how to achieve high-performance, scalable and distributed ...€¦ · network based computing...

How to Achieve High-Performance, Scalable and Distributed Training on Modern HPC Systems?

Dhabaleswar K. (DK) PandaThe Ohio State University

E-mail: [email protected]://www.cse.ohio-state.edu/~panda

Talk at HPCAC-AI Stanford Conference (April ’20)

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HPCAC-AI-Stanford (April ‘20) 2Network Based Computing Laboratory

Understanding the Deep Learning Resurgence

Adopted from: http://www.deeplearningbook.org/contents/intro.html

• Deep Learning (DL) is a sub-set of Machine Learning (ML)

– Perhaps, the most revolutionary subset!

– Feature extraction vs. hand-crafted features

• Deep Learning– A renewed interest and a lot of hype!

– Key success: Deep Neural Networks (DNNs)

– Everything was there since the late 80s except the “computability of DNNs”

AI

Machine Learning

Deep Learning

Examples:

MLPs, DNNs,

Examples:

Logistic Regression

http://www.deeplearningbook.org/contents/intro.html

mailto:[email protected]



• Modern and efficient hardware enabled

– Computability of DNNs – impossible in the past!

– GPUs – at the core of DNN training

– CPUs – catching up fast

• Availability of Datasets

– MNIST, CIFAR10, ImageNet, and more…

• Excellent Accuracy for many application areas

– Vision, Machine Translation, and several others...

Deep Learning in the Many-core Era

Courtesy: A. Canziani et al., “An Analysis of Deep Neural Network Models for Practical Applications”, CoRR, 2016.

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Big Data (Hadoop, Spark,

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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!!


Drivers of Modern HPC Cluster Architectures

• 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)

• Available on HPC Clouds, e.g., Amazon EC2, NSF Chameleon, Microsoft Azure, etc.

Accelerators / Coprocessors high compute density, high

performance/watt>1 TFlop DP on a chip

High Performance Interconnects -InfiniBand

<1usec latency, 200Gbps Bandwidth>Multi-core Processors SSD, NVMe-SSD, NVRAM

K - ComputerSunway TaihuLightSummit Sierra


• Deep Learning has two major tasks1. Training of the Deep Neural Network

2. Inference (or deployment) that uses a trained DNN

• DNN Training– Training is a compute/communication intensive process – can take days to

weeks

– Faster training is necessary!

• Faster training can be achieved by– Using Newer and Faster Hardware – But, there is a limit!

– Can we use more GPUs or nodes?• The need for Parallel and Distributed Training

Key Phases of Deep Learning


• Scale-up: Intra-node Communication

– Many improvements like:• NVIDIA cuDNN, cuBLAS, NCCL, etc.

• CUDA 9 Co-operative Groups

• Scale-out: Inter-node Communication

– DL Frameworks – most are optimized for single-node only

– Distributed (Parallel) Training is an emerging trend

• OSU-Caffe – MPI-based

• Microsoft CNTK – MPI/NCCL2

• Google TensorFlow – gRPC-based/MPI/NCCL2

• Facebook Caffe2 – Hybrid (NCCL2/Gloo/MPI)

Scale-up and Scale-out

Scal

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Scale-out Performance

cuDNN

gRPC

Hadoop

MPIMKL-DNN

Desired

NCCL2


Holistic Evaluation is Important!!

• My framework is faster than your framework!

• This needs to be understood in a holistic way.

• Performance depends on the entire execution environment (the full stack)

• Isolated view of performance is not helpful

A. A. Awan, H. Subramoni, and Dhabaleswar K. Panda. “An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures”, In Proceedings of the Machine Learning on HPC Environments (MLHPC'17). ACM, New York, NY, USA, Article 8.

MKL/MKL-DNN


How to efficiently scale-out a

Deep Learning (DL) framework and take advantage of heterogeneous

High Performance Computing (HPC) resources?

Broad Challenge: Exploiting HPC for Deep Learning


1. What are the fundamental issues in designing DL frameworks?

– Memory Requirements

– ComputationRequirements

– Communication Overhead

2. Why do we need to support distributed training?

– To overcome the limits of single-node training

– To better utilize hundreds of existing HPC Clusters

Research Challenges to Exploit HPC Technologies

InfiniBand GPUCPU

CNTK

Gradient AggregationModel Propagation Forward

Backward

Deep Learning and Machine Learning Frameworks

Caffe/OSU-Caffe Caffe2 TensorFlow MXNet

Communication Runtimes to support Distributed Training

HPC Platforms

Major Computation and Communication Phases in DL Frameworks

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2


3. What are the new design challenges brought forward by DL frameworks for Communication runtimes?

– Large Message CollectiveCommunication and Reductions

– GPU Buffers (CUDA-Awareness)

4. Can a Co-design approach help in achieving Scale-up and Scale-out efficiently?

– Co-Design the support at Runtime level and Exploit it at the DL Framework level

– What performance benefits can be observed?

– What needs to be fixed at the communication runtime layer?

Research Challenges to Exploit HPC Technologies (Cont’d)

CUDA-Awareness

InfiniBand GPUCPU

Large-message Collectives

CNTK

Point-to-Point

Operations

Gradient AggregationModel Propagation Forward

Backward

Deep Learning and Machine Learning Frameworks

Caffe/OSU-Caffe Caffe2 TensorFlow MXNet

Communication Runtimes (MPI/NCCL/Gloo/MLSL)

HPC Platforms

Major Computation and Communication Phases in DL Frameworks

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4 Co-Design Opportunities


• MPI-driven Deep Learning– CPU-based Deep Learning

– GPU-based Deep Learning

• Co-designing Deep Learning Stacks with High-Performance MPI

• Out-of-core DNN training

• Exploiting Hybrid (Data and Model) Parallelism

• Use-Case: AI-Driven Digital Pathology

Multiple Approaches taken up by OSU


Data Parallel Deep Learning and MPI Collectives

MPI_Bcast (GPU 0)

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ApplyUpdates

MPI_Reduce (GPU 0)

Loop {}• Major MPI Collectives

involved in Designing distributed frameworks

• MPI_Bcast – required for DNN parameter exchange

• MPI_Reduce – needed for gradient accumulation from multiple solvers

• MPI_Allreduce – use just one Allreduce instead of Reduce and Broadcast

A. 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)


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 3,075 organizations in 89 countries

– More than 732,000 (> 0.7 million) downloads from the OSU site directly

– Empowering many TOP500 clusters (November ‘19 ranking)

• 3rd ranked 10,649,640-core cluster (Sunway TaihuLight) at NSC, Wuxi, China

• 5th, 448, 448 cores (Frontera) at TACC

• 8th, 391,680 cores (ABCI) in Japan

• 14th, 570,020 cores (Nurion) in South Korea and many others

– Available with software stacks of many vendors and Linux Distros (RedHat, SuSE, OpenHPC, and Spack)

– http://mvapich.cse.ohio-state.edu

• Empowering Top500 systems for over a decade Partner in the 5th ranked TACC Frontera System

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


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MVAPICH2 Release Timeline and Downloads


Architecture of MVAPICH2 Software Family (HPC and DL)

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 andFile Systems

FaultTolerance

Virtualization Active Messages

Job StartupIntrospection

& Analysis

Support for Modern Networking Technology(InfiniBand, iWARP, RoCE, Omni-Path, Elastic Fabric Adapter)

Support for Modern Multi-/Many-core Architectures(Intel-Xeon, OpenPOWER, Xeon-Phi, ARM, NVIDIA GPGPU)

Transport Protocols Modern Features

RC SRD UD DC UMR ODPSR-IOV

Multi Rail

Transport MechanismsShared

MemoryCMA IVSHMEM

Modern Features

Optane* NVLink CAPI*

* Upcoming

XPMEM


• gRPC– Officially available and supported

– Open-source – can be enhanced by others

– Accelerated gRPC (add RDMA to gRPC)

• gRPC+X– Use gRPC for bootstrap and rendezvous

– Actual communication is in “X”

– XMPI, Verbs, GPUDirect RDMA (GDR), etc.

• No-gRPC– Baidu – the first one to use MPI Collectives for TF

– Horovod – Use NCCL, or MPI, or any other future library (e.g. IBM DDL support recently added)

Data Parallel Training with TensorFlow

*Awan et al., “Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation ”, CCGrid ’19.


MVAPICH2 (MPI)-driven Infrastructure for ML/DL Training

MVAPICH2-X for CPU-Based Training

MVAPICH2-GDR for GPU-Based Training

Horovod

TensorFlow PyTorch MXNet

ML/DL Applications


MVAPICH2 Software Family (CPU-Based Deep Learning)High-Performance Parallel Programming Libraries

MVAPICH2 Support for InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE

MVAPICH2-X Advanced MPI features, OSU INAM, PGAS (OpenSHMEM, UPC, UPC++, and CAF), and MPI+PGAS programming models with unified communication runtime

MVAPICH2-GDR Optimized MPI for clusters with NVIDIA GPUs and for GPU-enabled Deep Learning Applications

MVAPICH2-Virt High-performance and scalable MPI for hypervisor and container based HPC cloud

MVAPICH2-EA Energy aware and High-performance MPI

MVAPICH2-MIC Optimized MPI for clusters with Intel KNC

Microbenchmarks

OMB Microbenchmarks suite to evaluate MPI and PGAS (OpenSHMEM, UPC, and UPC++) libraries for CPUs and GPUs

Tools

OSU INAM Network monitoring, profiling, and analysis for clusters with MPI and scheduler integration

OEMT Utility to measure the energy consumption of MPI applications


Performance of CNTK with MVAPICH2-X on CPU-based Deep Learning

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20%

9%

• CPU-based training of AlexNet neural network using ImageNet ILSVRC2012 dataset

• Advanced XPMEM-based designs show up to 20% benefits over Intel MPI (IMPI) for CNTK DNN training using All_Reduce

• The proposed designs show good scalability with increasing system size

Designing Efficient Shared Address Space Reduction Collectives for Multi-/Many-cores, J. Hashmi, S. Chakraborty, M. Bayatpour, H. Subramoni, and DK Panda, 32nd IEEE International Parallel & Distributed Processing Symposium (IPDPS '18), May 2018

Available since MVAPICH2-X 2.3rc1 release


Distributed TensorFlow on TACC Frontera (2,048 CPU nodes)• Scaled TensorFlow to 2048 nodes on

Frontera using MVAPICH2 and IntelMPI

• MVAPICH2 and IntelMPI give similar performance for DNN training

• Report a peak of 260,000 images/sec on 2,048 nodes

• On 2048 nodes, ResNet-50 can be trained in 7 minutes!

A. Jain, A. A. Awan, H. Subramoni, DK Panda, “Scaling TensorFlow, PyTorch, and MXNet using MVAPICH2 for High-Performance Deep Learning on Frontera”, DLS ’19 (SC ’19 Workshop).


Scaling DL Frameworks using MVAPICH2-X on TACC Frontera

• On single node, TensorFlow (TF) is 8% better than MXNet

• TF (tf_cnn_benchmark) is 2.13x better than PyTorch

• TensorFlow is 1.7x better than MXNet

• TF (Keras) gives better performance compared to PyTorch and MXNet.

A. Jain, A. A. Awan, H. Subramoni, DK Panda, “Scaling TensorFlow, PyTorch, and MXNet using MVAPICH2 for High-Performance Deep Learning on Frontera”, DLS ’19 (SC ’19 Workshop).


MVAPICH2 Software Family (GPU-Based Deep Learning) High-Performance Parallel Programming Libraries

MVAPICH2 Support for InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE

MVAPICH2-X Advanced MPI features, OSU INAM, PGAS (OpenSHMEM, UPC, UPC++, and CAF), and MPI+PGAS programming models with unified communication runtime

MVAPICH2-GDR Optimized MPI for clusters with NVIDIA GPUs and for GPU-enabled Deep Learning Applications

MVAPICH2-Virt High-performance and scalable MPI for hypervisor and container based HPC cloud

MVAPICH2-EA Energy aware and High-performance MPI

MVAPICH2-MIC Optimized MPI for clusters with Intel KNC

Microbenchmarks

OMB Microbenchmarks suite to evaluate MPI and PGAS (OpenSHMEM, UPC, and UPC++) libraries for CPUs and GPUs

Tools

OSU INAM Network monitoring, profiling, and analysis for clusters with MPI and scheduler integration

OEMT Utility to measure the energy consumption of MPI applications


At Sender:

At Receiver:MPI_Recv(r_devbuf, size, …);

insideMVAPICH2

• 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 1.8-2.3.3 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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MV2-(NO-GDR) MV2-GDR 2.3

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


MVAPICH2-GDR vs. NCCL2 – Allreduce Operation (DGX-2)• Optimized designs in MVAPICH2-GDR offer better/comparable performance for most cases

• MPI_Allreduce (MVAPICH2-GDR) vs. ncclAllreduce (NCCL2) on 1 DGX-2 node (16 Volta GPUs)

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Platform: Nvidia DGX-2 system (16 Nvidia Volta GPUs connected with NVSwitch), CUDA 9.2

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~4.7X better

C.-H. Chu, P. Kousha, A. Awan, K. S. Khorassani, H. Subramoni and D. K. Panda, "NV-Group: Link-Efficient Reductions for Distributed Deep Learning on Modern Dense GPU Systems, " ICS-2020, Accepted to be Presented.


MVAPICH2-GDR: Enhanced MPI_Allreduce at Scale• Optimized designs in MVAPICH2-GDR offer better performance for most cases

• MPI_Allreduce (MVAPICH2-GDR) vs. ncclAllreduce (NCCL2) up to 1,536 GPUs

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MVAPICH2-GDR-2.3.3 NCCL 2.5

1.6X better

Platform: Dual-socket IBM POWER9 CPU, 6 NVIDIA Volta V100 GPUs, and 2-port InfiniBand EDR Interconnect

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NCCL 2.5 MVAPICH2-GDR-2.3.3

1.7X better

C.-H. Chu, P. Kousha, A. Awan, K. S. Khorassani, H. Subramoni and D. K. Panda, "NV-Group: Link-Efficient Reductions for Distributed Deep Learning on Modern Dense GPU Systems, " ICS-2020. Accepted to be Presented.


Scalable TensorFlow using Horovod and MVAPICH2-GDR• ResNet-50 Training using TensorFlow benchmark on 1 DGX-2 node (16 Volta GPUs)

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NCCL-2.5 MVAPICH2-GDR-2.3.3

9% higher

Platform: Nvidia DGX-2 system, CUDA 9.2

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Ideal throughput at scale× 100%

C.-H. Chu, P. Kousha, A. Awan, K. S. Khorassani, H. Subramoni and D. K. Panda, "NV-Group: Link-Efficient Reductions for Distributed Deep Learning on Modern Dense GPU Systems, " ICS-2020. Accepted to be Presented.


Distributed TensorFlow on ORNL Summit (1,536 GPUs)• ResNet-50 Training using

TensorFlow benchmark on SUMMIT -- 1536 Volta GPUs!

• 1,281,167 (1.2 mil.) images

• Time/epoch = 3.6 seconds

• Total Time (90 epochs) = 3.6 x 90 = 332 seconds = 5.5 minutes!

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Platform: The Summit Supercomputer (#1 on Top500.org) – 6 NVIDIA Volta GPUs per node connected with NVLink, CUDA 9.2

*We observed errors for NCCL2 beyond 96 GPUs

MVAPICH2-GDR reaching ~0.35 million images per second for ImageNet-1k!

ImageNet-1k has 1.2 million images


• LLNL/Lassen POWER9 CPU with 4 V100 GPUs/node

• PyTorch is becoming a very important DL framework

• Scaling PyTorch models with Horovod is simple

• MVAPICH2-GDR provides better performance and scalability compared to NCCL2

Scaling PyTorch on LLNL/Lassen using MVAPICH2-GDR

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Platform: The Lassen Supercomputer (#10 on Top500.org) – 4 NVIDIA Volta GPUs per node connected with NVLink, CUDA 10.1


• RTX 5000 are NVIDIA’s GPUs targeted for data centers

• Different from GTX series– Supports GPUDirect RDMA (GDR)

– Supports GDRCOPY

• MVAPICH2-GDR offers good performance and reasonable scaling

• Scaling is not as good as Lassen because– Nodes are connected with IB FDR

– No NVLink between GPUs (only PCIe)

Early Exploration of MXNet using MVAPICH2-GDR on TACC Frontera RTX GPU Nodes

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Platform: TACC Frontera-RTX – 4 NVIDIA RTX 5000 GPUs per node, CUDA 10.1


• TACC Longhorn is like the LLNL/Lassen system (POWER9 with 4 V100 GPUs/node)

• Some restrictions to keep in mind

– TensorFlow 2.1 on POWER9 only built with CUDA 10.2

• Early results are promising– Good scaling up to 256 GPUs

Early Exploration of TensorFlow 2.1 on TACC/Longhorn using MVAPICH2-GDR

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Platform: The Longhorn System at TACC – 4 NVIDIA Volta GPUs per node connected with NVLink, CUDA 10.2


• MPI-driven Deep Learning– CPU-based Deep Learning

– GPU-based Deep Learning

• Co-designing Deep Learning Stacks with High-Performance MPI

• Out-of-core DNN training

• Exploiting Hybrid (Data and Model) Parallelism

• Use-Case: AI-Driven Digital Pathology

Multiple Approaches taken up by OSU


• What are the fundamental bottlenecks in the existing DL frameworks?

• How to design a Scalable and Distributed Framework?

– Achieve both Scale-up and Scale-out efficiently

• What are the new requirements and expectations for Communication Runtimes?

• Can a Co-design approach help in achieving better training performance?

– Can a DL framework like Caffe be co-designed with an MPI runtime?

• What is the impact of the co-design?

– What performance benefits can be observed? At what levels?

S-Caffe: Co-designing HPC and DL

A. A. Awan, K. Hamidouche, J.M. Hashmi, DK Panda, “S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters”, ACM PPoPP ’17


• 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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Invalid use caseOSU-Caffe publicly available from

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

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


Out-of-core Training via CUDA Unified Memory• Large DNNs cannot be trained on GPUs due to memory limitation!

– ResNet-50 -- current frameworks only allow small batch sizes

– Next-generation models like Neural Machine Translation (NMT), AmoebaNet, etc.

• Ridiculously large consists of billions of parameters

– Can we design Out-of-core DNN training support using new software features in CUDA 8/9 and hardware mechanisms in Pascal/Volta GPUs?

• General intuition is that managed allocations “will be” slow!

– The proposed OC-Caffe (Out-of-Core Caffe) exploits the potential of CUDA Unified Memory.

• OC-Caffe-Opt: up to 80% better than Intel-optimized CPU Caffe for ResNet-50 training on the Volta V100 GPU with CUDA9 and CUDNN7

A. Awan et al., OC-DNN: Exploiting Advanced Unified Memory Capabilities in CUDA 9 and Volta GPUs for Out-of-Core DNN Training, HiPC ’18


• Data-Parallelism– only for models that fit the memory

• Out-of-core models– Deeper model Better accuracy but more memory required!

• Model parallelism can work for out-of-core models!

• Key Challenges– Model Partitioning is difficult for application

programmers

– Finding the right partition (grain) size is hard

– cut at which layer and why?

– Developing a practical system for model-parallelism• Redesign DL Framework or create additional layers?

• Existing Communication middleware or extensions needed?

HyPar-Flow: Hybrid and Model Parallelism for CPUs

A. A. Awan, A. Jain, Q. Anthony, H. Subramoni, and DK Panda, “HyPar-Flow: Exploiting MPI and Keras for Hybrid Parallel Training of TensorFlow models”, ISC ‘20 (Accepted to be presented), https://arxiv.org/pdf/1911.05146.pdf

https://arxiv.org/pdf/1911.05146.pdf


• ResNet-1001 with variable batch size

• Approach: – 48 model-partitions for 56 cores

– 512 model-replicas for 512 nodes

– Total cores: 48 x 512 = 24,576

• Speedup– 253X on 256 nodes

– 481X on 512 nodes

• Scaling Efficiency– 98% up to 256 nodes

– 93.9% for 512 nodes

Out-of-core Training with HyPar-Flow (512 nodes on TACC Frontera)

481x speedup on 512 Intel Xeon Skylake nodes (TACC Frontera)

A. A. Awan, A. Jain, Q. Anthony, H. Subramoni, and DK Panda, “HyPar-Flow: Exploiting MPI and Keras for Hybrid Parallel Training of TensorFlow models”, ISC ‘20 (Accepted to be presented), https://arxiv.org/pdf/1911.05146.pdf

https://arxiv.org/pdf/1911.05146.pdf


• The field of Pathology (like many other medical disciplines) is moving Digital

• Traditionally, a pathologist reviews a slide and carries out diagnosis based on prior knowledge and training

• Experience matters

• Can Deep Learning to be used to train thousands of slides and – Let the computer carry out the diagnosis

– Narrow down the diagnosis and help a pathologist to make a final decision

• Significant benefits in– Reducing diagnosis time

– Making pathologists productive

– Reducing health care cost

AI-Driven Digital Pathology


• Pathology whole slide image (WSI) – Each WSI = 100,000 x 100,000 pixels

– Can not fit in a single GPU memory

– Tiles are extracted to make training possible

• Two main problems with tiles– Restricted tile size because of GPU memory limitation

– Smaller tiles loose structural information

• Can we use Model Parallelism to train on larger tiles to get better accuracy and diagnosis?

• Reduced training time significantly– 7.25 hours (1 node, 4 GPUs) ->

27 mins (32 nodes, 128 GPUs)

Exploiting Model Parallelism in AI-Driven Digital Pathology

Courtesy: https://blog.kitware.com/digital-slide-archive-large-image-and-histomicstk-open-source-informatics-tools-for-management-visualization-and-analysis-of-digital-histopathology-data/

https://blog.kitware.com/digital-slide-archive-large-image-and-histomicstk-open-source-informatics-tools-for-management-visualization-and-analysis-of-digital-histopathology-data/


• Scalable distributed training is getting important

• Requires high-performance middleware designs while exploiting modern interconnects

• Provided a set of solutions to achieve scalable distributed training– MPI (MVAPICH2)-driven solution with Horovod for TensorFlow, PyTorch and

MXNet– Optimized collectives for CPU-based training– CUDA-aware MPI with optimized collectives for GPU-based training– Out-of-core training and Hybrid Parallelism

• Will continue to enable the DL community to achieve scalability and high-performance for their distributed training

Conclusions


• 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 being provided to Lawrence Livermore National Laboratory (LLNL) and KISTI, Korea

Commercial Support for MVAPICH2, HiBD, and HiDL Libraries

http://x-scalesolutions.com/


• High-performance solution for distributed training for your complex AI problems

• Features:– Integrated package with TensorFlow, PyTorch, MXNet, Horovod, and

MVAPICH2 MPI libraries– Targeted for both CPU-based and GPU-based Deep Learning Training– Integrated profiling support across the stacks– Support for x86 and OpenPOWER platforms– Support for InfiniBand, RoCE and NVLink Interconnects– Out-of-the-box optimal performance– One-click deployment and execution

• Send an e-mail to [email protected] for free trial!!

X-ScaleAI Product



Funding AcknowledgmentsFunding Support by

Equipment Support by


Personnel AcknowledgmentsCurrent Students (Graduate)

– A. Awan (Ph.D.)

– M. Bayatpour (Ph.D.)

– C.-H. Chu (Ph.D.)

– J. Hashmi (Ph.D.)

– A. Jain (Ph.D.)

– K. S. Kandadi (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.)

– S. Chakraborthy (Ph.D.)

– N. Dandapanthula (M.S.)

– V. Dhanraj (M.S.)

– R. Rajachandrasekar (Ph.D.)

– D. Shankar (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

– X. Lu

Past Post-Docs– D. Banerjee

– X. Besseron

– H.-W. Jin

– T. Gangadharappa (M.S.)

– K. Gopalakrishnan (M.S.)

– 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.)

– Kamal Raj (M.S.)

– K. S. Khorassani (Ph.D.)

– P. Kousha (Ph.D.)

– A. Quentin (Ph.D.)

– B. Ramesh (Ph.D.)

– S. Xu (Ph.D.)

– J. Lin

– M. Luo

– E. Mancini

Past Programmers– D. Bureddy

– J. Perkins

Current Research Specialist– J. Smith

– S. Marcarelli

– A. Ruhela– J. Vienne

Current Post-doc– M. S. Ghazimeersaeed

– K. Manian

Current Students (Undergraduate)– V. Gangal (B.S.)

– N. Sarkauskas (B.S.)

Past Research Specialist– M. Arnold

Current Research Scientist– A. Shafi

– H. Subramoni

– Q. Zhou (Ph.D.)

– H. Wang


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/


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