipeline
• E.g., Flickr DL/ML Pipeline, “How Deep Learning Powers Flickr”, http://bit.ly/1KIDfof
– Better data locality
– Efficient resource sharing and cost effective
Deep Learning over Big Data (DLoBD)
SCEC (Dec ‘18) 58Network Based Computing Laboratory
X. Lu, H. Shi, M. H. Javed, R. Biswas, and D. K. Panda, Characterizing Deep Learning over Big Data (DLoBD) Stacks on RDMA-capable Networks, HotI 2017.
High-Performance Deep Learning over Big Data (DLoBD) Stacks
• Benefits of Deep Learning over Big Data (DLoBD)▪ Easily integrate deep learning components into Big Data
processing workflow▪ Easily access the stored data in Big Data systems▪ No need to set up new dedicated deep learning clusters;
Reuse existing big data analytics clusters
• Challenges▪ Can RDMA-based designs in DLoBD stacks improve
performance, scalability, and resource utilization on high-performance interconnects, GPUs, and multi-core CPUs?
▪ What are the performance characteristics of representative DLoBD stacks on RDMA networks?
• Characterization on DLoBD Stacks▪ CaffeOnSpark, TensorFlowOnSpark, and BigDL▪ IPoIB vs. RDMA; In-band communication vs. Out-of-band
communication; CPU vs. GPU; etc.▪ Performance, accuracy, scalability, and resource utilization ▪ RDMA-based DLoBD stacks (e.g., BigDL over RDMA-Spark)
can achieve 2.6x speedup compared to the IPoIB based scheme, while maintain similar accuracy
0
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ura
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ime
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2.6X
SCEC (Dec ‘18) 59Network Based Computing Laboratory
• Traditional HPC
– Message Passing Interface (MPI), including MPI + OpenMP
– Exploiting Accelerators
• Deep Learning
– Caffe, CNTK, TensorFlow, and many more
• Big Data/Enterprise/Commercial Computing
– Spark and Hadoop (HDFS, HBase, MapReduce)
– Deep Learning over Big Data (DLoBD)
• Cloud for HPC and BigData
– Virtualization with SR-IOV and Containers
HPC, Big Data, Deep Learning, and Cloud
SCEC (Dec ‘18) 60Network 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 2.2 supports:– OpenStack, Docker, and singularity
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 Library 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
SCEC (Dec ‘18) 61Network Based Computing Laboratory
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milc leslie3d pop2 GAPgeofem zeusmp2 lu
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MV2-Native2%
• 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%
A Release for Azure Coming Soon
SCEC (Dec ‘18) 62Network Based Computing Laboratory
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NPB Class D
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• 512 Processes across 32 nodes
• Less than 7% and 6% overhead for NPB and Graph500, respectively
Application-Level Performance on Singularity with MVAPICH2
7%
6%
J. Zhang, X .Lu and D. K. Panda, Is Singularity-based Container Technology Ready for Running MPI Applications on HPC Clouds?,
UCC ’17, Best Student Paper Award
SCEC (Dec ‘18) 63Network Based Computing Laboratory
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MVAPICH2-GDR-2.3aIntel Haswell (E5-2687W @ 3.10 GHz) node - 20 cores
NVIDIA Volta V100 GPUMellanox Connect-X4 EDR HCA
CUDA 9.0Mellanox OFED 4.0 with GPU-Direct-RDMA
MVAPICH2-GDR on Container with Negligible Overhead
Works with NVIDIA HPC Container Makerhttps://github.com/NVIDIA/hpc-container-maker/blob/master/recipes/hpcbase-pgi-mvapich2.py
SCEC (Dec ‘18) 64Network Based Computing Laboratory
• Supported through X-ScaleSolutions (http://x-scalesolutions.com)
• Benefits:
– Help and guidance with installation of the library
– Platform-specific optimizations and tuning
– Timely support for operational issues encountered with the library
– Web portal interface to submit issues and tracking their progress
– Advanced debugging techniques
– Application-specific optimizations and tuning
– Obtaining guidelines on best practices
– Periodic information on major fixes and updates
– Information on major releases
– Help with upgrading to the latest release
– Flexible Service Level Agreements
• Support provided to Lawrence Livermore National Laboratory (LLNL) this year
Commercial Support for MVAPICH2, HiBD, and HiDL Libraries
SCEC (Dec ‘18) 65Network Based Computing Laboratory
• Looking for Bright and Enthusiastic Personnel to join as
– Post-Doctoral Researchers
– PhD Students
– MPI Programmer/Software Engineer
– Deep Learning/Big Data Programmer/Software Engineer
• If interested, please contact me at this conference and/or send an e-mail
Multiple Positions Available in My Group
SCEC (Dec ‘18) 66Network Based Computing Laboratory
Funding Acknowledgments
Funding Support by
Equipment Support by
SCEC (Dec ‘18) 67Network Based Computing Laboratory
Personnel AcknowledgmentsCurrent Students (Graduate)
– A. Awan (Ph.D.)
– M. Bayatpour (Ph.D.)
– S. Chakraborthy (Ph.D.)
– C.-H. Chu (Ph.D.)
– S. Guganani (Ph.D.)
Past Students
– A. Augustine (M.S.)
– P. Balaji (Ph.D.)
– R. Biswas (M.S.)
– 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.)
– J. Zhang (Ph.D.)
Past Research Scientist
– K. Hamidouche
– 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.)
– M. Li (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.)
– J. Hashmi (Ph.D.)
– H. Javed (Ph.D.)
– P. Kousha (Ph.D.)
– D. Shankar (Ph.D.)
– H. Shi (Ph.D.)
– J. Lin
– M. Luo
– E. Mancini
Current Research Scientists
– X. Lu
– H. Subramoni
Past Programmers
– D. Bureddy
– J. Perkins
Current Research Specialist
– J. Smith
– S. Marcarelli
– J. Vienne
– H. Wang
Current Post-doc
– A. Ruhela
– K. Manian
Current Students (Undergraduate)
– V. Gangal (B.S.)
– M. Haupt (B.S.)
– N. Sarkauskas (B.S.)
– A. Yeretzian (B.S.)
Past Research Specialist
– M. Arnold
SCEC (Dec ‘18) 68Network Based Computing Laboratory
Thank You!
Network-Based Computing Laboratoryhttp://nowlab.cse.ohio-state.edu/
The High-Performance MPI/PGAS Projecthttp://mvapich.cse.ohio-state.edu/
The High-Performance Deep Learning Projecthttp://hidl.cse.ohio-state.edu/
The High-Performance Big Data Projecthttp://hibd.cse.ohio-state.edu/