0 © 2017 FUJITSUISC2017 – Exhibitor Forum
Connect. Challenge. Inspire.
HPC UniverseExpansion
ISC2017 – Exhibitor Forum
1 © 2017 FUJITSUISC2017 – Exhibitor Forum
1975…
SOLAR 16-65:• 750ns for 16bits ADD instruction• 64KBytes main memory• 2.5MBytes disk storage
2 © 2017 FUJITSUISC2017 – Exhibitor Forum
1979 !
ICL PERQ
« smalltalk » environment
Multi-windows manager
Mouse
Text editor
shell
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2017 ?
SAMSUNG Galaxy S6
0.788 Gflops (Linpack) multi-threaded
4 © 2017 FUJITSUISC2017 – Exhibitor Forum
The scope of HPC is much larger than most think
Weather, Climate
Government Labs
Product Design & Behaviour
National Security
Academic Research
Oil/Gas Exploration and Extraction
New Hardware+Software for Deep Learning• HPC for Training: Exaflops per session, Computationally intensive,
GPUs/Phis/FPGAs
• Tactical HPC for Inference: data centers, self driving cars, smart phones, IoT, robots etc.
HPC in the Cloud• Virtualized and/or bare metal, Public/private/hybrid, On-prem/cloud
ecosystems, Making HPC more like clouds
New big data applications running in non-traditional HPC environments
• Finance or cyber security sectors: supercomputing-based threat analytics service on subscription basis
• Business analytics growing (forced) into HPC
Compute/Data-intensity creates challenges solved only by HPC architecture and software
Visible HPC Hidden HPC
5 © 2017 FUJITSUISC2017 – Exhibitor Forum
How does HPC work? … Parallelism
Early supercomputers first exploited vectorisation to multiply performance
Including Fujitsu’s own VPP systems
Followed by further parallelism from multiple CPUs
Increasingly, greater parallelism is available within a range of processors and systems, accelerating a broader of applications beyond the primarily scientific
Black-Scholes, Monte Carlo in Financial Services
Convolutional Neural Networks in Deep Learning
Parallel file systems for Data Analytics
And HPC scales to solve the largest problems
Efficiently handle the throughput to match data volume
Maximise performance levels even as model/analytical complexity increases
6 © 2017 FUJITSUISC2017 – Exhibitor Forum
Parallelism gains in Intel® Xeon Phi™ Processors
Latest Intel many-core processor available in PRIMERGY CX600
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2003
MapReduce:
Simplified Data Processing on Large Clusters
Jeff Dean, Sanjay Ghemawat
Google, Inc.
8 © 2017 FUJITSUISC2017 – Exhibitor Forum
High Performance Data Analysis (HPDA)
Big Data needs HPC capabilitiesPlatform to process Big Data with HPC technologies
HPDA solution Customer benefits
1
2
3
Fastest processing/transformationof large volume data
Real-time analysisto extract invisible insight from the data
Accelerated deep-learning technologyby GPU computation
Implementation & data management service
Analysis support service in collaboration with
data scientists
Data gathering
Data storeModeling/Analysis
9 © 2017 FUJITSUISC2017 – Exhibitor Forum
MODELING & SIMULATION
Existing HPC users Larger problem sizes Higher resolution Iterative methods EP jobs to the cloud
New commercial users SMEs Digitalisation across
organisations
ADVANCED ANALYTICS
Existing HPC users Intelligence community, FSI Data-driven science/
engineering (e.g., biology) Knowledge discovery ML/DL, cognitive, AI
New commercial users Fraud/anomaly detection Business intelligence Affinity marketing Personalized medicine
Convergence of Analytics and HPC
Drivers: Competition, Complexity, Time Fraud and anomaly detectionIdentifying harmful or potentially harmful patterns and causes using graph analysis, semantic analysis, or other high performance analytics techniques.
MarketingPromote products or services using complex algorithms to discern potential customers' demographics, buying preferences and habits.
Business intelligenceUses HPDA to identify opportunities to advance the market position and competitiveness of businesses, by better understanding themselves, their competitors, and the evolving dynamics of the markets they participate in.
Other Commercial HPDAAn example of such a high-potential workload is the use of HPDA to manage large IT infrastructures, ranging from on premise data centers to public clouds and Internet-of-Things (IoT) infrastructures.
Source: IDC,2016
10 © 2017 FUJITSUISC2017 – Exhibitor Forum
2014, the « caffe » break
@article{jia2014caffe, Author = {Jia, Yangqing and
Shelhamer, Evan and Donahue, Jeff and Karayev,
Sergey and Long, Jonathan and Girshick, Ross and
Guadarrama, Sergio and Darrell, Trevor}, Journal =
{arXiv preprint arXiv:1408.5093}, Title = {Caffe:
Convolutional Architecture for Fast Feature
Embedding}, Year = {2014} }
Deep Learning “democratization”• Caffe supports many different types of deep learning
architectures geared towards image classification and image segmentation.
• It supports CNN, RCNN, LSTM and fully connected neural network designs.
• Caffe supports GPU based accleration using CuDNN of Nvidia.
11 © 2017 FUJITSUISC2017 – Exhibitor Forum
Deep Learning is now reaching viable accuracy
Courtesy of Nervana
Continuing Challenges
ARTIFICIAL INTELLIGENCEA program that can sense, reasons, act and adapt
MACHINE LEARNINGAlgorithms whose performance improve when exposed to more data over time
DEEP LEARNINGMulti-layered neural networks
learn from vast amounts of data
Large compute requirements for training
Performance that scales with data
Calculation of increasingly complex models
12 © 2017 FUJITSUISC2017 – Exhibitor Forum
And HPC is fundamental to Deep Learning
Convolutional Neural Networks (CNNs) represent a significant class of Deep Learning (DL) algorithms today, and the de facto tool for visual understanding
Majority of the computations in CNNs can be formulated as Matrix-Matrix multiplications**
Optimisation approach is identical to simulation/iterative solvers in conventional HPC applications
Convolution and AllReduce are other main algorithmic kernels –also accelerated using HPC principles
All achieve high performance on many-core accelerators (GPU, Phi), with gains in specific kernels offered by dedicated units
Intel® Xeon Phi™ Processor
Knights Mill
Intel® Xeon Processor
Skylake
Lake Crest
Intel® Xeon® Processor + FPGA
Image Identity
** For more: https://svail.github.io/DeepBench/
Intel® Lake Crest Deep neural network processor
Accelerating Deep Learning Applications Convolutional Neural Network
13 © 2017 FUJITSUISC2017 – Exhibitor Forum
Fujitsu High Performance AI Ecosystem
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Invest more than 50 Millions on Digital Transformation in France
A joint research on Deep-learning technology with Inria
Center of Excellence focus on AI located in Polytechnique
CoE R&D
Collaboration with France’s leading technology companies
Ecosystem
15 © 2017 FUJITSUISC2017 – Exhibitor Forum
AI
Cloud
Parallel Processing
GPU
HPC
Deep Learning
Machine Learning
Image Analytics / Search
Encoding / Decoding
Imagingtech.
Video Surveillance
View of HPC in 2017 and Beyond
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