Software Libraries and Middleware for Exascale Systems · (Hadoop, Spark, HBase, Memcached, etc.) Deep Learning (Caffe, TensorFlow, BigDL, etc.) HPC (MPI, RDMA, Lustre, etc.) Increasing

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

http://www.cse.ohio-state.edu/~panda

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!!


Can We Run Big Data and Deep Learning Jobs on Existing HPC Infrastructure?

Physical Compute







Spark Job

Hadoop Job Deep LearningJob


• 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


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


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


• 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


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

http://mvapich.cse.ohio-state.edu/


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MVAPICH2 Release Timeline and Downloads


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


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


• 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


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

Omni-Path - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with Omni-Path switch

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Omni-Path - 3.1 GHz Deca-core (Haswell) Intel PCI Gen3 with Omni-Path switch

Bandwidth: MPI over IB with MVAPICH2

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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


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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

http://mvapich.cse.ohio-state.edu/static/media/mvapich/mvapich2-2.3-userguide.html#x1-990006.26


MPI_Allreduce on KNL + Omni-Path (10,240 Processes)

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OSU Micro Benchmark 64 PPN

2.4X

• 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


Optimized CMA-based Collectives for Large Messages

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Intel MPI 2017

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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


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

73.2

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OSU_Reduce (Broadwell 256 procs)

4X

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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


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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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.


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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Intel MPI 18.1.163

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Memory Consumption = 69%

PPN=28

P3DFFT High Performance Linpack (HPL)

44%

33%

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

mailto:[email protected]







• Scalability for million to billion processors






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


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


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MVAPICH2-GDR-2.3Intel 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

10x

9x

Optimized MVAPICH2-GDR Design

1.85us11X


• 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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Application-Level Evaluation (Cosmo) and Weather Forecasting in Switzerland

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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/


http://www2.cosmo-model.org/content









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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

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INTRA-NODE LATENCY (SMALL)

INTRA-SOCKET(NVLINK) INTER-SOCKET

MVAPICH2-GDR: Performance on OpenPOWER (NVLink + Pascal)

010203040

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INTRA-NODE BANDWIDTH


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)


INTER-NODE BANDWIDTH

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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INTER-NODE LATENCY (LARGE)

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


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MVAPICH2-GDR-Next

SpectrumMPI-10.1.0

OpenMPI-3.0.0

3X0

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Message Size

MVAPICH2-GDR-Next

SpectrumMPI-10.1.0

OpenMPI-3.0.0

34%

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OpenMPI-3.0.0

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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


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MVAPICH2-2.3

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)

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MVAPICH2-2.3








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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%


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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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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68 136 272 544 1088 2176 4352


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MVAPICH2

Cloverleaf (bm64) MPI+OpenMP, NUM_OMP_THREADS = 2


• 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

http://mvapich.cse.ohio-state.edu/best_practices/


• Traditional HPC



• Deep Learning









• 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


• 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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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

https://arxiv.org/abs/1810.11112


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• 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


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

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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


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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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


• 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/



• 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



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d

Batch Size

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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



4 Nodes (8 GPUS) 8 Nodes (16 GPUS) 12 Nodes (24 GPUS)

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Verbs (RDMA-100Gbps)

MPI (RDMA-100Gbps)

AR-gRPC (RDMA-100Gbps)


• Traditional HPC



• Deep Learning









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?


• 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

http://hibd.cse.ohio-state.edu/


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IPoIB (EDR)OSU-IB (EDR)

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


• 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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RDMA

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Tim

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ec)

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IPoIB

RDMA

43%37%


Using HiBD Packages on Existing HPC Infrastructure


Using HiBD Packages on Existing HPC Infrastructure


(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)


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

20

40

60

10

1010

2010

3010

4010

5010

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Acc

ura

cy (

%)

Ep

och

s T

ime

(sec

s)

Epoch Number

IPoIB-TimeRDMA-TimeIPoIB-AccuracyRDMA-Accuracy

2.6X


• Traditional HPC



• Deep Learning









• 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


0

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milc leslie3d pop2 GAPgeofem zeusmp2 lu

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Tim

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s)

MV2-SR-IOV-Def

MV2-SR-IOV-Opt

MV2-Native

1%9.5%

0

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4000

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6000

22,20 24,10 24,16 24,20 26,10 26,16

Exe

cuti

on

Tim

e (

ms)

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MV2-SR-IOV-Def

MV2-SR-IOV-Opt

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


0

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2000

2500

3000

22,16 22,20 24,16 24,20 26,16 26,20

BFS

Exe

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on

Tim

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ms)

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Graph500

Singularity

Native

0

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Tim

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s)

NPB Class D

Singularity

Native

• 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


0

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)


GPU-GPU Inter-node Bi-Bandwidth

Docker Native

02000400060008000

1000012000

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idth

(M

B/s

)


GPU-GPU Inter-node Bandwidth

Docker Native

1

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GPU-GPU Inter-node Latency

Docker Native

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

https://github.com/NVIDIA/hpc-container-maker/blob/master/recipes/hpcbase-pgi-mvapich2.py


• 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

http://x-scalesolutions.com/


• 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

to [email protected]

Multiple Positions Available in My Group


Funding Acknowledgments

Funding Support by

Equipment Support by


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


Thank You!

Network-Based Computing Laboratoryhttp://nowlab.cse.ohio-state.edu/

[email protected]

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/

http://nowlab.cse.ohio-state.edu/


Software Libraries and Middleware for Exascale Systems · (Hadoop, Spark, HBase, Memcached, etc.) Deep Learning (Caffe, TensorFlow, BigDL, etc.) HPC (MPI, RDMA, Lustre, etc.) Increasing

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