BDVA & ETP4HPC Workshop

Transcription

1 BDVA & ETP4HPC Workshop July 4 th, Bologna Organised by EXDCI 1

2 Agenda item Actions Time Welcome & Introductions 08:00-08:15 Agree timings 1. Common HPC and BD Glossary: determine a common reference to understand possible relationships between a typical HPC stack and a BD Analytics stack 2. Cross-Pollination of HPC and BD Technologies: What respective technologies/approaches from a HPC stack or a BD Analytics stack can benefit the other s needs e.g. respective hybrid systems that incorporate select elements from either HPC or BD technologies/approaches 3. Extreme BD Workloads: Understand bottlenecks through better appreciation of centralised and decentralised processing of extreme big data workloads 4. Collaboration between HPC CoEs and BD CoEs: Facilitate better communication between HPC s CoE ( and BD COEs ( BDVA Lead: Jim Kenneally (Intel Corp.) HPC lead: Mark Asch(Univ. de Picardie), Hans-Christian Hoppe (Intel) BDVA lead Nenad Stojanovic (Nissatech), supported by Gabriel Antoniu (Inria) & Alexandru Costan (Irisa) HPC lead - Costas Bekas, IBM Research Mark Asch (Univ. de Picardie), BDVA Lead: Maria Perez (UPM) HPC lead: Mark Asch, Stephane Requena (GENCI) BDVA lead: Paul Czech (know-center) - HPC Lead: Erwin Laure (PDC-KTH) Available Extreme Scale Demonstrators (ESDs) and Exascale co-design: Information update only 6. User engagement, that include understanding user base (UX Analysis), Skills development, Business Models 7. Explore options for possible collaborations in view of forthcoming WP18-20 HPC lead: Michael Malms (ETP4HPC) BDVA lead: Andrea Manieri (ENG) HPC Lead:Francois Bodin (IRISA), Catherine Inglis (epcc) All Close 12:00 2

3 #1 COMMON HPC AND BD GLOSSARY 3

4 AI/ML/DL??Spark??Flink? 4

5 HPC, Big Data &Deep Learning Stacks Can be part of HPC Big Data Deep Learning Applications Compiled in-house, commercial & OSS applications Workflows combining many application elements Defined and instantiated/trained neural networks IDEs & Frameworks (PETSc, ) Compiled languages (C++) Scripting & WF languages (R, Python, Java, ) Scripting languages (Python, ) Load distribution layer Middleware & Mgmt. Compiled languages (C, C++, FORTRAN) Domain-specific libraries Scripting lang. (Python) I/O libraries (HDF5, ) Distributed coordination (Zookeeper, ) Map-Reduce Processing (Hadoop, Spark) Traditional ML (Mahout) Data stream processing (Storm, ) Neural network frameworks (Caffe, Torch, Theano, ) Numerical libraries (dense LA) Inference engines (low precision) Accelerator APIs PFS (Lustre etc.) Numerical libraries Performance & debugging Cloud service I/F Storage systems (DFS, Key/value, ) Cloud service I/F Storage systems (DFS, Key/value, ) MPI OpenMP, threading Accelerator APIs Orchestration and RMS Orchestration and RMS System SW Cluster management (OpenHPC) Batch scheduling (SLURM ) Containers VMM and container management Virtualization: hypervisor or containers (Dockers, Kubernetes, ) VMM and container management Virtualization: hypervisor or containers (Dockers, Kubernetes, ) Linux OS Variant Linux OS Variant (some Windows) Linux OS Variant (Windows?) Hardware Infiniband & OPA fabrics Storage & I/O nodes x86 nodes, GPUs, FPGAs Ethernet fabrics Local storage x86 hyperconvergent nodes Ethernet (traditional) Infiniband + OPA (scale-out) x86 + GPU/FPGA, TPU 5 EXDCI WP2 Source: Hans-Christian Hoppe, Intel Corp July 4th, 2017

6 #2 CROSS-POLLINATION OF HPC AND BD TECHNOLOGIES 6

7 Historical Differences between Big Data and HPC Workload type Big Data Typical workload focus is Data-intensive: devote most of their processing time to I/O and manipulation of data Design principles for infrastructure and software are optimised for cost (IOPS) first, rather than maximum performance HPC Compute-intensive: devote most of their execution time to computation optimised for performance (FLOPS) first, rather than for minimal cost. 7

8 Traditional Big Data Data-intensive workloads [Example] Inferring new insights from big data-sets e.g. pattern recognition across suppliers, consumers, etc for data-driven insights and innovation Enterprise IT Regular workloads [Example] Running the enterprise HR, Legal, Payroll, finance, etc. Extreme Data Analytics Compute- and Data intensive workloads: [Example] Reshaping healthcare through advanced analytics and artificial intelligence leading to predictive and personalized medicine HPC Compute-intensive workloads: [Example] Modelling and simulating focusing on interaction amongst parts of a system and the system as a whole e.g. product design Real-World Use Cases Fraud/error anomaly detection e.g. FSI Intelligence community e.g. anti-terrorism, anti-crime Cyber security Data-driven science/ engineering (e.g., biology) Knowledge discovery e.g. ML/DL, cognitive, AI The hyper-growth area of Machine and Deep Learning and AI sit at the intersection of HPDA and HPC Key workloads include video analysis, image speech & text analytics, medicine, IoT, ADAS, Security 8

9 Cross-Pollination of HPC and BD Technologies Cross-Pollination of respective BD and HPC platforms to build respectively for compute-intensive analytics (BD) data-driven simulations (HPC) Complex scenarios of this type of computation are emerging The entire engineering domain based on digital twins is full of scenarios requiring a hybird system Digital twins use data from sensors installed on physical objects to represent their near real-time status, working condition or position. Increasingly used for improving the real-time operation of complex products/systems 9

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11 HPC and BD (separate) parameters COMPUTE INTENSIVE DIGITAL TWIN = digital model exists DATA LESS-INTENSIVE 10+ parameters DATA INTENSIVE HPC COMPUTE LESS INTENSIVE BD 11

12 HPC and BD INTEGRATED COMPUTE INTENSIVE TBs/hour parameters DATA LESS-INTENSIVE 10+ parameters DIGITAL TWIN = digital model exists BEHAVIOUR PREDICTIONS DATA TWIN DATA INTENSIVE Model of normal behaviour (predictive) PBs COMPUTE INTENSIVE EXTREME DATA ANALYTICS BD BEHAVIOUR SIMULATIONS HPC

13 Connected car example 380 million connected cars will be on the road by 2021 Ford: Predicting data storage requirements of 200PB by 2021 growing from today s DATA TWIN 13PB EXTREME DATA ANALYTICS BD HPC PB data BD simulations HPC DIGITAL TWIN BD streaming analytics BD 1TB data/hour edge analytics

14 What are BD advantages (connected car example) Stream processing Efficient complex pipelines for real-time processing (e.g. Storm) Real-time stream analytics (on-the-fly, no storage) Edge analytics (real-time, on-the-fly, no storage) Methods for real-time stream analytics can be downsized to work efficiently on the edge Service logistics Analytics on different levels Combining real-time and batch processing (lambda architecture) 14

15 What are challenges for BD Streaming analytics The streams and context can be dynamic, implying the need for dynamically changing processing infrastructure (e.g. Storm has a static topology) Self-adaptivity is the goal Intelligent service placement Edge off-loading Efficiency in processing extremly huge datasets 15

16 What are opportunities for BD HPC hybrid Data twin? 16